Traditional approaches to cybersecurity have always been to defend the digital perimeter surrounding internal networks. However, with the popularity of remote work and cloud computing technologies, conventional security strategies are no longer as effective at protecting organizations.
Zero trust has now become the go-to security approach. Its guiding concepts are built around the mindset of “never trust, always verify.” Each user, access device, and network connection is strictly evaluated and monitored regardless of where they originate from.
Artificial intelligence (AI) has become an addition to zero trust security architecture. With the ability to analyze large volumes of information and apply complex processes to automate security functions, AI has helped how modern businesses approach their security planning.
Understanding zero trust in modern organizations
Digital environments have changed the cybersecurity paradigm in many different ways, as businesses have moved toward highly connected infrastructures.. Zero trust security models assume every network connection within the organization is a potential threat and requires various strategies to address them effectively.
Zero trust models work on several core principles that include:
Providing minimum access privileges: Employees should only be given access to information and systems that are absolutely essential for the job function they perform. This limits unauthorized access at all times, and in the event a security breach does occur, the damage is contained to a minimum.
Creation of isolated network areas: Rather than having a single company network, organizations should segment their systems and databases into smaller, isolated networks. This limits an attacker’s access to only a part of the system in the event of a successful perimeter breach.
Constant verification: All users and devices are checked and rechecked frequentlyTrust is never assumed, and all activity is closely monitored regardless of who is gaining access or what they’re doing.
Assumed breaches: With zero trust, potential breaches are always viewed as a possibility. Because of this, security strategies don’t just focus on prevention, but also limiting the possible damage from a successful attack.
Identity-centric security has now become an essential element for building a strong cybersecurity posture and improved operational resilience. A big part of this process is safeguarding sensitive information and making sure that even if breaches do occur, it’s less likely that it becomes compromised.
The role of AI in strengthening zero trust models
Bringing AI and zero trust together represents a major step forward for cybersecurity. AI’s power to analyze large datasets, spot unusual network activity, and automate security responses makes the core principles of zero trust even stronger, allowing for a more flexible and resilient defense.
Improving identity and access management
With leveraging AI, managing various identities and provisioning system access within a zero trust environment can be improved. Machine learning models can scan user behaviors looking for anomalies indicative of compromised accounts or potentially dangerous network activity. Adaptive authentication protocols can then use these risk-based assessments to change various security validation parameters dynamically.
AI technology also helps automate authentication processes when validating user identities. They can help facilitate new user setups, streamlining IT processes while at the same time minimizing human error. This added efficiency reduces the strain and resource requirements of IT support teams and significantly reduces the possibility of accidentally giving out wrong access permissions.
Intelligent threat detection and response
Traditional security measures can overlook subtle, yet important indicators of malicious network activity. However, machine learning algorithms can aid in detecting these threats ahead of time, resulting in a far more proactive approach to threat response.
Autonomous threat hunting and incident resolution can reduce the time necessary to identify and contain breaches while mitigating any associated damage. With AI, network monitoring processes can be done automatically, allowing security personnel to act faster if and when the time comes.
AI can also provide organizations with predictive analytics that help to guard against possible attacks by anticipating them before they occur. By using threat intelligence gathered from external vendors, and at the same time, checking for system vulnerabilities, essential steps can be taken to tighten security defenses to avoid any weaknesses from being exploited.
Automating data security and governance processes
AI systems can help sensitive business information be protected in real time. As data is collected, it can be automatically classified into various categories. This dynamic classification allows AI systems to apply relevant security controls to certain datasets, helping to align with various compliance requirements while adhering to any of the organization’s specific data management policies.
Another important security element for modern organizations is data loss prevention (DLP). AI-driven DLP solutions can be configured to automatically supervise the way users access and relocate information within a system. This helps to identify potential data manipulation and greatly minimizes the danger of unauthorized system access and data leakage.
Though AI drastically improves the capabilities of traditional zero-trust models, it also can present additional security considerations that require organizations’ attention. Some of these include:
Bias in AI systems should be dealt with as well. Machine learning algorithms trained on outdated data are capable of producing inaccurate results that could lead to more passive security measures being put in place. Organizations need to ensure that any of their AI-driven systems have supporting policies in place to prevent these biased analyses from taking place.
Integration and implementation challenges
Integrating AI into a zero trust framework isn’t always straightforward. Complications can surface – especially when it comes to system and network compatibility. Organizations need to ensure that their AI solutions can be seamlessly integrated into the existing tech stack and that there aren’t any potential barriers that will impede data flow to and from critical systems.
Another operational challenge with AI-driven security systems is finding qualified talent to operate them. Companies will likely need to allocate dedicated resources for training and staff development to keep systems functioning effectively.
The importance of regular AI model training
AI solutions, especially those that use complex learning algorithms, aren’t a “set-it-and-forget-it” implementation. With cyber threats constantly evolving, maintaining the effectiveness of AI-driven systems requires regular model training.
Without regular intervals of AI model retraining, these systems won’t function accurately and efficiently over time. An AI model must be regularly reviewed and modified to avoid false positive alerts, broken automation, or inadequate threat mitigation protocols.
The future of cybersecurity
Integrating AI with zero trust architecture has changed how businesses can approach their cybersecurity initiatives. As cyberthreats become increasingly more sophisticated, then the need for increased automation and identity-centric security planning will only continue to grow.
With the proper implementation strategies in place, organizations can benefit from enhanced threat management, streamlined access management, and a more proactive approach to data protection.
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Artificial intelligence assistants are quickly becoming vital tools in modern workplaces, transforming how businesses operate by making everyday tasks simpler and faster.
But despite their widespread adoption and advanced capabilities, even the best AI assistants today face a significant limitation: they often lack access to a company’s internal knowledge.
AI assistants need real-time, seamless connections to your company’s databases, documents, and internal communication tools to realize their full potential. This integration ensures they’re brilliant and contextually aware, making them genuinely valuable workplace assets.
The rise of AI assistants
AI assistants are smart applications that understand commands and use a conversational AI interface to conduct tasks. They’re often embedded into dedicated hardware and even incorporated with several systems.
Unlike chatbots, AI assistants are less limited in both intelligence and functionality. They have more agency and advanced abilities, like contextual understanding and personalization. From drafting emails to summarizing reports, these assistants are everywhere.
In business, these large language models (LLMs) can also help you with data analysis, task automation, workflow streamlining, and more. They can be mostly free if you don’t need to scale up, although some users might struggle with the free versions when it comes to tasks that involve uploading or downloading data.
However, even the more advanced AI assistants are missing something that makes them truly useful in your workplace: they don’t have access to your company’s knowledge and information. Without that, these assistants are simply guessing.
Picture this: you ask your AI assistant about a specific company policy you need to quote, a conversation that’s buried in Slack, or a past project you need vital information from. You’re likely to get a vague and generic answer or, even worse, something that’s completely irrelevant or downright wrong.
That’s because these AI assistants don’t have access to the right data – your data – and rely on public information instead. As they aren’t drawing from internal knowledge that sits behind your business, you’ll often find issues with their responses.
Wasted time searching for answers the AI should be able to provide.
Frustration when employees get irrelevant or outdated responses.
AI that feels like more of a novelty than a real workplace tool.
If an AI assistant is to work in a business environment, it needs more than intelligence. It needs context, otherwise it won’t be helpful for your employees.
The fix: Connecting AI assistants to your knowledge base
How do you tackle the information problem?
The answer is simple: the AI assistants have to be plugged into your company’s internal database. When they have real-time access to company documents, emails, Slack threads, and more, they can help you the way your business needs.
But how can AI assistants help your business by being connected to your company data?
When you connect an AI assistant to your institutional knowledge base with policies, documentation, manuals, and more, they’ll be able to provide you with accurate and contextual answers on a wider variety of topics.
This could change how employees share knowledge in the workplace, moving from a tedious process of manual document searching to a more conversational, self-service experience. Employees’ wait times and support costs will be reduced by simply asking assistants and getting instant replies.
A custom AI assistant lets you quality customers and offers personalized solutions by taking care of repetitive and time-consuming tasks. Your employees can then focus on improving products and strategic work.
This streamlined strategy leads to increased efficiency and productivity, which greatly reduces bottlenecks and improves output. And as AI assistants can also handle companies’ growing needs, they’ll adapt to increased workloads and offer long-term ROI and usability.
How Glean makes AI smarter
That’s where Glean comes in. Glean connects AI assistants directly to your company’s knowledge, turning them into real, reliable workplace tools. It’s designed to integrate AI capabilities into your company’s data for up-to-date and context-aware answers.
Here’s what that means in practice:
Real-time data synchronization
Glean’s connectors support real-time synchronization, making sure that any updates in the source applications are immediately reflected. This means that your assistant will always work with the most current information, enhancing its responses’ accuracy and timeliness.
An extensive data integration makes sure that your AI assistant can access a wide range of company data, which allows it to offer relevant and informed responses. Glean connects with over 100 enterprise applications like Box, Confluence, Dropbox, GitHub, Gmail, Google Drive, Jira, Microsoft Teams, OneDrive, Outlook, Salesforce, ServiceNow, SharePoint, Slack, and Zendesk.
Permissions-aware responses
Strictly enforcing the same permissions in your company’s data sources, Glean ensures that users only have access to the information they have permission to see. This keeps your data secure and in compliance with regulations while still delivering the relevant answers.
Personalized results and semantic understanding
Glean Assistant uses deep learning-based language models, meaning it understands natural language queries and can deliver intuitive interactions. Every personalized result takes into consideration ongoing projects, the user’s role, and collaborations for tailored information.
Universal knowledge access
As it combines external web information with your internal company data, Glean Assistant is ideal for researching internal projects and accessing publicly available insights in just one platform. The integration makes it much easier for a comprehensive understanding and informed decision-making.
AI-driven content generation and analysis
Glean Assistant can analyze structured and unstructured data simultaneously across your company’s applications, documents, and even the web. It offers assistance in supporting a smarter decision-making process by drafting deliverables and finding relevant insights.
A seamless integration with your company’s data ecosystem and advanced AI techniques allow for Glean Assistant to enhance your productivity.
The smarter way forward
AI assistants have the potential to transform the workplace significantly, but only if they have access to accurate and relevant internal information. Connecting them directly to internal knowledge allows companies to move from nice-to-have AI to must-have AI.
Glean makes that shift seamless, turning AI from a frustrating gimmick into a powerful, reliable assistant. This enhances productivity and empowers employees to achieve more meaningful outcomes.
This article comes from Nick Nolan’s talk at our Washington DC 2025 Generative AI Summit. Check out his full presentation and the wealth of OnDemand resources waiting for you.
What happens when a powerful AI model goes rogue? For organizations embracing AI, especially large language models (LLMs), this is a very real concern. As these technologies continue to grow and become central to business operations, the stakes are higher than ever – especially when it comes to securing and optimizing them.
I’m Nick Nolan, and as the Solutions Engineering Manager at Fiddler, I’ve had countless conversations with companies about the growing pains of adopting AI. While AI’s potential is undeniable – transforming industries and adding billions to the economy – it also introduces a new set of challenges, particularly around security, performance, and control.
So in this article, I’ll walk you through some of the most pressing concerns organizations face when implementing AI and how securing LLMs with the right guardrails can make all the difference in ensuring they deliver value without compromising safety or quality.
Let’s dive in.
The growing role of AI and LLMs
We’re at an exciting moment in AI. Right now, research shows around 72% of large enterprises are using AI in some way, and it’s clear that generative AI is definitely on the rise – about 65% of companies are either using it or planning to.
On top of this, AI is also expected to add an enormous amount to the global economy – around $15.7 trillion by 2030, but let’s keep in mind that these numbers are just projections. We can only guess where this journey will take us, but there’s no denying that AI is changing the game.
But here’s the thing: while the excitement is real, so are the risks. The use of AI, particularly generative AI, comes with a unique set of challenges – especially when it comes to ensuring its security and performance. This is where guardrails come into play.
If organizations do AI wrong, the cost of failure can be astronomical – not just financially, but also in terms of reputational damage and compliance issues.
The current end-to-end trade lifecycle is highly dependent on having accurate data at each stage. The goal of the Investment iook of records (IBOR) system is to ensure the trade, position, and cash data match the custodian and for the accounting book of records (ABOR) system for this same data set to match the fund accountant.
There are other stakeholders in the process, including broker systems, transfer agents, central clearing parties, etc, depending on the type and location of execution. A position that reflects identically across all systems is known as having been “straight-through processed”; in other words, systems have recognized the trade, and datasets are in line, or at least, within tolerance.
While efficient, the addressal and eventual resolution of non-STP executions remains highly manual. Stakeholders typically compare data points across multiple systems, beginning as upstream as possible, and gradually move down the lifecycle to the root cause of the break. This investigation takes time, creates noise across the value chain, and most importantly, creates uncertainty for the front office to take new decisions.
The proposal is to leverage AI to continually create and refine gold-copy data at each stage of the life cycle through comparison with sources and link downstream processes to automatically update in real-time with the accurate datasets. Guardrails should also be implemented in case of material differences.
Let’s analyze the current process with an example – a vanilla bond is about to undergo a payment-in-kind (PIK) corporate action (PIKs occur when an issuer decides to capitalize interest it would have paid in cash as additional security). Assume that the vendor an IBOR system is using utilizes an ACT/360 day-count (to calculate accrual) than the custodian (who uses ACT/365):
On ex-date, the PIK will process with a higher capitalization than the custodian and a mismatch will form between IBOR and Bank.
This mismatch will first be uncovered on ex-date, assuming the bank sends MT567 (corp. action status) and flags the positional difference between the two systems.
Next, on SD+1, this will again be flagged when the bank sends MT535 (position statement), showing the mismatch during position reconciliation.
Finally, if investment accounting is run on ex-date or on SD+1, there’ll be a mismatch between IBOR and the fund accountant, where the balance sheet and statement of change in net asset reports will again show an exception for the security.
This simple example illustrates how one mismatch well upstream in the lifecycle causes three separate breaks in the downstream chain; in other words, three different segments of users (corp. action user, reconciliation user, and an accounting user are all investigating the same root cause).
Once the IBOR system’s data is resolved, each of these user segments need to coordinate the waterfall logic to have each of the downstream system/process updated.
The problem
Unfortunately, such occurrences are common. As front-to-middle-to-back investment systems become more integrated, inaccurate data at any point in the process chain creates inefficiencies across a number of user segments and forces multiple users to analyze the same exception (or the effect of that exception) on their respective tools.
Downstream users that are reconciling to the bank or the fund accountant will notice the security mismatch but would not immediately recognize the root cause of day count difference. These users would typically undertake the below tasks to investigate:
Raise an inquiry with the bank’s MT535 statement to explain the position difference
Raise an inquiry with the fund accountant’s statement to explain the position difference
Raise inquiry with the internal data team to specify IBOR’s position calculations
Once aware of a recent corp. action, raise inquiry with the internal COAC team to investigate the processing of the PIK
As seen, multiple teams’ energy and capacity are being expended to investigate the root cause and all being undertaken manually.
On the other hand, an AI process that could continually query multi-source datasets should have been proactively able to flag the day count discrepancy prior to the corp. action processing, as well as automatically inform downstream teams of potential inaccuracy in the specific position of the PIK security.
While any changes to user data from AI should still undergo a reviewer check, such proactive detection and communication drastically increases resolution times and should reduce user frustration.
Let’s look at the corporate action workflow in detail. Users typically create a “gold-copy” event once they’ve “scrubbed” data from multiple sources and created an accurate, up-to-date copy of the event that will occur. This is ideal in many ways: scrubbing multiple sources ensures there’s less chance of an incorrect feed from a single vendor, creating process gaps.
