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.