We need AI to undertake this process continuously. IBOR systems should, at minimum, be subscribed to two or more vendors from whom data should be retrieved. Any change to the dataset should be continually updated (either through a push or pull API mechanism). This would work as follows:
A new public security is set up in the marketplace with public identifiers including CUSIP, ISIN, SEDOL etc.
The data vendors supplying the feed to IBOR systems should feed this through automatically, once the required minimum data point details are populated.
IBOR systems, at this point, would create this security within their data systems
Any mismatches across vendors should be reviewed by a user, and appropriate values chosen (if deemed necessary)
Any updates the securities undergo from that point in the market should be automatically captured and security updated in the IBOR system
At this point, downstream applications that leverage the application should automatically flag a security market update and the impending event-driven update
This informs users that the dataset they’re seeing may be stale vs. external processes that may be receiving up-to-date data
To protect against the risk of inaccurate data from a single vendor, only a dataset that is consistent across all vendors should be automatically updated
Data updates from a single vendor only should be prompted to a user to review and approve
Once underlying securities are updated, this would be considered an ‘event’, which should drive updates to all downstream applications that rely on the security update (called event-driven updates)
Event-driven updates greatly reduce the number of manual touches downstream users need to make for inaccuracies that have been identified upstream
Once all applications are in line with the updated data sets, the security market update flag should be removed automatically.
Potential concerns
While exciting, the use of AI and event-driven updates raises a few concerns worth discussing – data capacity/storage, potential timing differences with external participants, and materiality/tolerance.
Let’s address the latter first – materiality/tolerance. Securities can undergo immaterial changes from time to time that may have little to no impact on all upstream and downstream processes in the trade lifecycle.
As a result, a set of fields and tolerances should be identified to be flagged in case of market updates (core dataset). If the updates occur on these specific fields and they’re outside of the existing tolerance, IBOR systems should consume the updates provided by vendors.
If updates occur on any other fields (or are within tolerance), the updates should be rejected. This would ensure the system leverages the efficiency of AI without the inefficiency of noise.
Secondly, there is potential for timing differences with external participants. While the IBOR system may have up-to-date data, external participants (e.g., banks or fund accounting systems) may continue to leverage stale or outdated datasets.
There should be an audit history available of the core dataset’s historical data; in other words, if the bank/fund accounting system refers to any of the audit datasets, an automatic note should be sent to the external participant informing them of stale data and to recheck against external market vendors.
Finally, there is the concern about data capacity. There’s no doubt that continual querying, validation, and updates of core datasets by multiple vendors, along with maintaining audit data, will increase data consumption and storage costs.
A number of companies are required by law to keep an audit history of at least five years, and adding the above requirement would certainly expand the capacity requirements. Making security updates to solely the core data sets and allowing tolerance should help to manage some of this required capacity.
Future
Despite these strong concerns highlighted, the use of AI is still valuable to design and implement across the trade lifecycle process and would be substantially more valuable than the costs that would likely be incurred. While much of the examples in this paper discussed public securities, the universe is substantially wider in private securities with much less high-quality data.
With the investing world transitioning to increased investments in private securities, leveraging AI will continue to pay dividends across both universes.
Large Language Models (LLMs) like GPT-4 are advanced AI systems designed to process and generate human-like text, transforming how businesses leverage AI.
GPT-4’s pricing model (32k context) charges $0.06 per 1,000 input tokens and $0.12 per 1,000 output tokens, which makes it a scalable option for businesses. However, it can become expensive very quickly when it comes to production environments.
New models cross-reference all bits of data, or tokens, that deal with other tokens in order to both quantify and understand the context behind each pair. The result? Quadratic behavior of algorithms that becomes more and more expensive as the number of tokens increases.
And scaling isn’t linear; costs increase quadratically when it comes to the length of sequences. If you need to scale up to handle text that’s 10x longer, the cost will go up 10,000 times, and so on.
This can be a significant setback for scaling projects; the hidden cost of AI impacts sustainability, resources, and requirements. This lack of insight can lead to businesses overspending or inefficiently allocating resources.
Where costs lie
Let’s look deeper into tokens, per-token pricing, and how everything works.
Tokens are the smallest unit of text processed by models – something simple like an exclamation mark can be a token. Input tokens are used whenever you enter anything into the LLM query box, and output tokens are used when the LLM answers your query.
On average, 740 words are equivalent to around 1,000 tokens.
Inference costs
Here’s an illustrative example of how costs can exponentially grow:
Input tokens: $0.50 per million tokens
Output tokens: $1.50 per million tokens
Month
Users/ Avg. prompts per user
Input/output tokens per prompt
Total input tokens
Total output tokens
Input cost
Output cost
Total monthly cost
1
1,000/20
200/300
4,000,000
6,000,000
$2
$9
$11
3
10,000/25
200/300
50,000,000
75,000,000
$25
$122.50
$137.50
6
50,000/30
200/300
300,000,000
450,000,000
$150
$675
$825
9
200,000/35
200/300
1,400,000,000
2,100,000,000
$700
$3,150
$3,850
12
1,000,000/40
200/300
8,000,000,000
12,000,000,000
$4,000
$18,000
$22,000
As LLM adoption expands, the user numbers grow exponentially and not linearly. Users engage more frequently with the LLM, and the number of prompts per user increases. The number of total tokens increases significantly as a result of increased users, prompts, and token usage, leading to costs multiplying monthly.
What does it mean for businesses?
Anticipating exponential cost growth becomes essential. For example, you’ll need to forecast token usage and implement techniques to minimize token consumption through prompt engineering. It’s also vital to keep monitoring usage trends closely in order to avoid unexpected cost spikes.
Latency versus efficiency tradeoff
Let’s look into GPT-4 vs. GPT-3.5 pricing and performance comparison.
Model
Context window (max tokens)
Input price
Output price
GPT-3.5 Turbo
4,000
$0.0015
$0.0020
GPT-3.5 Turbo
16,000
$0.0030
$0.0040
GPT-4
8,000
$0.03
$0.06
GPT-4
32,000
$0.06
$0.12
GPT-4 Turbo
128,000
$0.01
$0.03
Latency refers to how quickly models respond; a faster response leads to better user experiences, especially when it comes to real-time applications. In this case, GPT-3.5 Turbo offers lower latency because it has simpler computational requirements. GPT-4 standard models have higher latency due to processing more data and using deeper computations, which is the tradeoff for more complex and accurate responses.
Efficiency is the cost-effectiveness and accuracy of the responses you receive from the LLMs. The higher the efficiency, the more value per dollar you get. GPT-3.5 Turbo models are extremely cost-efficient, offering quick responses at low cost, which is ideal for scaling up user interactions.
GPT-4 models deliver better accuracy, reasoning, and context awareness at much higher costs, making them less efficient when it comes to price but more efficient for complexity. GPT-4 Turbo is a more balanced offering; it’s more affordable than GPT-4, but it offers better quality responses than GPT-3.5 Turbo.
To put it simply, you have to balance latency, complexity, accuracy, and cost based on your specific business needs.
High-volume and simple queries: GPT-3.5 Turbo (4K or 16K).
Perfect for chatbots, FAQ automation, and simple interactions.
Complex but high-accuracy tasks: GPT-4 (8K or 32K).
Best for sensitive tasks requiring accuracy, reasoning, or high-level understanding.
Balanced use-cases: GPT-4 Turbo (128K).
Ideal where higher quality than GPT-3.5 is needed, but budgets and response times still matter.
Experimentation and iteration
Trial-and-error prompt adjustments can take multiple iterations and experiments. Each of these iterations consumes both input and output tokens, which leads to increased costs in LLMs like GPT-4. If not monitored closely, incremental experimentation will very quickly accumulate costs.
You can fine-tune models to improve the responses; this requires extensive testing and repeated training cycles. These fine-tuning iterations require significant token usage and data processing, which increases costs and overhead.
The more powerful the model, like GPT-4 and GPT-4 Turbo, the more these hidden expenses multiply because of higher token rates.
Activity
Typical usage
GPT-3.5 Turbo cost
GPT-4 cost
Single prompt test iteration
~2,000 tokens (input/output total)
$0.0035
$0.18
500 iterations (trial/error)
~1,000,000 tokens
$1.75
$90
Fine-tuning (multiple trials)
~10M tokens
$35
$1,800
(Example assuming average prompt/response token counts.)
Strategic recommendations to ensure efficient experimentation without adding overhead or wasting resources:
Start with cheaper models (e.g., GPT-3.5 Turbo) for experimentation and baseline prompt testing.
Progressively upgrade to higher-quality models (GPT-4) once basic prompts are validated.
Optimize experiments: Establish clear metrics and avoid redundant iterations.
Vendor pricing and lock-in risks
First, let’s have a look at some of the more popular LLM providers and their pricing:
OpenAI
Model
Context length
Pricing
GPT-4
8K tokens
Input: $0.03 per 1,000 tokens
Output: $0.06 per 1,000 tokens
GPT4
32K tokens
Input: $0.06 per 1,000 tokens
Output: $0.12 per 1,000 tokens
GPT4 Turbo
128K tokens
Input: $0.01 per 1,000 tokens
Output: $0.03 per 1,000 tokens
Anthropic
Claude 3.7 Sonnet
Claude.ai plans
Input: $3 per million tokens ($0.003 per 1,000 tokens)
Output: $15 per million tokens ($0.015 per 1,000 tokens)
Free: Access to basic features
Pro plan: $20 per month (Enhanced features for individual users)
Team plan (minimum 5 users):
$30 per user per month (monthly billing) or $25 per user per month (annual billing)
Enterprise plan: Custom pricing tailored to organizational needs.
Google
Gemini Advanced
Gemini Code Assist Enterprise
Included in the Google One AI Premium plan
$19.99 per month.
Includes 2 TB of storage for Google Photos, Drive, and Gmail
$45 per user per month with a 12-month commitment
Promotional rate of $19 per user per month available until March 31, 2025
Committing to just one vendor means you have reduced negotiation leverage, which can lead to future price hikes. Limited flexibility increases costs when you switch providers, considering prompts, code, and workflow dependencies. Hidden overheads like fine-tuning experiments when migrating vendors can increase expenses even more.
When thinking strategically, businesses should keep flexibility in mind and consider a multi-vendor strategy. Make sure to keep monitoring evolving prices to avoid costly lock-ins.
How companies can save on costs
Tasks like FAQ automation, routine queries, and simple conversational interactions don’t need large-scale and expensive models. You can use cheaper and smaller models like GPT-3.5 Turbo or a fine-tuned open-source model.
LLaMA or Mistral are great fine-tuned smaller open-source model choices for document classification, service automation, or summarization. GPT-4, for example, should be saved for high accuracy and high-value tasks that’ll justify incurring higher costs.
Prompt engineering directly affects token consumption, as inefficient prompts will use more tokens and increase costs. Keep your prompts concise by removing unnecessary information; instead, structure your prompts into templates or bullet points to help models respond with clearer and shorter outputs.
You can also break up complex tasks into smaller and sequential prompts to reduce the total token usage.
Example:
Original prompt:
“Explain the importance of sustainability in manufacturing, including environmental, social, and governance factors.” (~20 tokens)
Optimized prompt:
“List ESG benefits of sustainable manufacturing.” (~8 tokens, ~60% reduction)
To further reduce costs, you can use caching and embedding-based retrieval methods (Retrieval-Augmented Generation, or RAG). Should the same prompt show up again, you can offer a cached response without needing another API call.
For new queries, you can store data embeddings in databases. You can retrieve relevant embeddings before passing only the relevant context to the LLM, which minimizes prompt length and token usage.
Lastly, you can actively monitor costs. It’s easy to inadvertently overspend when you don’t have the proper visibility into token usage and expenses. For example, you can implement dashboards to track real-time token usage by model. You can also set a spending threshold alert to avoid going over budget. Regular model efficiency and prompt evaluations can also present opportunities to downgrade models to cheaper versions.
Start small: Default to GPT-3.5 or specialized fine-tuned models.
Engineer prompts carefully, ensuring concise and clear instructions.
Adopt caching and hybrid (RAG) methods early, especially for repeated or common tasks.
Implement active monitoring from day one to proactively control spend and avoid
The smart way to manage LLM costs
After implementing strategies like smaller task-specific models, prompt engineering, active monitoring, and caching, teams often find that a systematic approach to operationalize these approaches at scale is needed.
The manual operation of model choices, prompts, real-time monitoring, and more can very easily become both complex and resource-intensive for businesses. This is where you’ll find the need for a cohesive layer to orchestrate your AI workflows.
Vellum streamlines iteration, experimentation, and deployment. As an alternative to manually optimizing each component, Vellum will help your teams choose the appropriate models, manage prompts, and fine-tune solutions in one integrated solution.
It’s a central hub that allows you to operationalize cost-saving strategies without increasing costs or complexity.
Here’s how Vellum helps:
Prompt optimization
You’ll have a structured, test-driven environment to effectively refine prompts, including a side-by-side comparison across multiple models, providers, and parameters. This helps your teams identify the best prompt configurations quickly.
Vellum significantly reduces the cost of iterative experimentation and complexity by offering built-in version control. This ensures that your prompt improvements are efficient, continuous, and impactful.
There’s no need to keep your prompts on Notion, Google Sheets, or in your codebase; have them in a single place for seamless team collaboration.
Model comparison and selection
You can compare LLM models objectively by running side-by-side systematic tests with clearly defined metrics. Model evaluation across the multiple existing providers and parameters is made simpler.
Businesses have transparent and measurable insights into performance and costs, which helps to accurately select the models with the best balance of quality and cost-effectiveness. Vellum allows you to:
Run multiple models side-by-side to clearly show the differences in quality, cost, and response speed.
Measure key metrics objectively, such as accuracy, relevance, latency, and token usage.
Quantify cost-effectiveness by identifying which models achieve similar or better outputs at lower costs.
Track experiment history, which leads to informed, data-driven decisions rather than subjective judgments.
Real-time cost tracking
Enjoy detailed and granular insights into LLM spending through tracking usage across the different models, projects, and teams. You’ll be able to precisely monitor the prompts and workflows that drive the highest token consumption and highlight inefficiencies.
This transparent visualization allows you to make smarter decisions; teams can adjust usage patterns proactively and optimize resource allocation to reduce overall AI-related expenses. You’ll have insights through intuitive dashboards and real-time analytics in one simple location.
Seamless model switching
Avoid vendor lock-in risks by choosing the most cost-effective models; Vellum gives you insights into the evolving market conditions and performance benchmarks. This flexible and interoperable platform allows you to keep evaluating and switching seamlessly between different LLM providers like Anthropic, OpenAI, and others.
Base your decision-making on real-time model accuracy, pricing data, overall value, and response latency. You won’t be tied to a single vendor’s pricing structure or performance limitations; you’ll quickly adapt to leverage the most efficient and capable models, optimizing costs as the market dynamics change.
Final thoughts: Smarter AI spending with Vellum
The exponential increase in token costs that arise with the business scaling of LLMs can often become a significant challenge. For example, while GPT-3.5 Turbo offers cost-effective solutions for simpler tasks, GPT-4’s higher accuracy and context-awareness often come at higher expenses and complexity.
Experimentation also drives up costs; repeated fine-tuning and prompt adjustments are further compounded by vendor lock-in potential. This limits competitive pricing advantages and reduces flexibility.
Vellum comprehensively addresses these challenges, offering a centralized and efficient platform that allows you to operationalize strategic cost management:
Prompt optimization. Quickly refining prompts through structured, test-driven experimentation significantly cuts token usage and costs.
Objective model comparison. Evaluate multiple models side-by-side, making informed decisions based on cost-effectiveness, performance, and accuracy.
Real-time cost visibility. Get precise insights into your spending patterns, immediately highlighting inefficiencies and enabling proactive cost control.
Dynamic vendor selection. Easily compare and switch between vendors and models, ensuring flexibility and avoiding costly lock-ins.
Scalable management. Simplify complex AI workflows with built-in collaboration tools and version control, reducing operational overhead.
With Vellum, businesses can confidently navigate the complexities of LLM spending, turning potential cost burdens into strategic advantages for more thoughtful, sustainable, and scalable AI adoption.
Think about the modern classroom. Each pupil receives a unique lesson plan courtesy of generative AI.
Every single plan is flawlessly customized and catered for – even in remote schools with unstable internet. Now consider this projection from MarketResearch: the generative AI in the EdTech sector is anticipated to increase to $5.26 billion by 2033 from $191 million in 2023, which comes with a CAGR of 40.5%.
Or take the National Education Policy Center figure: classroom districts spent $41 million on adaptive learning for personalized education in just two years.
But here’s an astounding statistic – currently, cyberattacks on educational institutions have compromised the information of more than 2.5 million users (eSchool News).
Moreover, over 1,300 schools have been victims of cyberattacks which include data breaches, ransomware, and phishing email scams since 2016 according to a report by Cybersecurity and Infrastructure Security Agency in January 2023. In Sophos’ most recent survey, 80% of schools were reported as a target for a cyber assault in 2022, which is an increase from 56% in 2021.
In fact, schools have now become the predominant targets for cybercriminals according to The74. The increase in attacks on the education sector shows that it has one of the highest rates of ransom payment, where 47% of K-12 organizations admitted they paid an average of $2.18 million in recovery attacks.
These numbers indicate there is a glaring problem: security and privacy have not been more important as EdTech continues transforming the learning experience. There is robust security software that is manageable and economical, but gives schools deep financial challenges.
Here is where Edge AI comes in: this advanced technology not only promises scalable, personalized learning experiences, but it also delivers a privacy-first approach by keeping sensitive information protected through local on-device processing rather than cloud systems. Let’s explore how EdTech and Edge AI merging can solve these nagging problems and reshape the future of education.
The innovative integration of edge AI with educational technologies
For years now, Education Technology (EdTech) has been ‘revolutionizing’ the world of learning by turning simple textbooks into complex adaptive systems that strive to meet the requirements of individual students.
This transformation is driven by adaptive learning algorithms infused with AI, which processes student data and modifies lessons in real-time. However, one flaw exists: The more traditional systems of AI tend to rely favorably on cloud processing. This form of computing has its drawbacks with regard to bandwidth, peak latency periods, real-time responsiveness lag, or even more concerning leakage of sensitive student data.
Enter Edge AI, an AI system that resides within smartphones, laptops, smart gadgets…you name it. Whereas systems dependent on the cloud would struggle with latency and privacy concerns, Edge AI can process data locally, resulting in an increased absence of risk.
The crossroads where Edge AI meets EdTech is more than just a technological improvement: It serves as a scalability and privacy solution, two crucial components needed in education today. This is how education ecosystems stand to be revamped considerably.
Technical overview: The role of edge AI in adaptive learning algorithms
What is edge AI?
In its most basic form, Edge AI is the placement of an AI “brain” on the very “edge” of a network – where data is produced.
As an example, instead of sending every byte of information to a faraway cloud server, algorithms execute on-device using available hardware such as microcontrollers or GPUs. A student’s tablet can evaluate quiz performance, adjust subsequent lessons, and provide feedback, all in real-time, without having to contact a centralized data hub.
Scalability with low latency
The benefits of edge AI are its speed and ability to scale. Adaptive learning requires real time feedback, like increasing the difficulty of math problems for a student who has already mastered the basics. In most cases, cloud-enabled systems tend to falter in this area due to latency as data is sent and received.
Edge AI, on the other hand, does not have this issue as processing is done locally, meaning feedback is instantaneous. According to a 2023 survey by ACM computing surveys, edge computing lagging by as much as 80% when compared with the cloud, makes it best suited for time-sensitive EdTech applications.
Take AI-enabled tutoring platforms for instance: they can not only analyse a learner’s mastery of algebra, but also switch to geometry mid-session without buffering. This kind of immediacy enhances engagement as learners remain submerged in the flow of the moment, not waiting or idling for the next chore.
Energy efficiency
Edge AI is not only swift, but also efficient. It reduces energy use by cutting down data transfers to the cloud. Edge-cloud systems as outlined in ScienceDirect demonstrate local processing can reduce energy usage by 30-50%. This is beneficial for the battery life of devices and the emissions from data centers. In EdTech, this translates to affordable and eco-friendly tools that do not burden school budgets.
With GDPR, FERPA, and CCPA components intensely scrutinizing student data, it has become a liability. Edge AI keeps it on-device, eliminating the need to transmit sensitive information such as a child’s reading preferences or test scores over the internet.
This, of course, dovetails with privacy regulations: Learnosity reported that GDPR fines exceeded €1 billion in 2023 alone, demonstrating the regulators’ no-tolerance policy regarding data mismanagement.
Reduction of breach opportunities
Hackers have a field day with cloud servers. Edge AI flips the script; there is no single centralized honeypot to crack. On-device processing reduces the opportunity for exposure. According to Parachute, in Q1 2024, the education sector experienced an average of 2,507 cyber attacks per week, indicating a significant rise in targeted attacks on educational institutions.
Ethical issues
There are a lot more issues than compliance when it comes to Edge AI in education technology, surveillance is creepy and data faces constructively exploitation. With capitalist motives milking every click of profit, honed by centralised AI, it’s understandable to feel like Big Brother was tracking you. Users gain back control with decentralized Edge AI. That changes everything. Now, it’s education, not espionage.
Examples of privacy-focused EdTech
The mobile app from Duolingo incorporates some local processing for various language exercises and minimizes reliance on the cloud. On the other hand, some startups like Century Tech use Edge AI to tailor the learning experience while also branding themselves as compliant with GDPR, earning accolades from privacy-sensitive parents.
Case study: ASU’s secure federated learning platform
Together with ATTO Research, Arizona State University is building an edge device secure federated learning platform with a focus on privacy (ASU AI Edge Project).
Under the guidance of Assistant Professor Hokeun Kim, the project develops middleware for edge developers – facilitating collaborative learning without sharing raw data amongst devices. “Historically, edge devices were fairly secure,” says Kim, a faculty member in the School of Computing and Augmented Intelligence, part of the Fulton Schools.
“The devices were performing basic functions and transmitting information to data centers where most of the real work was being done. These centers are managed by experts who provide multiple layers of data protection.”
Use case scenarios range from medical education to smart campus initiatives, improving scalability and privacy. The outcomes are yet to be achieved, but the emphasis on secure, on-device AI is a primary concern for EdTech, especially in remote learning situations.
Limitations and bias: A multi-faceted spectrum
There are some flaws with Edge AI. Devices such as inexpensive tablets have hardware limits, which pose a bottleneck for complicated models; imagine the neural networks needing more power than a microcontroller can provide. As the 2025 Edge AI survey on arXiv mentions, developers have to optimize algorithms, pruning and quantizing to mechanical limits.
Bias is problematic regardless of the form of AI being used: If there’s a skew in data sets that are used for training, all outcomes will be biased. This can be a cause for exacerbating the education gap.
There’s a need for transparency: algorithms need to be made available for examination, something EdTech companies are obligated to provide. While it improves privacy, Edge AI increases the demand for strong security on the device. Take over the tablet, and you have control.
The future is based on teamwork. AI Giants like Google and NVIDIA can partner with EdTech players such as Pearson or Coursera to develop open-source Edge AI frameworks. These toolkits would allow smaller companies to develop privacy-first, scalable solutions without reinventing the wheel. There is already a glimpse of this in TensorFlow Lite’s focus on the edge; Imagine it’s curriculum specific.
Lowering the barriers
Cooperative effort lowers expenses and technical sophistication. Custom-tailored AI systems are financially unfeasible for rural school districts or lean startup EdTech companies; open frameworks level the playing field. This allows innovation as per Forbes’ reporting on technology inclusivity.
Futuristic-proofing education
AI tech companies are yet to focus on securable scalable tools for EdTech – for example, plug-and-play adaptive learning systems that automatically comply with GDPR and FERPA. Suggestion? Annual AI-EdTech joint conference or interdisciplinary laboratories that combine AI brawn and educational expertise for innovative development.
Final thoughts
The combination of Edge AI and EdTech seems to create the perfect learning environment. By merging expansion and privacy, they’re creating systems for learning that are quick, equitable, and ready for the future.
From distant communities to expansive educational institutions, this unification aims to deliver personalized, safeguarded, and robust educational experiences. In reality, the numbers speak for themselves: With climbing adoption levels and growing concerns over cyberattacks, Edge AI is not an option – it is a necessity for future schools. Let’s embrace the change.
AIAInow is your chance to stream exclusive talks and presentations from our previous events, hosted by AI experts and industry leaders.
It’s a unique opportunity to watch the most sought-after AI content – ordinarily reserved for AIAI Pro members. Each stream delves deep into a key AI topic, industry trend, or case study. Simply sign up to watch any of our upcoming live sessions.
🎥 Access exclusive talks and presentations ✅ Develop your understanding of key topics and trends 🗣 Hear from experienced AI leaders 👨💻 Enjoy regular in-depth sessions
We’ll explore how data scientists, engineers, and end-users can work together seamlessly to unlock the full potential of LLMs, ensuring effective, confident deployment across use cases.
Key points to be covered:
Understanding the LLMOps lifecycle: An overview of the LLMOps lifecycle from model design and development to deployment, monitoring, and refinement. Optimising collaboration: Practical approaches to accelerate collaboration among data scientists, engineers and users. The what, why, and how of LLMOps: A foundational understanding of LLMOps, why it’s critical for organisations, and how to build and scale efficient operations. Real-world scenarios: Case studies showcasing success with LLM applications. Challenges in LLMOps and practical solutions: Addressing common obstacles in LLMOps life cycle.
This presentation is perfect for AI practitioners, developers, and team leaders looking to advance their knowledge of LLMOps.
Yiqi Zhao, Product Design Lead, Meta Reality Labs at Meta gave this talk at the Generative AI Summit in Washington DC, 2025.
I’m Yiqi, the design lead for Meta Reality Labs, the organization that makes many AR/VR glasses, like the Ray-Ban and the Meta Quest series.
Today, I bring a video along with a topic that might not be something you’ve thought about deeply before. But I want you to consider this—can you be a creator?
Can you be someone who makes content and actually makes money from it? Can you create fun, engaging experiences within the new developer ecosystem that’s emerging with devices like the Meta Quest, the Meta Ray-Ban glasses, and the incredible capabilities of AI? Would this be possible?
I want to talk about how you can unlock your creative power and, more importantly, how you can leverage AI to be fully ready for this new platform and the opportunities that come with it.
The rise of immersive content and Meta Horizon
From the video, you might have noticed the rich, detailed 3D immersive content. This isn’t something that’s coming in the future—it’s happening right now on our platform.
We recently rebranded our platform under the Meta Horizon name. Essentially, everything is becoming Horizon.
Meta Horizon is more than just a name change—it represents our vision of a platform that connects people in ways that are richer, more interactive, and more immersive. We want people to socialize, engage, and find their communities in a way that feels natural, just as they do in the real world.
Unlocking your creative power
We are seeing a shift in devices from traditional screens—laptops, phones, tablets—to mixed-reality experiences. The shift is massive.
If you look at traditional devices, they have always had limitations. They are separate from us; they require us to interact with them from a distance. But mixed reality devices, like VR headsets and AR glasses, are different.
If you’ve ever built a GenAI application, you know the drill—your prototype looks amazing in a demo, but when it’s time to go live? Different story.
In this exclusive video, Samin Alnajafi, Success Machine Learning Engineer at Weights & Biases, unpacks why LLMOps is the missing link between promising GenAI experiments and real-world deployment.
Here’s what you’ll learn:
Why so many GenAI projects stall before reaching production
How to measure and optimize performance using LLMOps best practices
Key components of a scalable retrieval-augmented generation (RAG) pipeline
Practical examples and a live demo of Weights & Biases tools
P.S. And if you have a few minutes to spare today, why not share your LLMOps expertise? We know how busy you are, so thank you in advance!
Share the tools you use, the challenges you have, and more, and help define the LLMOps landscape.
Whenever you’re ready, here are three ways we can help you grow your AI career:
Become a Pro+ member. Want to be an expert in AI? Join Pro+ for exclusive access to insights from industry leaders at companies like Meta and Google, one complimentary ticket to an in-person Summit of your choice, experienced mentors, AI advantage workshops, and more.
Become a Pro member. Want to elevate your AI expertise? Join Pro for exclusive access to expert insights from leaders at top companies like HuggingFace and Microsoft, member-only articles and frameworks, an extensive video library, networking opportunities, and more.
AI webinar. Want to unlock smarter, faster, and more scalable incident management? Join us on April 25 for a live session on how AI transforms incident management to accelerate investigations, surface relevant insights, and dynamically scale workflows. Register here.
Exclusive tech leader dinner. Join us in NYC on March 19 for an insightful conversation around the trends, challenges, and opportunities related to harnessing and maximizing Generative AI for the enterprise.
Discuss challenges with the ITSM landscape, real steps to get started with AI-powered solutions, and how it helps with transforming IT.
We invite you to join an exclusive, interactive virtual roundtable with industry peers, thought leaders, and our partners Freshworks.
This is a by-invitation-only event designed for senior IT leaders (minimum Director level) keen on leveraging AI to transform their IT landscape. Reserve your spot and be a part of the conversation.
A 2024 global survey by Harvard Business Review Analytic Services reveals that while 80% of IT decision-makers believe improving ITSM would enhance employee satisfaction, only 22% believe their organizations provide ITSM in a very effective manner.
The solution lies in going back to the basics – reducing complexities, dismantling silos, modernizing ITSM, and aligning it closely with business goals.
The event promises to be interactive as you meet with other leaders from the industry over lively discussions that highlight the focus areas for AI in IT, what challenges to look out for, and how you can showcase quick impact while scaling up your IT maturity with the power of AI.
Why attend?
✔ Engage in dynamic discussions – Collaborate with fellow IT leaders in an interactive setting designed to foster meaningful conversations and knowledge sharing. ✔ Gain exclusive insights – Learn from industry experts about the key focus areas for AI in IT and what challenges to anticipate as you scale IT maturity. ✔ Discover AI-driven solutions – Explore how AI-powered ITSM can dismantle silos, modernize IT operations, and create immediate impact while aligning IT with broader business goals.
Unlike traditional AI systems, agentic AI is defined by its ability to perform tasks autonomously without user prompts. Amazon Web Services (AWS) invested in a dedicated agentic AI group; AWS’s Bedrock platform now features “agents” that allow customers to integrate generative AI models into their operations.
This allows these systems to autonomously access data, trigger actions, and provide end-to-end solutions.
Agentic AI represents a shift in how intelligent systems work. While many current AI applications rely on specific commands or user inputs, agentic AI systems are designed to operate independently. They can handle complex, multistep workflows seamlessly and connect with APIs, data sources, and other tools.
This article offers a high-level overview of agentic AI, examining the technological shift, industry perspectives, and the implications for businesses and developers alike.
AWS CEO Matt Garman announced the creation of a new agentic AI group led by Swami Sivasubramanian. The group aims to advance AI automation and broaden the scope of what AWS’s AI tools can achieve.
AWS sees agentic AI as the “next frontier” of computing, a leap forward from traditional machine learning models that often need human direction at each stage. This initiative builds on AWS’s broader AI strategy, which has long focused on providing scalable, user-friendly machine learning solutions.
AWS’s Bedrock platform exemplifies the potential of agentic AI. By integrating generative AI capabilities with business systems, Bedrock agents can automate multistep tasks, such as extracting data from multiple sources, performing analyses, and triggering follow-up actions.
This functionality streamlines operations and lets businesses respond quickly to changing conditions.
The perspectives about agentic AI
Industry leaders, including those at AWS, are optimistic about agentic AI’s transformative potential.
Travis Rehl, CTO of AWS Premier partner Innovative Solutions, noted that AWS has consistently built infrastructure ahead of market demand, enabling enterprises to adopt groundbreaking technologies before they even realize the need.
Supporters argue that agentic AI will enhance efficiency, reduce costs, and empower businesses to focus on strategic rather than operational tasks.
However, the introduction of agentic AI has not been without skepticism. Some analysts warn that fully autonomous systems could introduce unforeseen risks, including over-reliance on AI decision-making and challenges related to security and transparency.
Concerns about data privacy, for example, are rising as agentive AI systems often need extensive access to sensitive information to function effectively. These critics highlight the need for robust governance frameworks and clear accountability measures.
Broader implications for the AI ecosystem
Enabling systems to act independently could redefine industry best practices and set new standards for efficiency. AWS’s moves will likely spur competition among other major cloud providers and AI leaders, prompting them to develop their agentic AI capabilities. This competitive push could accelerate innovation, making agentic AI an integral part of modern AI workflows.
Beyond technological advances, agentic AI could transform how businesses operate. By automating repetitive and time-intensive tasks, companies can allocate resources toward innovation and growth.
Additionally, as agentic AI becomes more prevalent, developers will need to acquire new skills, such as designing systems that can handle autonomous interactions, manage complex integrations, and ensure that AI-driven processes remain secure and ethical.
The road ahead for agentic AI is both promising and complex. As the technology matures, it will likely expand into new industries and applications, driving further innovation.
However, regulatory challenges, particularly around data privacy and accountability, could slow adoption. User trust is also necessary; businesses must guarantee that agentic AI systems are transparent, reliable, and secure. Technical limitations must be addressed to fully realize the vision of autonomous, end-to-end workflows.
With AWS’s recent initiatives leading the charge, the potential for agentic AI to streamline operations, reduce costs, and enhance productivity is becoming increasingly apparent. At the same time, the industry must navigate the accompanying challenges, from privacy concerns to ethical questions.
By staying informed and engaging in the ongoing conversation, businesses and developers can position themselves to leverage the opportunities that agentic AI presents, ensuring that this transformative technology serves as a tool for progress and innovation.
In 2025, adapting, refining and pivoting strategies will not be a matter of choice, but rather a necessity for survival and expansion for companies. Spending on technologies that support digital transformation is expected to reach 3.9 trillion dollars by 2027.
The figure shows the continued increasing effort by companies in this field. The road ahead is not simple, however, studies indicate that almost 70% of digital transformation endeavors fail due to mismanagement, unsupporting corporate culture, and vague goals.
Take the example of General Electric (GE). Once regarded as an industrial innovations leader, GE pursued a strategy of investing heavily into a digital unit with the hopes of transforming its operations and products. The project turned out to be underwhelming as a result of overly optimistic demand forecasts and internal pushback, and serves as a story of what not to do for other businesses with similar objectives.
Getting your digital transformation strategy right can lead businesses towards endless possibilities and provide a competitive advantage. Adopting a digital transformation strategy is not the challenge, rather mastering it is.
Formulating digital transformation framework to achieve competitive advantage
In simple terms, digital transformation can be described as the integration of digital technologies in every aspect of a business.
This includes the modification of business processes and the manner in which value is provided to clients. Does this enhance competitive advantage? When done effectively, it enables companies to gain the following benefits:
Operational excellence: Companies can use digital tools and platforms to automate the workflows, minimize unnecessary processes and increase productivity, thus improving operational effectiveness.
Improved customers’ lifetime value (CLV): Digital and personalized customer interactions, along with extensive data analytics, help foster strong customer relationships, increase customer satisfaction, and consequently boost profitability in the longer run.
Being the first to market: The adoption of groundbreaking digital solutions permits companies to claim a portion of the market and establish themselves as industry leaders at the same time.
For organisations to digitally transform efficiently, they can leverage pre-existing models, such as the McKinsey 7S Model. This model focuses on seven interrelated elements Strategy, Structure, Systems, Shared Values, Skills, Style, and Staff which help ensure alignment in the processes of transformation.
With the assessment and realignment of these components, businesses can develop an infrastructure that facilitates digital embedding.
Furthermore, the MIT Sloan Digital Business Model framework offers important analyses by investigating the rebalancing of important relationships: Minds and Machines, Products and Platforms, and the Core and the Crowd. This strategy helps the reconfiguration of a company’s primary strategies to make the most out of the digital economy.
As the digital world advances, no industry leader will dispute that for one to be competitive, the core of business strategy has to be based on digital transformation. Essentially, as McKinsey & Company noted, to succeed in digital transformation one has to make trade off decisions that will aid in the reinvention of the business.
Alleviating the complexity of digital transformation is possible when these frameworks and insights are adhered to, in turn facilitating the fostering of sustainable competitive advantage alongside the growing digital landscape.
Many businesses now utilize AI for productivity growth. For example, AI is applied in writing codes, composing content, and even doing workflow management which tremendously increases efficiency.
Companies that have AI integrated into their systems report a ROI that is almost 2 times higher than those who apply AI platforms for specific tasks and functions only. Moreover, 92% of large companies report achieving returns on their deep learning and AI investments.
Real life example
The emergence of DeepSeek, a Chinese AI startup, marked a major turn in the AI scene with its advanced open-source AI model called R1. This model competes with the best in reasoning from OpenAI and Google but at a much lower cost.
DeepSeek trained their R1 for under $6 million, using 2,000 less powerful chips instead of the tens of thousands of specialised chips that would cost around $100 million. The launch of DeepSeek’s R1 has caused established AI companies in Silicon Valley to revise their plans since it indicates a movement from a focus on raw power to one of reasoning and optimization.
In addition, the fact that the model is open source means that any researcher in the world can examine its architecture and build on it, making a more collaborative approach to AI.
Challenges faced
Most companies find it challenging to determine the ROI on AI spending with only 31% of leaders expecting to be able to measure ROI in 6 months.
Emerging patterns
The increased affordability of AI is shifting investments from hardware to software which is positive for companies such as Salesforce and Microsoft.
Expansion of cloud services
Strategy case
Companies are developing their capabilities in cloud services to enhance their flexibility and scalability. Around 45% of companies are upgrading their cloud infrastructure to facilitate the transformation process.
Metrics
Europe’s largest software company, SAP expects its cloud revenues to be between 21.6 bln euros and 21.9 bln euros in 2025 which will increase further due to the growth of cloud computing.
Real-life example
Airbnb uses cloud services to manage peak demand during holiday seasons by enabling “on-the-fly” scaling of infrastructure for high resource consuming events like Black Friday.
Challenges faced
Cloud cost management is a pain point for 82% of cloud decision makers.
Emerging patterns
People want more sustainable features in cloud services, which are often overlooked.
According to IoT Analytics’ 418-page IoT Use Case Adoption Report 2024, IoT initiatives appear more successful than ever, as 92% of enterprises report positive ROI from IoT use case implementations.
Metrics
Various studies predict significant growth in IoT, particularly in industries where IoT can help save operational costs by more than 10-20%.
Real-world example
IoT has been used in environmental conservation through advanced sensing methods that promote monitoring and the protection of forest ecosystems. Advanced sensing technologies allow for a notable real world application of IoT.
Devices have gotten smaller and smarter, becoming much more interconnected in the process, transforming data collection to even the most harsh of conditions. Innovations such as Dryad Networks’ wildfire sensors that detect preemptive signs of a fire, Treevia’s digitized dendrometers for tree growth monitoring, and Rainforest Connection’s illegal activity and wildlife monitoring smartphone powered devices are great examples.
Such technologies like Switzerland’s eDNA-collecting drones, Freiburg University’s leaves sensors, Plant-e’s bacteria powered sensors, and seed dropping drones revolutionize forest conservation efforts by providing critical insight and reforestation.
Challenges faced
Protecting the data and the underlying integration issues remain challenges for IoT implementation.
Emerging patternsand
In 2023, the IoT integration market was predicted to be worth USD 3.83 bln, with an anticipated CAGR growth rate of 28.3% between 2024 and 2030.
This growth can be attributed to the rising number of connected devices, smart cities, advancements in AI and ML, a greater emphasis on cybersecurity, and the growing popularity and capabilities of edge computing.
Furthermore, the stronger focus on data-based decision-making is driving investment expansion, which is further aiding the growth of value from IoT platforms. These platforms enable businesses to collect data and provide analysis and visualization tools, permitting real-time decision-making.
The coming years are expected to provide further tailwinds to growth, enabling IoT’s full potential.
Generative AI can help with content creation
Strategy Case
The integration of AI within branding initiatives has automated the creation of effective marketing material like videos, images, and text which improves marketing and communications strategies.
Metrics
92% of Fortune 500 firms have adopted the technology, including major brands like Coca-Cola, Walmart, Apple, General Electric, and Amazon.
Real life example
A mid-sized tech company in Denver, six months after fully adopting AIContentPad, produced30% more content at 62% less cost, and engagement doubled across key sales channel platforms.
Challenges faced
The inability of Generative AI to produce high quality content and preserving the brand voice are problems that are worth mentioning.
Emerging patterns
There is an increasing trend to deploy Generative AI with customisation to build multiple experiences for single AI model targets.
Digital twin technology
Strategy case
Currently, the engineering and manufacturing sectors primarily use Digital Twins as accurate virtual representations of an object or process simulation. Several publications examine the application of Digital Twins in operational and supply chain management, emphasizing the functions of operations tracking, transport servicing, remote support, asset seeing, and customized design.
Metrics
Organisations that have adopted digital twins have said that the time taken for designing processes was reduced by 30%. They also mention a 25% reduction in expenses related to system upkeep.
Real life example
Altum RF advanced the design of new semiconductor components through the use of a digital twin which enabled them to reduce the design process by 30%.
Challenges faced
The implementation of digital twins will need a considerable initial investment as well as difficulties with data processing from old systems.
Emerging patterns
Progressive cities are increasingly using digital twins for planning as a way to create a simulated city and effectively manage infrastructure and resources. Also, McKinsey research indicates the global market for digital-twin technology will grow about 60% annually over the next five years, reaching $73.5 billion by 2027.
RPA is considered a developing technology that can speed up business procedures by automating mundane and demanding tasks within the supply chain systems. RPA is also known as software robotics or ‘bots’ and is designed to follow instructions provided by the users in order to execute repetitive tasks in business organisations.
Metrics
Companies that have adopted RPA have achieved process time reduction of up to 80% and a decrease in operational costs by 10%-20%.
Real-world example
New Mexico Mutual incorporated RPA and saved 3.5 hours per day from redundant tasks, which allowed employees to focus on critical higher-value activities.
Challenges
Adaptation of RPA can present issues such as lack of standardization for the processes being automated and opposition from employees fearing job loss.
Emerging patterns
The use of RPA on its own is sufficient, however, we have seen a growth in the combining of AI with RPA to create more advanced automation that is capable of making complex decisions.
What’s next?
It’s expected that the merger of AI technologies and 5G by 2025 will radically compress the developmental timeline of digital transformation efforts. Generative AI’s ability to automate the process of content creation will allow firms to facilitate marketing, product design, and customer relations on completely different levels.
Consequently, productivity is expected to rise by at least 20% for companies that embrace these tools. Moreover, customer retention rates have the potential to rise up to 15% due to the use of hyper personalization techniques powered by increased data analytics to provide tailored customer experiences.
Adoption of 5G is expected to improve the connectivity of multiple sectors including healthcare, transportation, manufacturing, and more, enabling real-time data collection and analysis through IoT devices.
This, in conjunction with IoT, 5G is set to increase connectivity and make operations more data-centric. As a result, not only will operational efficiency increase, but more innovative developments like smart cities and self-driving cars will become a reality.
Adopting these advancements is expected to increase operational efficiency by 30%, providing a sustainable competitive advantage in this rapidly changing digital world.
Unlock smarter, faster, and more scalable incident management.
IT teams are under increasing pressure to detect, investigate, and resolve incidents faster than ever. But with siloed data, manual processes, and escalating complexity, teams struggle to keep up, leading to slow resolutions, poor customer experiences, and costly downtime.
Join us and BigPanda where we’ll explore how AI is transforming incident management to accelerate investigations, surface relevant insights, and dynamically scale workflows.
Why attend?
🚨 Siloed data and institutional knowledge make it hard to get a complete picture of incidents.
📉 L1 NOC and service desk teams lack context, leading to unnecessary escalations and slow response times.
🔄 Manual processes and poor communication create inefficiencies, massive bridge calls, and poor documentation.
By attending, you’ll learn how organizations are saving an average of 30 minutes per task during incident investigations.
What you’ll walk away with:
We’ll walk you through real-world use cases and practical strategies to optimize ITSM workflows using AI. You’ll discover how to:
✅ Augment team knowledge – Equip responders with AI-driven insights, including impact assessment, priority scoring, and change risk analysis, so they can resolve incidents faster and more effectively.
✅ Streamline incident processes – Reduce manual, broken workflows by ensuring the right teams are engaged at the right time, improving internal communication and collaboration.
✅ Prevent future incidents – Analyze operational and ITSM data to detect recurring issues, measure gaps, and implement proactive fixes before they escalate.
Hosted by:
Katie Petrillo Senior Director, Product Marketing at BigPanda
As the senior director of product marketing, Katie is an experienced go-to-market leader with a deep understanding of IT Operations, Incident Management, and Cybersecurity markets and buyers. At BigPanda, Katie leads a team of product marketers, and uses customer and market insights to tell compelling stories for customers, prospects, and internal audiences.
Travis Carlson Senior Product Manager – AI Products at BigPanda
Travis Carlson leads product management for AI Products at BigPanda and is known for his ability to quickly study, thoroughly understand, and think critically about extraordinarily complex systems. He’s doing this at BigPanda as he drives the GTM development and release of BigPanda’s generative AI technology, Biggy AI. He’s previously developed products at Cisco and VMware, and has an MBA from MIT.
China’s recently unveiled AI agent, Manus, represents a significant leap forward. Introduced by the Chinese startup Monica, Manus is described as a fully autonomous AI agent capable of handling a wide range of tasks with minimal human intervention.
Since its launch on March 6, 2025, Manus has attracted considerable global attention, sparking discussions about its technological implications, ethical considerations, and potential impact on the AI landscape. This article explores what makes Manus unique, examines the perspectives of its supporters and critics, and considers the broader implications of its development.
The emergence of Manus
Manus differs from conventional AI systems’ ability to independently plan, execute, and complete tasks without constant human supervision. The agent can analyze financial transactions, screen job applicants, and even create websites—all in real-time.
Unlike traditional AI models that rely on pre-programmed inputs or human oversight, Manus learns from user interactions and adapts its approach to achieve its goals. Its creators have positioned it as a competitor to systems from global leaders such as OpenAI and Google.
Manus stands out for its advanced autonomous capabilities, which allow it to handle complex workflows and provide real-time outputs without user intervention. By integrating these features, it opens new doors for automation in industries like:
Human resources:In recruitment, Manus can autonomously screen resumes, evaluate candidate skills, and rank applicants, streamlining the hiring process and reducing human bias.
Real estate: Manus can assess real estate affordability, analyze market trends, and provide personalized property recommendations, enhancing buyers’ and investors’ decision-making.
These applications demonstrate Manus’s potential to set new benchmarks for autonomous AI, offering efficiency and precision in complex workflows.
Perspectives on Manus
Supporters’s views
Supporters argue that Manus could revolutionize industries by significantly improving efficiency and productivity. With its ability to process information and make decisions autonomously, supporters see it as a tool that could reduce costs, streamline operations, and drive innovation.
They highlight the agent’s potential to tackle repetitive tasks, allowing human workers to focus on higher-level, creative endeavors.
Critics’ concerns
On the other hand, critics caution against the ethical and societal risks posed by fully autonomous AI agents. Privacy advocates worry about the potential misuse of Manus’s capabilities, mainly when dealing with sensitive data.
Additionally, concerns about job displacement loom, with some fearing that Manus could render specific roles obsolete. These critics call for more robust regulatory frameworks and transparent mechanisms to ensure accountability.
Global observers’ opinion
Internationally, Manus is seen as a symbol of China’s growing AI prowess. Observers have compared it to leading AI initiatives from companies like OpenAI and Google, noting that Manus’s launch could heighten global competition in the autonomous AI space.
This international attention underscores how pivotal Manus’s development could be in shaping future AI standards and benchmarks.
The emergence of Manus raises critical ethical questions.
How should regulators oversee systems that operate without direct human guidance? What safeguards are needed to ensure that these agents act responsibly and transparently? Current regulatory frameworks are not yet equipped to address the challenges of fully autonomous agents.
To maintain public trust and safety, policymakers must consider new approaches, such as mandatory audits, continuous performance monitoring, and stricter data protection standards.
Autonomy and accountability: Determining responsibility for the actions of autonomous agents like Manus is complex, especially when decisions lead to unintended consequences.
Privacy concerns: Manus’s ability to process vast amounts of data autonomously raises questions about data privacy and the potential for misuse.
Employment impact: Automating complex tasks traditionally performed by humans could lead to job displacement, necessitating discussions on workforce adaptation and reskilling.
The future of Manus and autonomous AI
Manus’s development could inspire a new wave of autonomous AI agents that redefine industries and reshape societal norms. In the coming years, we may see broader deployment of Manus’s capabilities, potential enhancements that increase its utility, and more companies entering the autonomous agent space.
However, this growth must be accompanied by robust policy frameworks and collaborative efforts among global stakeholders to ensure these systems are developed and deployed responsibly.
China’s AI agent Manus represents a significant milestone in autonomous intelligence, blending advanced technology with unprecedented autonomy. By examining its development, technological implications, and the wide range of perspectives it has generated, readers can gain a clear understanding of Manus’s significance in the AI landscape.
As we navigate the challenges and opportunities presented by such advancements, it is crucial to foster an informed dialogue that ensures autonomous AI serves as a force for progress, not harm.
Agentic AI refers to artificial intelligence systems that act autonomously, make decisions, set goals, and adapt to their environment with minimal human intervention. Unlike traditional AI, which follows predefined instructions, agentic AI continuously learns, reasons, and refines its actions to achieve specific objectives.
This type of AI moves beyond simple automation. Traditional AI models rely on predefined rules and patterns, executing tasks within strict boundaries. In contrast, agentic AI exhibits problem-solving capabilities, proactively adjusting its behavior based on new inputs, unexpected changes, or emerging patterns. It functions more like an independent entity than a programmed tool.
Agentic AI is modeled on human-like intelligence, meaning it doesn’t just respond to commands but can initiate actions independently. This includes setting intermediate goals, prioritizing tasks, and iterating on previous efforts to improve results. It can navigate uncertainty, make real-time adjustments, and optimize decisions without constant human oversight.
What sets agentic AI apart is its ability to self-direct. It doesn’t require explicit step-by-step instructions for every scenario—it learns from experience, understands context, and makes informed choices to achieve its objectives. This makes it particularly valuable in dynamic environments with insufficient predefined rules.
Examples of agentic AI include self-driving cars that adapt to unpredictable traffic conditions, AI-powered research assistants that generate and test scientific hypotheses, and autonomous trading systems that make investment decisions based on real-time market shifts. These systems don’t just follow orders; they work toward goals, improving over time through continuous feedback loops.
As AI evolves, agentic capabilities will become increasingly prevalent, shaping industries by enabling machines to take on more complex, independent roles that previously required human intelligence.
Agentic AI operates independently, determining the best action based on the data it gathers. It can analyze complex situations, weigh options, and choose paths optimizing efficiency, performance, or any given metric.
Unlike traditional AI, which relies on human input at every stage, agentic AI continuously assesses and adjusts its approach to maximize effectiveness. This autonomy enables it to function in unpredictable environments, making it a crucial tool for logistics, finance, and healthcare industries.
Goal-oriented behavior
Unlike static AI models that follow rigid instructions, agentic AI defines its sub-goals in pursuit of broader objectives. It can break down significant problems into manageable steps, pivot strategies when necessary, and prioritize actions based on real-time feedback.
By dynamically setting and adjusting goals, agentic AI can handle complex decision-making tasks, such as optimizing supply chains, managing large-scale data analysis, or improving automated customer service interactions.
Self-learning and adaptability
Agentic AI continuously improves by learning from its own experiences. Using reinforcement learning, fine-tuning techniques, or human feedback, it adapts to new challenges and refines its decision-making.
Unlike traditional AI models that require constant updates from developers, agentic AI identifies inefficiencies and self-corrects. This adaptability allows it to excel in fast-changing environments like financial markets, cybersecurity threat detection, and personalized marketing campaigns.
Context awareness
Understanding context is crucial for agentic AI. Whether analyzing user interactions, external data, or real-time environmental inputs, it adjusts its responses accordingly, making it more effective in real-world applications.
For example, an AI-driven medical assistant can interpret patients’ symptoms within the broader context of their medical history, lifestyle, and genetic factors, allowing for more accurate diagnoses.
Similarly, an autonomous vehicle must process road conditions, traffic patterns, and unexpected obstacles to make split-second decisions that ensure safety.
Collaboration with humans and other AI systems
Rather than operating in isolation, agentic AI can collaborate with humans and other AI systems. It interprets human intent, takes feedback, and delegates tasks when necessary.
This characteristic is especially valuable in workplace automation, where AI can enhance human decision-making rather than replace it. For instance, AI-powered project management tools can anticipate deadlines, allocate resources efficiently, and suggest workflow improvements while keeping human stakeholders in control.
Multiple AI agents can communicate and coordinate actions in collaborative AI ecosystems, improving logistics, manufacturing, and scientific research efficiency.
How agentic AI works
1. Perception and data collection
Agentic AI gathers data from multiple sources, including sensors, databases, APIs, and user interactions. This continuous stream of information feeds its decision-making process.
The more diverse and high-quality the data, the more effective the AI becomes. These systems use natural language processing (NLP), computer vision, and data mining techniques to extract meaningful insights and detect patterns from structured and unstructured data.
2. Reasoning and planning
The AI uses advanced algorithms to evaluate available data and map actions to achieve its goals. This step involves predictive modeling, scenario analysis, and strategic decision-making.
Unlike reactive AI, which merely responds to inputs, agentic AI develops a forward-looking strategy. It can simulate potential future scenarios, weigh risks, and optimize for long-term success.
This reasoning process allows it to perform complex problem-solving tasks, such as diagnosing a medical condition, optimizing financial investments, or managing logistics in supply chain networks.
3. Execution and action
Once a plan is formed, the AI system executes actions—whether automating a process, making recommendations, or interacting with humans. Agentic AI can operate across multiple platforms and interfaces, integrating seamlessly with business workflows, robotic systems, and software applications.
For example, in an industrial setting, an AI-powered manufacturing system can adjust machinery parameters in real-time to optimize production efficiency and minimize waste. In digital marketing, an AI-driven content management system can autonomously generate and distribute personalized campaigns tailored to user engagement metrics.
4. Feedback and iteration
Every action feeds new data into the system, allowing the AI to refine future decisions. This iterative loop enables continuous improvement. The AI detects patterns in its successes and failures, tweaking its strategies accordingly.
Reinforcement learning models help the AI optimize its behavior over time, ensuring better outcomes with each iteration. This feedback loop makes agentic AI systems highly adaptive, enabling them to evolve with changing conditions. In cybersecurity, for instance, agentic AI can analyze past threats and proactively develop countermeasures before new attacks occur.
Self-driving cars, robotic process automation (RPA), and smart drones leverage agentic AI to make real-time decisions and operate with minimal human oversight.
These systems use AI to analyze their surroundings, predict possible obstacles, and dynamically adjust their actions to ensure efficiency and safety. For example, autonomous drones in agriculture can monitor crop health and apply fertilizers only where needed, optimizing resource use.
Customer support and chatbots
AI-driven chatbots go beyond scripted responses. They analyze user queries, determine intent, and personalize interactions based on prior conversations and learned behaviors.
Modern agentic chatbots can troubleshoot problems, suggest solutions, and even escalate issues to human representatives when necessary. Some can handle entire customer service processes, such as refund requests and product recommendations, without human intervention.
Healthcare and diagnostics
AI agents assist in diagnosing diseases, recommending treatments, and even autonomously conduct research by analyzing vast medical records and scientific literature datasets.
Agentic AI in healthcare can monitor patients in real time, predicting potential complications before they arise. For example, AI-powered wearable devices track vital signs and alert doctors to early signs of heart disease or diabetes.
Finance and trading
Algorithmic trading, fraud detection, and personalized financial recommendations benefit from agentic AI, which monitors markets and adapts trading strategies accordingly.
Hedge funds and investment firms use agentic AI to process market data at lightning speed, making split-second trading decisions based on real-time financial trends. In fraud prevention, AI systems detect unusual transactions and flag potential risks before money is lost.
Personal assistants
Voice assistants like Siri, Alexa, and Google Assistant are becoming more agentic by predicting user needs, automating tasks, and integrating with smart home devices. They are evolving into proactive assistants that respond to commands and anticipate user needs.
For instance, an AI assistant could schedule meetings based on past behavior, adjust a smart thermostat according to weather forecasts, or automatically reorder groceries when supplies run low.
Scientific discovery and research
AI-driven research tools help scientists analyze massive datasets, formulate hypotheses, and generate new physics, chemistry, and biology theories. In drug discovery, agentic AI accelerates the identification of new compounds by simulating millions of molecular interactions in a fraction of the time traditional methods require.
In physics, chemistry, and biology theories, autonomous AI agents on rovers analyze planetary conditions, adjust exploration paths, and make scientific discoveries without waiting for instructions from Earth.
Manufacturing and supply chain optimization
Manufacturing processes increasingly rely on agentic AI to optimize workflows, reduce waste, and increase efficiency. AI-driven robotics adjust production speeds, detect defects, and predict maintenance needs, preventing costly downtime.
In supply chain management, AI agents track global logistics, anticipate disruptions, and reroute shipments automatically, ensuring smooth operations despite unforeseen challenges.
Cybersecurity and threat detection
With cyber threats growing more sophisticated, agentic AI plays a crucial role in real-time threat detection and response. AI systems monitor network activity, identify anomalies, and autonomously neutralize potential threats before they escalate.
Unlike traditional cybersecurity measures, which rely on predefined rules, agentic AI continuously learns from attack patterns and adapts defenses dynamically.
Challenges and ethical considerations
Control and oversight
Autonomous AI systems need guardrails to prevent unintended consequences. Defining clear boundaries and monitoring their actions ensures alignment with human interests.
Without proper oversight, agentic AI could take unpredictable or harmful actions. Regulatory frameworks, safety protocols, and human-in-the-loop designs must be implemented to mitigate risks.
Bias and fairness
AI learns from data, and biased training data can lead to skewed decision-making. Ethical AI development requires rigorous testing and mitigation strategies to ensure fairness.
Bias in AI systems can perpetuate or even amplify societal inequalities, particularly in hiring, lending, and law enforcement applications. Developers must prioritize diverse and representative datasets and implement fairness audits to prevent discriminatory outcomes.
Security risks
Highly autonomous AI systems are attractive targets for cyberattacks. Ensuring robust security measures is crucial to prevent AI manipulation and unauthorized access.
Malicious actors could exploit agentic AI for financial fraud, disinformation campaigns, or even autonomous cyber warfare. Strong encryption, continuous monitoring, and adversarial testing are necessary to protect AI-driven systems from attacks.
Transparency and accountability
Understanding how agentic AI makes decisions is critical, especially in high-stakes domains like healthcare and finance. Explainability remains a key challenge in AI development.
When AI systems operate opaquely, users and regulators struggle to hold them accountable for errors. Implementing explainable AI (XAI) techniques, such as model interpretability and decision-tracking mechanisms, helps build trust and accountability.
Job displacement and workforce impact
As AI takes over complex tasks, some jobs may become obsolete, while others will evolve. Preparing the workforce for this shift is essential to minimize disruption.
While agentic AI can increase efficiency and productivity, it threatens traditional employment structures, particularly in transportation, customer service, and manufacturing industries. Governments and businesses must invest in retraining programs and workforce transition strategies to mitigate economic displacement.
AI agents making autonomous decisions raise ethical concerns about responsibility and moral judgment. Who is accountable for AI-driven decisions in critical applications such as autonomous weapons or medical diagnostics?
Developers, organizations, and regulators must establish clear ethical guidelines, ensuring AI aligns with human values and legal norms.
AI alignment and safety
Ensuring that agentic AI systems align with human intentions and values is a complex challenge. Misaligned AI could act in ways that contradict societal norms or business objectives.
Research in AI alignment focuses on developing models that understand and prioritize human goals while preventing unintended behaviors that could cause harm.
The future of agentic AI
Agentic AI is set to revolutionize industries by increasing automation, improving decision-making, and enhancing efficiency. As AI becomes more autonomous, businesses and policymakers must proactively address its challenges while leveraging its potential.
With ethical considerations and responsible development, agentic AI can drive innovation and create a more innovative, adaptive future.
The integration of agentic AI across industries
The adoption of agentic AI will continue to expand, transforming how industries operate. In healthcare, AI-driven assistants will collaborate with doctors, analyzing patient data in real time and recommending tailored treatments.
AI autonomously manages portfolios in finance, detecting opportunities and risks far beyond human capabilities. The transportation sector will witness a shift toward fully autonomous logistics networks, optimizing supply chains from production to delivery without human intervention.
Evolving AI-human collaboration
Future AI systems will enhance human productivity rather than replace workers entirely. AI will take over repetitive, data-heavy tasks, allowing humans to focus on creativity, strategic thinking, and interpersonal roles.
Organizations will implement AI-assisted decision-making systems, where AI provides recommendations, but final decisions remain with human operators. This dynamic partnership will help bridge AI’s efficiency with human intuition.
Advancements in AI self-learning and adaptation
As AI research progresses, self-learning capabilities will become even more sophisticated. Future agentic AI will refine its ability to self-improve, correct errors, and develop new problem-solving strategies without human intervention.
Technologies like meta-learning and transfer learning will allow AI systems to adapt knowledge from one domain to another, expanding their capabilities beyond specialized functions.
Regulatory and ethical frameworks will evolve
Governments and organizations must establish comprehensive regulations to ensure AI operates within ethical boundaries. Policies on data privacy, AI accountability, and transparency will shape how AI is deployed.
Expect increased global collaboration to create unified AI governance models, ensuring agentic AI development remains beneficial to humanity rather than a disruptive force.
The road to Artificial General Intelligence (AGI)
While agentic AI today focuses on specific tasks, the long-term trajectory points toward artificial general intelligence (AGI)—AI that can perform any intellectual task a human can.
As agentic AI systems become more advanced, they will develop broader reasoning capabilities, generalization skills, and common-sense understanding, inching closer to AGI. Researchers are working on techniques to ensure AGI remains aligned with human values and goals.
As enterprises race to integrate generative AI into their applications and workflows, adversaries are finding new ways to exploit language models through prompt injection attacks to leak sensitive data and bypass security controls.
But how do these attacks actually work, and what can organizations do to defend their GenAI applications against them?
Join us for an exclusive deep dive with Rob Truesdell, Chief Product Officer at Pangea, as we explore the evolving landscape of prompt injection threats and the latest strategies to secure GenAI applications.
How prompt injection works – A breakdown of direct and indirect techniques, with real-world attack examples and data.
What LLM providers are doing – A look at native defenses built into top models to counteract prompt injection risks.
The insider vs. outsider threat – How adversaries both inside and outside an organization can manipulate GenAI models.
Risk mitigation strategies – Engineering and security best practices to prevent, detect, and respond to prompt injection attempts.
Measuring effectiveness – How to evaluate the efficacy of prompt injection detection mechanisms.
This webinar is a must-attend for security leaders, AI engineers, and product teams looking to understand and mitigate the risks of AI-powered applications in an increasingly adversarial landscape.
Generative artificial intelligence (AI) lets users quickly create new content based on a wide variety of inputs. These can be text, images, animation, sounds, 3D models, and more.
These systems use neural networks to identify patterns in existing data, producing fresh and unique content. One significant advancement in generative AI is the capacity to utilize various learning methods, like unsupervised or semi-supervised learning, during training.
This allows individuals to efficiently use vast amounts of unlabeled data to construct foundation models. These models serve as the groundwork for multifunctional AI systems.
How do you evaluate generative AI models?
There are three main requirements of a successful generative AI model:
1. Quality
Mainly important for applications that interact with users directly, a high-quality generation output is vital. In speech generation, for example, having poor speech quality means it’ll be difficult to understand, and in image generation, outputs need to be visually indistinguishable from natural images.
2. Diversity
Good generative AI models can capture minority modes in their data distribution without compromising on quality. This leads to a minimization of undesired biases in learned models.
3. Speed
A wide variety of interactive applications need fast generation, like real-time image editing for content creation workflows.
How do you develop generative AI models?
There are several types of generative models; combining their positive attributes will lead to even more powerful models:
Diffusion models
Also known as denoising diffusion probabilistic models (DDPMs), these determine vectors in latent space through a two-step process when in training.
Forward diffusion. This process slowly adds random noise to training data.
Reverse diffusion. This process reverses the noise and reconstructs data samples.
New data is created by running the reverse denoising process from entirely random noise.
Diffusion models can, however, take longer to train than variational autoencoder (VAE) models. But the two-step process allows for hundreds, and even an infinite number, of layers to be trained, meaning diffusion models tend to offer the highest quality of output when you’re building generative AI models.
Also categorized as foundation models, diffusion models are large-scale, they’re flexible, and tend to be the best for generalized use cases. Their reverse sampling process does, however, make running them a slow and lengthy process.
Variational autoencoders (VAE) models
Consisting of two neural networks: the encoder and the decoder. When VAE models are given an input, the encoder converts it into a smaller and denser representation of the data.
The compressed representation of data keeps the information needed for a decoder to then reconstruct the original input data while discarding anything irrelevant. Both encoder and decoder work together to learn a simple and efficient latent data representation, allowing users to sample new latent representations that can be mapped through the decoder to create new data.
VAE models can create outputs, like images for example, faster but they won’t be as detailed as the ones from diffusion models.
Before diffusion models, GANs were the most commonly used methodology. These models place two neural networks against each other.
Generator. Creates new examples.
Discriminator. Learns to separate created content as real or fake.
GANs can offer high-quality samples and they often create outputs quickly; the sample diversity, however, is weak, and GANs are better suited for domain-specific data generation.
ChatGPT
Developed by OpenAI, ChatGPT allows users to have free access to basic artificial intelligence content generation. Its premium subscription, ChatGPT Plus, is marketed to users who need extra processing power and want early access to new features.
Key features
Language fluency
Personalized interactions
Conversational context
Language translation
Natural language understanding
Completion and suggestion of text
Open-domain conversations
Use cases
Chatbot
Content generation
Pros
A free version for the general public
Offers more accurate answers and natural interactions
The API lets developers embed a ChatGPT functionality into apps and products
Cons
Can’t access data after September 2021, but plugins may help fix this issue
Can be prone to errors and misuse
Pricing
A free version is available
Paid membership: begins at $0.002 per 1,000 prompt tokens
GPT-4
It creates human-like text responses to both word prompts and questions. Each response is unique, allowing you to enter the same query as many times as you want and get different responses every time.
The latest version of this large language model, GPT-4, has been marketed as more accurate and inventive than its previous iterations while being safer and more stable.
Key features
Multilingual ability
Human-level performance
100 trillion parameters
Enhance steerability
Image input ability
Factual performance improved
Use case
Large language model
Pros
A cost-effective solution
Consistent and reliable time saver
GPT-4 has more extensive safety checks and training than previous versions
ChatGPT is the app and GPT is the brain behind it.
Simply put, this is the difference between GPT and ChatGPT.
For efficiency purposes, in this report, we use ChatGPT as a blanket term for OpenAI’s offerings.
Bard
Both a content generation tool and a chatbot, Bard was developed by Google. It uses LaMDA, which is a transformer-based model, and it’s often seen as ChatGPT’s counterpart.
By May 10, Google opened up access to Bard for everyone and added functionalities such as image processing, coding features, and app integration. This enabled a broad spectrum of users, including developers and marketers from around the globe, to leverage Bard for their professional tasks.
Unlike ChatGPT, which has an information cutoff in September 2021, Google Bard has live internet connectivity, allowing it to provide real-time information. According to Sundar Pichai, CEO of Google and Alphabet, Bard strives to merge the expansive knowledge of the world with the capabilities of large language models, generating high-quality responses by sourcing information from the web.
Notably, Google currently views Bard as an ongoing experiment.
Key features
Rating system for user responses
Can help with tasks related to software development and programming
Built on LaMDA
Available through individual Google accounts
Use cases
Chatbot
Content generation
Pros
Pre-tested extensively
A transparent and ethical approach to AI development
Cons
Only available in English
Not available through Google accounts managed by a Google Workspace admin
No conversational history
Pricing
Free
Midjourney
Midjourney stands as a cutting-edge AI art interface, tapping into generative algorithms to fuel artistic creativity. It helps artists to create distinct and captivating pieces, capitalizing on advanced machine learning methodologies.
Offering both art prompts and ideas, Midjourney can even mold full-fledged artworks in response to user preferences. Its intricate neural network has been shaped by comprehensively studying a variety of artistic datasets, paintings, sketches, and photos.
Midjourney appeals to a diverse audience, from seasoned artists who want a fresh point of view to novices wanting to get started.
Key features
High-resolution images
Great image composition
Collaborative potential
Professional applications of images
Pros
Endless prompt generation
Offers big style diversity
Efficient iteration
Cons
High usage costs
Platform not as user-friendly as other options
Pricing
Basic: $10 per month, 3.3 fast hours
Standard: $30 per month, 15 fast hours per month
Pro: $60 per month, 30 fast hours
Mega: $120 per month, 60 fast hours
How generative AI can impact your work
Speed
Thanks to its capability of producing and assisting in decision-making across several areas, generative AI can considerably speed up work processes in companies. It enhances human input and makes sure that time-consuming tasks are completed in a fraction of the time it typically takes.
With artificial intelligence technologies progressively being integrated into workplaces, we can reasonably expect that businesses will operate at an even quicker pace, which will make adaptability and speed essential for success.
Let’s take a look at ways in which generative AI can help speed up work processes:
1. Improving decision-making
Generative AI can quickly analyze large amounts of data to produce insights or suggestions. In finance, for example, AI can generate investment strategies by considering thousands of data points and trends much quicker than a human analyst could. This leads to faster and potentially more accurate decisions.
2. Enhancing creativity and design
When it comes to architecture or product design, generative AI can produce multiple design variations in minutes according to project needs. This means designers can quickly iterate and refine ideas, cutting down the time traditionally required in the design phase.
3. Streamlining content creation
Generative AI can draft articles, generate graphics, or produce video content at an impressive speed. This quick content-generation ability can be particularly useful for industries like journalism, advertising, and entertainment.
4. Providing instant answers to customers
AI chatbots can offer real-time answers to customer queries, which greatly reduces or even eliminates wait times. Whether it’s helping with troubleshooting, product information, or general inquiries, immediate feedback enhances customer experience.
5. Speeding up research and development
In sectors like biotechnology, for example, AI can predict molecule interactions or simulate experiments at a much quicker rate than traditional methods. This means reduced time-to-market for new drugs or materials.
6. Increasing task automation efficiency
Tasks like data entry, scheduling, and basic administrative duties can be completed faster and more efficiently using generative AI. When these repetitive tasks are addressed quickly, businesses can focus on more complex and strategic endeavors.
7. Completing real-time forecasting
Generative AI can rapidly predict market trends, customer preferences, or inventory needs. This instant forecasting helps businesses to make swift decisions, adjust marketing strategies, or manage stock.
8. Generating training modules
AI-based training programs can be generated based on individual needs, which makes sure that employees are brought up to speed faster. Through this tailored content, training durations are minimized, and efficiency is boosted.
9. Speeding up recruitment processes
Generative AI can quickly screen candidate profiles, matching skills and qualifications with job requirements. This speeds up the shortlisting process and helps companies hire employees faster, which reduces vacant position downtimes.
10. Enhancing cybersecurity
AI systems can detect and neutralize threats in real-time, making sure that business operations are uninterrupted. A fast response to potential threats leads to less downtime and swift work processes.
Generative AI’s role in software development is paving the way for faster, more efficient, and more intuitive software creation processes. This technology can significantly improve writing, testing, and optimizing software, leading to a transformation in how software is conceptualized, developed, and deployed.
Let’s have a look at how generative AI is changing software development:
1. Generating and auto-completing code
This technology can help developers by auto-generating bits of code based on context. By understanding the objective and the existing code structure, AI can suggest or even write snippets, which speeds up the development process.
2. Detecting bugs
By analyzing big code repositories, generative AI models can easily predict where bugs could happen – and even suggest potential fixes. This proactive approach can lead to more stable software and reduce debugging time.
3. Testing software
AI can simulate a variety of user behaviors and scenarios to help test software. This makes sure that comprehensive testing is completed in a fraction of the time, which provides strong and reliable software applications.
4. Providing API integrations
Generative AI can help with the integration of many APIs by understanding their documentation and generating appropriate integration code, simplifying the process of adding new functionalities to applications.
5. Enhancing user interface (UI) design
Generative design tools can create multiple UI variations based on given parameters. Developers and designers can streamline the UI creation process by choosing or iterating from these designs.
6. Providing personalized user experience (UX)
Generative AI tools can analyze user behavior and feedback, suggesting or even implementing UX improvements so the software can then be adapted to meet individual user needs and preferences.
7. Managing and optimizing databases
Artificial intelligence can help with structuring, querying, and optimizing databases. When predicting potential bottlenecks or inefficiencies, AI can ensure straightforward and efficient data operations.
8. Improving security
Generative AI can simulate cyber-attacks or probe software for vulnerabilities. This helps developers strengthen their applications, as they can understand and predict potential security flaws.
Content creation
These technologies are reshaping the daily work process content landscape, as they provide quick, tailored, and efficient content generation. This lets professionals focus on creative or strategic aspects of their tasks.
As artificial intelligence keeps evolving, its integration into everyday work tasks is likely to become even more prevalent, simplifying the content generation process and enhancing overall productivity.
Let’s explore how this technology makes content generation easier for everyday tasks and operations:
1. Drafting reports and documents
Generative AI can quickly draft reports, summaries, or other documents based on provided data or guidelines. Because you don’t start from scratch and have a foundational draft, you can refine it as needed and streamline your work.
2. Content personalization for marketing
Generative AI can greatly help in analyzing user preferences and behavior. It can tailor content to individual users by creating personalized email campaigns or customized product recommendations on e-commerce platforms.
3. Automated journalism
For news outlets and publishers, artificial intelligence can draft news articles or updates, especially for repetitive content like sports scores or financial updates. This lets human journalists focus on in-depth analyses and features.
4. Graphic design
Generative AI tools can generate a variety of visual content, from website banners to product mock-ups. For daily tasks, like social media posts, AI can deliver many design options, easing the rapid content roll-out.
5. Research summaries
AI can process large amounts of literature or data to generate summaries or insights in academia. Instead of filtering through numerous papers, professionals can receive a condensed overview, which accelerates the research process.
6. Email writing
Drafting emails, proposals, or other communications is much faster with generative AI. The technology uses key points or themes to give users a well-structured draft, streamlining daily communication tasks.
7. Educational content
For trainers, educators, or e-learning platforms, AI can generate quizzes, assignments, or study summaries based on provided course material.
8. Article creation
For content-based websites, generative AI can create article drafts, topic suggestions, or even SEO-optimized content. This can be especially useful for maintaining daily content schedules.
9. Social media management
Social media managers can use artificial intelligence to create post captions, responses to comments, or content suggestions based on trends. This means you can have consistent engagement without needing continuous manual input.
10. Meeting notes and minutes
AI tools can process recordings or notes to create succinct minutes or action points. This reduces administrative load after meetings and helps participants have a clear understanding of what was discussed.
Cost reduction
Through using generative AI, businesses can have a competitive advantage by innovating and saving on costs.
With automating, optimizing, and predicting, companies can easily streamline operations, reduce waste, and make sure they get the best value for their outgoings. AI technology keeps evolving, meaning that its potential for cost savings will only grow.
Here are a few ways that AI can help companies save on costs:
1. Product design and prototyping
Generative AI can create many design alternatives by defining specific constraints and parameters. Designers can use AI to rapidly generate hundreds of options in seconds instead of days or even weeks, which reduces both time and material costs.
2. Content creation
Generating content, such as advertising, web designs, or articles, can be a resource-intensive process. Generative AI models can generate human-like text, images, or even videos.
The automation of part of the content creation process helps businesses drastically reduce the costs associated with hiring multiple content creators, graphic designers, and videographers.
3. Personalization and customer engagement
Generative AI tools can create personalized content for users based on their preferences and behavior. This personalization improves user engagement and can result in higher conversion rates.
4. Repetitive task automation
A variety of businesses face the challenge of repetitive and mundane tasks, like data entry, report generation, and simple customer service inquiries. Generative AI can automate these processes, leading to significant savings in labor costs and increasing overall employee efficiency.
5. Enhanced research and development
Generative AI models can help with drug discovery, materials science, and other sectors with intensive research and development. By predicting molecular structures, testing potential scenarios, or simulating experiments, AI can severely minimize the number of physical tests required, which accelerates timelines and saves on costs.
6. Customer service and support
Generative AI-powered chatbots can handle a wide range of customer inquiries without employee intervention. These systems can offer instant answers at any time of day, which leads to improved customer satisfaction while drastically reducing the need for large customer service teams working around the clock.
7. Improved forecasting
Generative AI can be used to simulate different business scenarios, which helps companies to make better-informed decisions about inventory management, sales strategies, and more. By accurately predicting demand or potential business disruptions, companies can reduce waste, avoid overstocking, and optimize supply chains.
8. Training and education
By using Generative AI to create personalized learning paths for employees, businesses don’t need to invest heavily in training programs, seminars, or courses. These AI-driven platforms can adapt to each individual’s learning pace and needs, reducing the time and cost of training.
9. Recruitment and human resources
Screening candidates, processing applications, and performing initial interviews can be time-consuming and expensive. Generative AI tools can analyze large amounts of applications, predict the fit between candidates and jobs, and even automate the initial communication between companies and applicants.
10. Enhancing cybersecurity
Generative AI can simulate cyberattacks and help companies identify vulnerabilities in their systems. This proactive approach can prevent expensive breaches and make sure there aren’t any interruptions in business continuity. AI-driven systems can also monitor networks in real time, identifying and countering threats faster than human-only teams.
Increased personalization
The increasing integration of generative AI into personalization is changing how businesses and platforms interact with and serve their users. By offering highly tailored experiences, products, and services, companies can enhance user satisfaction and encourage deeper loyalty and trust.
Here’s how this technology enhances personalization:
1. E-commerce experience
Generative AI can tailor the shopping experience by analyzing user behavior, preferences, and purchase history. It can also recommend products, offer personalized discounts, or even generate custom product designs, making online shopping a better experience according to individual preferences.
Streaming platforms and social media platforms, for example, can use generative AI to curate content feeds. By understanding user preferences, these platforms can offer highly relevant content, such as articles or posts to improve user engagement.
3. Learning and education
Students can have a more personalized learning path with generative AI. The technology can assess students’ strengths, weaknesses, and learning paces, offering tailored lessons, assignments, or resources for optimal learning outcomes.
4. Marketing and advertising
Companies can use generative AI to create personalized marketing messages, email campaigns, or advertisements. Understanding individual user demographics, interests, and behaviors, helps to make marketing more effective.
5. Health and fitness
Generative AI can create custom workout plans, diet charts, or even mental health exercises by analyzing a person’s health data, goals, and preferences. This leads to a more effective and sustainable wellness journey.
6. Customer support
Chatbots and support systems powered by generative AI can offer personalized solutions based on a user’s past interactions, purchase history, and preferences, for faster and better issue resolution.
7. Product development
Companies can use generative AI to analyze customer feedback, reviews, and preferences to design products or services. Products can then meet market demand and resonate with target audiences.
8. Financial services
Banks and financial institutions can utilize generative AI to offer personalized financial advice, investment strategies, or loan options based on individual financial behavior, needs, and goals.
9. Event planning
Generative AI can create personalized event agendas, travel itineraries, or experiences. It can help plan a city tour based on interests or other more personalized ideas according to every individual user.
10. User interface and experience (UI/UX)
Generative AI can adapt and redesign software or website interfaces based on user behavior. This offers users a smoother, more intuitive, and more engaging digital experience.
5 uses of generative AI tools
Audio applications
Generative AI audio models use machine learning techniques, artificial intelligence, and algorithms to create new sounds from existing data. This data can include musical scores, environmental sounds, audio recordings, or speech-to-sound effects.
After the models are trained, they can create new audio that’s original and unique. Each model uses different types of prompts to generate audio content, which can be:
Environmental data
MIDI data
User input in real-time
Text prompts
Existing audio recordings
There are several applications of generative AI audio models:
1. Data sonification
Models can convert complex data patterns into auditory representations, which lets analysts and researchers understand and explore data through sound. This can be applied to scientific research, data visualization, and exploratory data analysis.
2. Interactive audio experiences
Creating interactive and dynamic audio experiences, models can generate adaptive soundtracks for virtual reality environments and video games. The models can also respond to environmental changes or user inputs to improve engagement and immersion.
3. Music generation and composition
Creating musical accompaniment or composing original music pieces is easy for these models; they can learn styles and patterns from existing compositions to generate rhythms, melodies, and harmonies.
4. Audio enhancement and restoration
You can restore and enhance audio recordings with generative AI, which lets you reduce noise, improve the overall quality of sound, and remove artifacts. This is useful in audio restoration for archival purposes.
5. Sound effects creation and synthesis
Models can enable the synthesis of unique and realistic sounds, like instruments, abstract soundscapes, and environmental effects. They can create sounds that copy real-world audio or completely new audio experiences.
6. Audio captioning and transcription
Helping to automate speech-to-text transcription and audio captioning, models can greatly improve accessibility in several media formats like podcasts, videos, and even live events.
7. Speech synthesis and voice cloning
You can clone someone’s voice through generative AI models and create speech that sounds exactly like them. This can be useful for audiobook narration, voice assistants, and voice-over production.
8. Personalized audio content
Through the use of generative AI models, you can create personalized audio content tailored to individual preferences. This can range from ambient soundscapes to personalized playlists or even AI-generated podcasts.
Like other AI systems, generative audio models train on vast data sets to generate fresh audio outputs. The specific training method can differ based on the architecture of each model.
Let’s take a look at how this is generally done by exploring two distinct models: WaveNet and GANs.
WaveNet
Created by Google DeepMind, WaveNet is a generative audio model grounded on deep neural networks. Using dilated convolutions, it creates great-quality audio by referencing previous audio samples. It can produce lifelike speech and music, finding applications in speech synthesis, audio enhancement, and audio style adaptation. Its operational flow consists of:
Waveform sampling. WaveNet starts with an input waveform, usually a sequence of audio samples, processed through multiple convolutional layers.
Dilated convolution. To recognize long-spanning dependencies in audio waveforms, WaveNet employs dilated convolutional layers. The dilation magnitude sets the receptive field’s size in the convolutional layer, helping the model distinguish extended patterns.
Autoregressive model. Functioning autoregressively, WaveNet sequentially generates audio samples, each influenced by its predecessors. It then forecasts the likelihood of the upcoming sample based on prior ones.
Sampling mechanism. To draw audio samples from the model’s predicted probability distribution, WaveNet adopts a softmax sampling approach, ensuring varied and realistic audio output.
Training protocol. The model undergoes training using a maximum possibility estimation technique, which is designed to increase the training data’s probability when it comes to the model’s parameters.
Generative Adversarial Networks (GANs)
A GAN encompasses two neural networks: a generator for creating audio samples and a discriminator for judging their authenticity. Here’s an overview:
Architecture. GANs are structured with a generator and discriminator. The former ingests a random noise vector, outputting an audio sample, while the latter evaluates the audio’s authenticity.
Training dynamics. The generator creates audio samples from random noise during training and the discriminator’s task is to categorize them. Working together, the generator refines its output to appear genuine to the discriminator, and this synchronization is executed by reducing the binary cross-entropy loss between the discriminator’s findings and the actual labels of each sample.
Adversarial loss. GANs aim to reduce the adversarial loss, which is the gap between real audio sample distributions and fake ones. This minimization rotates between the generator’s enhancements for more authentic output and the discriminator’s improvements in differentiating real from generated audio.
Audio applications. GANs have various audio purposes, such as music creation, audio style modulation, and audio rectification. For music creation, the generator refines itself to form new musical outputs. For style modulation, it adapts the style from one sample to another. For rectification, it’s trained to eliminate noise or imperfections.
Text applications
Artificial intelligence text generators use AI to create written copy, which can be helpful for applications like website content creation, report and article generation, social media post creation, and more.
By using existing data, these artificial intelligence text generators can make sure that content fits tailored interests. They also help with providing recommendations on what someone will most be interested in, from products to information.
There are several applications of generative AI text models:
1. Language translation
These models can be used to improve language translation services, as they can analyze large volumes of text and generate accurate translations in real time. This helps to enhance communication across different languages.
2. Content creation
Perhaps one of the most popular applications, content creation refers to blog posts, social media posts, product descriptions, and more. Models are trained on large amounts of data and can produce high-quality content very quickly.
3. Summarization
Helpful for text summarization, models provide concise and easy-to-read versions of information by highlighting the most important points. This is useful when it comes to summarizing research papers, books, blog posts, and other long-form content.
4. Chatbot and virtual assistants
Both virtual assistants and chatbots use text generation models to be able to interact with users in a conversational way. These assistants can understand user queries and offer relevant answers, alongside providing personalized information and assistance.
5. SEO-optimized content
Text generators can help to optimize text for search engines. They can decide on the meta description, headline, and even keywords. You can easily find out the most search topics and their keyword volumes to make sure you have the best-ranking URLs.
How do generative AI text models work?
AI-driven content generators use natural language processing (NLP) and natural language generation (NLG) techniques to create text. These tools offer the advantage of improving enterprise data, tailoring content based on user interactions, and crafting individualized product descriptions.
Algorithmic structure and training
Content-based on NLG is crafted and structured by algorithms. These are typically text-generation algorithms that undergo an initial phase of unsupervised learning. During this phase, a language transformer model immerses itself in vast datasets, extracting a variety of insights.
By training on extensive data, the model becomes skilled in creating precise vector representations. This helps in predicting words, phrases, and larger textual blocks with heightened context awareness.
Evolution from RNNs to transformers
While Recurrent Neural Networks (RNNs) have been a traditional choice for deep learning, they often have difficulty in modeling extended contexts. This shortcoming comes from the vanishing gradient problem.
This issue happens when deep networks, either feed-forward or recurrent, find it difficult to relay information from the output layers back to the initial layers. This leads to multi-layered models either failing to train efficiently on specific datasets or settling prematurely for less-than-ideal solutions.
Transformers emerged as a solution to this dilemma. With the increase in data volume and architectural complexity, transformers provide advantages like parallel processing capabilities. They’re experienced at recognizing long patterns, which leads to stronger and more nuanced language models.
Simplified, the steps to text generation look like this:
Data collection and pre-processing. Text data gathering, cleaning, and tokenization into smaller units for model inputs.
Model training. The model is trained on token sequences, and it adjusts its parameters in order to predict the next token in a sequence according to the previous ones.
Generation. After the model is trained, it can create new text by predicting one token at a time based on the provided seed sequence and on tokens that were previously generated.
Decoding strategies. You can use different strategies, such as beam search, op-k/top-p sampling, or greedy coding to choose the next token.
Fine-tuning. The pre-trained models are regularly adjusted on particular tasks or domains to improve performance.
Conversational applications
Conversational AI focuses on helping the natural language conversations between humans and AI systems. Leveraging technology like NLG and Natural Language Understanding (NLU), it allows for seamless interactions.
There are several applications of generative AI conversational models:
1. Natural Language Understanding (NLU)
Conversational AI uses sophisticated NLU techniques to understand and interpret the meanings behind user statements and queries. Through analyzing intent, context, and entities in user inputs, conversational AI can then extract important information to generate appropriate answers.
2. Speech recognition
Conversational AI systems use advanced algorithms to transform spoken language into text. This lets the systems understand and process user inputs in the form of voice or speech commands.
3. Natural language generation (NLG)
To generate human-like answers in real time, conversational AI systems use NLG techniques. By taking advantage of pre-defined templates, neural networks, or machine learning models, the systems can create meaningful and contextually appropriate answers to queries.
Using strong dialogue management algorithms, conversational AI systems can maintain a context-aware and coherent conversation. The algorithms allow AI systems to understand and answer user inputs in a natural and human-like way.
How do generative AI conversational models work?
Backed by underlying deep neural networks and machine learning, a typical conversational AI flow involves:
An interface that lets users input text into the system or automatic speech recognition, which is a user interface that transforms speech into text.
Natural language processing extracts users’ intent from text or audio input, translating text into structured data.
Natural language understanding processes data based on context, grammar, and meaning to better understand entity and intent. It also helps it to act as a dialogue management unit in order to build appropriate answers.
An AI model predicts the best answer for users according to the intent and the models’ training data. Natural language generation infers from the processes above to form an appropriate answer to interact with humans.
Data augmentation
Through using artificial intelligence algorithms, especially generative models, you can create new, synthetic data points that can be added to an already existing dataset. This is typically used in machine learning and deep learning applications to enhance model performance, achieved by increasing both the size and the diversity of the training data.
Data augmentation can help to overcome challenges of imbalance or limited datasets. By creating new data points similar to the original data, data scientists can make sure that models are stronger and better at generalizing unseen data.
Generative AI models like Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) are promising for the generation of high-quality synthetic data. They learn the underlying distribution of input data and are able to create new samples that very closely resemble the original data points
Variational Autoencoders (VAEs)
Type of generative model that utilizes an encoder-decoder architecture. The encoder learns a lower-dimensional representation (latent space) of the input data and the decoder rebuilds the input data from the latent space.
VAEs force a probabilistic structure on the latent space that lets them create new data points by sampling from learned distribution. These models are useful for data augmentation tasks with input data that has a complex structure, like text or images.
Generative Adversarial Networks (GANs)
Consisting of two neural networks, a discriminator and a generator, that are simultaneously trained. The generator creates synthetic data points and the discriminator assesses the quality of the created data by comparing it to the original data.
Both the generator and the discriminator compete against each other, with the generator attempting to create realistic data points to deceive the discriminator. The discriminator tries to accurately tell apart real and generated data, and as the training progresses, the generator gets better at producing high-quality synthetic data.
There are several applications of generative AI data augmentation models:
1. Medical imaging
The generation of synthetic medical imaging like MRI scans or X-rays helps to increase the size of training datasets and enhance diagnostic model performance.
2. Natural language processing (NLP)
Creating new text samples by changing existing sentences, like replacing words with synonyms, adding noise, or changing word order. This can help enhance the performance of machine translation models, text classification, and sentiment analysis.
3. Computer vision
The enhancement of image datasets by creating new images with different transformations, like translations, rotations, and scaling. Can help to enhance the performance of object detection, image classification, and segmentation models.
4. Time series analysis
Generating synthetic time series data by modeling underlying patterns and creating new sequences with similar characteristics, which can help enhance the performance of anomaly detection, time series forecasting, and classification models.
5. Autonomous systems
Creating synthetic sensor data for autonomous vehicles and drones allows the safe and extensive training of artificial intelligence systems without including real-world risks.
6. Robotics
Generating both synthetic objects and scenes lets robots be trained for tasks like navigation and manipulation in virtual environments before they’re deployed into the real world.
How do generative AI data augmentation models work?
Augmented data derives from original data with minor changes and synthetic data is artificially generated without using the original dataset. The latter often uses GANs and deep neural networks (DNNs) in order to generate synthetic data.
There are a few data augmentation techniques:
Text data augmentation
Sentence or word shuffling. Change the position of a sentence or word randomly.
Word replacement. You can replace words with synonyms.
Syntax-tree manipulation. Paraphrase the sentence by using the same word.
Random word insertion. Add words at random.
Random word deletion. Remove words at random.
Audio data augmentation
Noise injection. Add random or Gaussian noise to audio datasets to enhance model performance.
Shifting. Shift the audio left or right with random seconds.
Changing speed. Stretches the times series by a fixed rate.
Changing pitch. Change the audio pitch randomly.
Image data augmentation
Color space transformations. Change the RGB color channels, brightness, and contrast randomly.
Image mixing. Blend and mix multiple images.
Geometric transformations. Crop, zoom, flip, rotate, and stretch images randomly; however, be careful when applying various transformations on the same images, as it can reduce the model’s performance.
Random erasing. Remove part of the original image.
Kernel filters. Change the blurring or sharpness of the image randomly.
Visual/video applications
Generative AI is becoming increasingly important for video applications due to its ability to produce, modify, and analyze video content in ways that were previously impractical or impossible.
With the growing use of generative AI for video applications, however, some ethical concerns arise. Deep Fakes, for example, have been used in malicious ways, and there’s a growing need for tools to detect and counteract them.
Authenticity verification, informed consent for using someone’s likeness, and potential impacts on jobs in the video production industry are just some of the challenges that still need to be navigated.
There are several applications of generative AI video models:
1. Content creation
Generative models can be used to create original video content, such as animations, visual effects, or entire scenes. This is especially important for filmmakers or advertisers on a tight budget who might not be able to afford extensive CGI or live-action shoots.
2. Video enhancement
Generative models can upscale low-resolution videos to higher resolutions, fill in missing frames to smooth out videos, or restore old or damaged video footage.
3. Personalized content
Generative AI can change videos to fit individual preferences or requirements. For example, a scene could be adjusted to show a viewer’s name on a signboard, or a product that the viewer had previously expressed interest in.
4. Virtual reality and gaming
Generative AI can be used to generate realistic, interactive environments or characters. This offers the potential for more dynamic and responsive worlds in games or virtual reality experiences.
5. Training
Due to its ability to create diverse and realistic scenarios, generative AI is great for training purposes. It can generate various road scenarios for driver training or medical scenarios for training healthcare professionals.
6. Data augmentation
For video-based machine learning projects, sometimes there isn’t enough data. Generative models can create additional video data that’s similar but not identical to the existing dataset, which enhances the robustness of the trained models.
7. Video compression
Generative models can help in executing more efficient video compression techniques by learning to reproduce high-quality videos from compressed representations.
8. Interactive content
Generative models can be used in interactive video installations or experiences, where the video content responds to user inputs in real time.
9. Marketing and advertising
Companies can use generative AI to create personalized video ads for viewers or to quickly generate multiple versions of a video advertisement for A/B testing.
10. Video synthesis from other inputs
Generative AI can produce video clips from textual descriptions or other types of inputs, allowing for new ways of storytelling or visualization techniques.
Generative video models are computer programs that create new videos based on existing ones. They learn from video collections and generate new videos that look both unique and realistic.
With practical applications in virtual reality, film, and video game development, generative video models can be used for content creation, video synthesis, and special effects generation.
Creating a generative video model involves:
Preparing video data
The first step includes gathering a varied set of videos reflecting the kind of output to produce. Streamlining and refining this collection by discarding any unrelated or subpar content guarantees both quality and relevancy. The data must then be organized into separate sets for training and validating the model’s performance.
Choosing the right generative model
Picking an appropriate architecture for generating videos is vital. Potential choices include Variational Autoencoders (VAEs) or Generative Adversarial Networks (GANs). The options are:
Variational Autoencoders (VAEs). These models acquire a latent understanding of videos and then craft new sequences by pulling samples from this acquired latent domain.
Generative Adversarial Networks (GANs). These models consist of a generator and discriminator that work in tandem to produce lifelike videos.
Recurrent Neural Networks (RNNs). Models adept at recognizing time-based patterns in videos, producing sequences grounded in these identified patterns.
Conditional generative models. These models create videos based on specific given attributes or data. Factors like computational needs, intricacy, and project-specific demands need to be taken into account when selecting.
Training process for the video generation model
The structure and hyperparameters for the selected generative model are outlined. The curated video data teaches the model, aiming to create both believable and varied video sequences. The model’s efficacy needs to be checked consistently using the validation dataset.
Refining the output
If needed, the generated sequences need to be adjusted to uplift their clarity and continuity. Employ various enhancement techniques, such as diminishing noise, stabilizing the video, or adjusting colors.
Assessment and optimization of the model
The produced videos need to be examined by using multiple criteria, like their visual appeal, authenticity, and variety. Opinions from specialized users or experts can be helpful in gauging the utility and efficiency of the video-generating model.
Putting the model to use
If everything is working as it should, the model can be launched to produce new video sequences. The video generation model can be utilized in diverse areas, including video creation, special cinematic effects, or immersive experiences in virtual reality.
As a result of artificial intelligence’s continuous evolution, there’s an increasing and ever present demand for more efficient, faster and scalable AI solutions. Traditional AI models, especially deep learning approaches, always require exhaustive computational resources which can make them massively expensive and power-hungry.
In light of these challenges, there are many next-generation AI architectures that are emerging as promising alternatives such as hyperdimensional computing (HDC), neuro-symbolic AI (NSAI), capsule networks, and low-power AI chips.
This article is an exploration into how these innovations can power AI algorithms, in turn making them more efficient and accessible for business use cases and applications.
Hyperdimensional computing (HDC) for AI acceleration
Hyperdimensional computing (HDC) is a novel type of computing paradigm that fully encodes and processes information using high-dimensional vectors. HDC is very different from normal computing models that tend to need to use exact numerical operations, HDC is a way to create AI that mimics the way our brain encodes and processes information in turn enabling faster learning and better generalisation.
Why is HDC impacting the future of AI?
Accelerated learning: Contrary to normal deep learning models that tend to need thousands of training samples, HDC models excel at learning from a small amount of data whilst not losing accuracy.
Robustness: HDC is resistant to noise by default, making it incredibly fit for real-world AI applications in fields such as healthcare, finance, quantum computing and cybersecurity.
Energy efficiency: Since HDC relies solely on binary operations instead of super complex floating-point arithmetic it significantly reduces energy required for advanced AI making it more viable for low-power devices and edge computing.
Advanced fraud detection: Banks and other financial institutions can employ HDC to identify fraud patterns within transactions very quickly.
Healthcare diagnostics: HDC-powered models can recognise medical conditions with much fewer training samples in turn reducing their dependency on massive labeled datasets.
Edge AI: HDC is incredibly beneficial for AI applications running on edge devices such as smart sensors and IoT systems.
Neuro-symbolic AI in edge computing
Normal deep learning models work really well in structured environments but really tend to struggle when asked to reason, explain their decisions or adapt to novel information. Neuro-symbolic AI (NSAI) combines the deep learning approach with symbolic reasoning in turn making AI systems more interpretable and adaptable.
How does NSAI benefit edge computing?
Reasoning and learning: Different from deep learning models that learn from patterns alone, NSAI integrates deep symbolic rules that allow AI to naturally reason and make decisions.
Efficient decision-making: This hybrid approach lessens the need for massive datasets in turn allowing AI to work effectively on edge devices where processing power is limited.
Explainability: Since NSAI models incorporate natural rules and logic, they provide clear justifications for their decisions in turn making them far more trustworthy in regulated industries like healthcare and finance.
Business applications
Autonomous vehicles: AI-powered decision-making in self-driving cars can be vastly improved using NSAI by combining sensor data with predefined road safety and other complex rules.
Smart manufacturing: Predictive maintenance powered by NSAI can further help factories reduce downtime and optimise their machinery performance.
Customer service AI: AI chatbots using NSAI can provide much more human-like interactions, for example, they can deeply understand customer intent beyond simple pattern matching.
Transformers have constantly been at the forefront of AI advancements, especially in natural language processing (NLP) and image generation. That being said, Capsule Networks (CapsNets) offer us an alternative that addresses most of the inefficiencies found with traditional deep learning models.
Transformers: Strengths and drawbacks
Transformers including models like GPT-4 and BERT, excel at understanding complicated language and generating very human-like text.
They do however have limitations:
High computational cost: They require extensive computational resources, making them very difficult to deploy on edge devices.
Lack of hierarchical understanding: Transformers treat all data as sequences in turn limiting their ability to understand deep spatial relationships in images.
Capsule networks: A more efficient alternative?
CapsNets were designed to overcome the limitations of convolutional neural networks (CNNs) and transformers.
They offer:
Better representation of spatial hierarchy: Unlike CNNs which always lose spatial information when pooling data, CapsNets maintain this information in turn making them better for image recognition tasks.
Fewer training samples: CapsNets generalise quite well with fewer samples also reducing the need for massive labeled datasets.
Improved generalisation: Unlike transformers, which require fine-tuning for every new domain found, CapsNets can better recognise patterns across different contexts.
Business applications
Medical imaging: Capsule Networks can improve the accuracy of diagnosing certain diseases in radiology and pathology.
Autonomous drones: CapsNets help drones better understand environments in turn reducing reliance on massive amounts of training data.
Cybersecurity: AI-driven intrusion detection systems (IDS) using CapsNets can better recognise attack patterns with very limited training data.
Low-power AI chips and quantum-inspired computing
One of the biggest challenges in AI today is energy consumption. As AI models grow larger and larger, they require more processing power, leading to completely unsustainable energy demands.
Low-power AI chips and quantum-inspired computing offer us several potential solutions.
Low-Power AI chips
Neuromorphic chips: Inspired by the brain, these chips use spikes instead of traditional binary computation in turn drastically reducing energy consumption.
Edge AI processors: Custom AI accelerators designed for mobile and IoT applications can run AI workloads without draining battery life.
Memory-in-compute ships: These chips integrate memory and computation in turn,For every layer of business understanding, these advancements are crucial in making strategic investments in AI technologies. reducing data transfer bottlenecks and increasing processing speed.
Quantum-inspired computing
Quantum annealing for optimisation: Quantum-inspired approaches help us to solve complex optimisation problems faster than traditional AI models.
Hybrid AI-quantum systems: Some companies are exploring AI models that integrate classical deep learning with quantum-inspired algorithms to further enhance their efficiency.
Business applications
Supply chain optimisation: AI models powered by quantum-inspired techniques can optimise logistics and delivery routes in real-time.
Financial modeling: AI-driven risk assessment and fraud detection can be enhanced using quantum-inspired methods.
Smart cities: Low-power AI chips enable efficient traffic control, energy management and real-time monitoring of city infrastructure.
As AI becomes more intertwined with our everyday lives the need for more efficient, interpretable and scalable models is more important than ever.
Hyperdimensional computing, neuro-symbolic AI, capsule networks and low-power AI chips are guiding the way for AI systems that are powerful but also practical for real-world applications.
For every layer of business understanding, these advancements are crucial in making strategic investments in AI technologies. Companies that adopt these next-generation architectures will gain a competitive edge by delivering AI-powered solutions that are faster, more efficient and easier to deploy across multiple environments.
Now is the time to explore these innovative AI architectures and leverage them to build the future of intelligent computing.
Large Language Models (LLMs) have demonstrated extraordinary performance in various benchmarks, ranging from complex mathematical problem-solving to nuanced language comprehension.
However, these same models fail almost completely on EnigmaEval—a test suite specifically designed to measure spatial reasoning and puzzle-solving skills. This glaring gap in AI competency not only highlights the current shortcomings of LLMs but also raises important questions about how to improve them, especially for practical applications in business, engineering, and robotics.
In this article, we will explore:
LLM performance in math benchmarks vs. EnigmaEval
Why LLMs Struggle with simple spatial reasoning
The implications for AI-powered automation
Potential solutions: Enhancing spatial intelligence through humans, reinforcement learning, and mixture-of-experts (MoE) models
1. LLM performance in math benchmarks vs. EnigmaEval
LLMs have proven their worth on a variety of math-focused benchmarks but falter on spatial puzzles:
Fig-1 : Excellent in Math, faltering in simple spatial puzzles
While these models excel in complex abstract reasoning and numerical computations, their near-total failure in EnigmaEval exposes a significant deficit in spatial reasoning capabilities.
Fig-2 : Actual ScoreFig-3 : Sample Questions : Link for the entire Q:
2. Why do LLMs struggle with simple spatial reasoning?
A. Text-based training bias
LLMs are predominantly trained on textual data and are optimized to find linguistic and statistical patterns.
Spatial reasoning, particularly when it involves 3D object manipulation or visual geometry, is not well-represented in text corpora. Consequently, these models lack the “visual scaffolding” that humans naturally acquire from interacting with the physical world.
B. Lack of embodied experience
Humans develop spatial intuition through embodied experiences—seeing objects, picking them up, navigating spaces, and manipulating items in real life. LLMs, in contrast, have no direct sensory inputs; they rely solely on textual descriptions, limiting their ability to form the mental models required for spatial or causal reasoning.
Even if an LLM can parse a textual description of a puzzle, the lack of spatial or physical “muscle memory” leads to misguided outputs.
D. Limitations of current architectures
Models like Transformers are exceptionally good at sequence-to-sequence transformations (i.e., text in, text out) but are not natively designed for spatial manipulation.
While some architectures (e.g., Mixture-of-Experts, hierarchical or multimodal systems) have begun to incorporate specialized “expert” modules, mainstream LLMs often do not focus on dedicated spatial-reasoning subcomponents—yet.
3. What does this mean for businesses?
A. LLMs may struggle in key business automation areas
Business processes that implicitly involve spatial understanding can suffer if they rely solely on traditional LLM outputs. Examples include:
Debugging git issues – While text-based merges can be handled, any refactoring that requires visualizing complex dependencies or branching structures may lead to poor results.
Data visualization & analysis – LLMs often fail to interpret charts, graphs, and heatmaps effectively, limiting their utility in business intelligence.
Manufacturing & robotics – Spatially dependent tasks such as assembly line coordination or robotic manipulation demand spatial cognition that current LLMs lack.
Navigation & mapping – Autonomous vehicles and logistics optimizations require AI to handle maps, sensor data, and 3D structures—a challenge for text-anchored models.
B. Prevalence of spatial reasoning tasks
A surprising amount of business and engineering work involves spatial reasoning:
Most of engineering applications (CAD design, architecture)
Some of business analytics tasks (interpreting graphical trends, dashboards)
Some of coding tasks (complex code refactoring, dependency resolution)
Without improvements in spatial understanding, LLMs will remain limited in real-world automation and problem-solving.
One pathway to better spatial reasoning is to fuse text-based LLMs with vision and 3D simulation models. In a Mixture-of-Experts (MoE) architecture, different “experts” handle specific modalities—text, images, point clouds—while a high-level gating network decides which expert to consult. For instance, an “expert” in geometric transformations could help parse and manipulate visual puzzle data, supplementing the LLM’s linguistic strengths.
B. Reinforcement learning and simulation
Reinforcement learning (RL) provides an interactive framework for models to learn from trial and error. By placing AI agents in 3D simulated environments—think robotics simulators, game engines, or specialized puzzle platforms—they can develop an embodied sense of how objects move and interact.
Reward functions – Encouraging correct spatial manipulations or puzzle solutions
Humans can act as on-demand “experts” to guide AI systems during training or real-time decision-making:
Active learning – Human annotators can correct or guide models on spatial tasks, refining their understanding.
Hybrid systems – Combining a human’s intuitive spatial reasoning with an LLM’s processing power can lead to better outcomes, especially in high-stakes scenarios like architecture or surgical robotics.
D. Neural-symbolic and knowledge-based methods
Some researchers advocate blending neural networks with symbolic reasoning engines that can encode geometric and physical laws. Symbolic modules could handle geometric constraints (e.g., angles, distances, volume) while the neural net handles pattern recognition. This hybrid approach aims to give AI a “grounded” understanding of space.
The dismal performance of LLMs on EnigmaEval is not an isolated data point; it underscores a core limitation in current AI models—namely, the lack of spatial reasoning. For businesses and developers relying on AI-driven automation, this shortfall can be a significant barrier. Yet, the path forward is promising:
Mixture-of-experts (MoE) architectures can incorporate specialized spatial or vision “experts.”
Reinforcement learning and simulated 3D environments can imbue AI with a more embodied sense of space.
Human collaboration ensures that AI remains grounded in real-world tasks that require physical intuition and problem-solving.
Ultimately, bridging the gap between text-based reasoning and spatial understanding will be essential for AI’s next leap forward.
Models that can genuinely perceive, manipulate, and reason about the physical world will transform a wide array of industries—from logistics and robotics to design and data analytics—ushering in an era of more versatile, reliable, and cognitively flexible AI systems.
Helenio Gilabert
