Agentic AI: Revolutionizing Enterprise Automation and Decision-Making

Agentic AI will revolutionize business in the cognitive era

Agentic AI could lead to a hybrid workforce, but policy-makers and wider society must ensure that it is developed responsibly. Historically, technological revolutions have augmented our physical capabilities, from the steam engine to the assembly line. But today's transformation is different. We are entering a new era where technology is no longer just a tool, it is becoming an active participant in decision-making. By amplifying our cognitive abilities, artificial intelligence (AI) is redefining the nature of work, leadership and strategy. This is the beginning of the cognitive era, an age where human intelligence is extended through machines that perceive, learn and act alongside us. The cognitive era holds the promise of reshaping how enterprises operate, compete and grow. In a recent Harvard Business Review study, 86% of CEOs acknowledged that rising complexity is no longer just a challenge, it is a barrier to growth. What stands in the way of better decisions? For many enterprises, it's a combination of factors: a shortage of specialized talent, fragmented systems, inefficient manual processes and overwhelming volumes of data. These pressures are compounded by rapidly-evolving customer expectations and the need for real-time responsiveness. To thrive in today's environment, businesses must move beyond incremental improvements. They need intelligent, scalable systems capable of operating with minimal human intervention. This is where the cognitive enterprise begins to take shape. Cognitive enterprises continuously learn, adapt and improve by using AI. They move beyond automation, driving faster, more precise and adaptive actions across strategy and execution. This is best understood as an intelligent flywheel: a self-reinforcing loop where sensing, thinking, acting and learning continuously compound to accelerate enterprise performance. To understand the power of cognitive enterprises, consider the taxi industry. For decades, the traditional taxi model remained unchanged, disconnected from technology, with static pricing and limited availability. Then came Uber and its peers: digitally native enterprises powered by real-time data, predictive algorithms and platform coordination. Within a few years, ride-hailing platforms matched and surpassed the scale that traditional taxi businesses took 80 years to achieve. By embedding intelligence into every rider-driver interaction, these platforms optimized routes, reduced idle time and unlocked massive network effects, transforming customer satisfaction into dominance. It wasn't just better service; it was smarter, faster and more adaptive service. This is the hallmark of a cognitive enterprise. The technology powering cognitive enterprises comes in the form of AI agents. These agents can handle a wide range of tasks depending on their role, the complexity of actions they manage, their placement within the enterprise, and the underlying technology that powers them. Understanding these four dimensions helps clarify both their current applications and future potential. AI agents can serve a range of roles with some taking on multiple roles that include: Orchestrating the efforts of multiple specialized agents to solve complex, interconnected problems. AI agents operate across a broad spectrum of complexity, from simple tools to highly sophisticated systems. At the most basic level, they function as ultra-narrow models, retrieving information to answer a single, well-defined question without reasoning or planning. As they grow more advanced, agents can autonomously orchestrate workflows across domains, harmonizing data and managing complex processes. At the highest level today, agents can coordinate multiple specialized agents, each with its own supervision and governance, to execute adaptive, enterprise-wide workflows. Agents can be embedded across a wide range of business functions, including marketing, sales, operations, product development, HR, strategy and finance, where they enhance both decision quality and operational efficiency. Examples include agents that generate customer insights, forecast demand, optimize operations and support strategic scenario planning, among other high impact uses. AI agents are shaped by the technology types they are built on, such as predictive, generative and AI workflows. Predictive AI enables agents to forecast outcomes, simulate scenarios and inform data-driven decisions, while generative AI powers the creation of new content, ranging from text and images to code and media, supporting more dynamic and personalized outputs. Finally, structured AI workflows integrate multiple models and tools to manage complex, multi-step tasks. The four dimensions determine how an agent perceives, reasons and acts, defining its scope, sophistication and potential impact within the enterprise. These offer a structured lens to design and evaluate AI agents. Many enterprises are exploring AI agents, but efforts often remain fragmented, limited to individual functions and low in sophistication. Without coordination, these agents fail to deliver their full potential. Agentic AI addresses this by integrating individual agents into a connected, strategy-aligned system enriched with sector-specific knowledge. Rather than functioning as standalone tools, agents collaborate across the enterprise. This orchestration of intelligence is what enables true, organization-wide transformation. To scale this approach, enterprises need more than generic AI solutions. They require specialized enterprise AI platforms, designed to deploy, coordinate and evolve intelligent agents at scale. As in past technological shifts, new infrastructure players are emerging to fill this role - the Oracles and SAPs of the cognitive era. Embedding agentic AI through such platforms allows companies to shift from isolated experiments to intelligent systems that learn and act cohesively across business functions. But this shift is not instantaneous, it requires a maturity journey across three dimensions: Progress along these dimensions determines how effectively an enterprise can operate as a truly cognitive organization - intelligent, adaptive and aligned from end to end. As AI agents become more capable, the age of human-only enterprises is coming to an end. CEOs will soon manage not just people, but hybrid workforces of humans and intelligent agents. This shift prompts a critical question: where is this headed? Some experts envision a future where agentic AI and enterprise platforms are so advanced that a single individual could run an entire company. In this model, every operational, strategic and customer-facing task is handled by a network of AI agents, seamlessly coordinated through a central platform. More radical still is the possibility of fully autonomous enterprises, operating without any human involvement. While this may seem distant, the foundations are already in place. As decision-making becomes embedded and automated, human roles will shift from execution to oversight and innovation. Fewer individuals may drive greater impact, amplified by intelligent agents. This future, however, comes with profound societal implications. If machines can replicate cognition, what becomes of human work, value and purpose? The cognitive enterprise must not be defined by performance alone, but by how it contributes to human progress. As we move forward, ensuring that AI augments rather than replaces humanity is a responsibility shared by leaders, policy-makers and society at large.

The path to Agentic AI: overcoming complexity to embrace the autonomous enterprise

Empowering enterprise operations with autonomous AI agents The future of enterprise AI isn't just about insights - it's about a monumental evolution of how businesses buy and sell in the global economy. AI agents are poised to take automation beyond any capabilities that we've witnessed to date, shifting from AI tools that assist decision-making to independently thinking entities that augment execution at scale. Deloitte predicts that by 2027, half of all companies will use GenAI to launch agentic AI pilots or proofs of concept, marking a significant transformation in how businesses operate. While agentic AI holds immense promise, organizations must first overcome multiple hurdles. Case in point: Another recent survey found that more than 85 percent of enterprises will require upgrades to their existing technology stack in order to deploy AI agents. Most businesses are still in the early stages of AI adoption, and scaling agentic workflows from initial investments to drive enterprise-wide ROI remains a major challenge. The road to agentic AI requires rethinking IT infrastructure, ensuring seamless and quality data integration, addressing security and compliance risks, and fostering organizational trust in autonomous solutions - all while ensuring the right guardrails are in place. Without a well-defined strategy, companies risk inefficiencies, implementation barriers, reputational risk, and missed opportunities to harness AI's full potential. Agents individually aren't enough. They can't be deployed in isolation and need to work in coordination across systems to execute complex multi-step processes - manifesting as agentic workflows. Unlike monolithic systems with predictable interactions, an agentic workflow orchestrates a network of AI agents to solve intricate and layered problems autonomously with machine-scale analysis and human in the loop decision making. Businesses need advanced orchestration frameworks capable of managing these complex interactions, ensuring robust error handling and maintaining workflow continuity across teams. Developing a clear roadmap will be critical in helping organizations deploy and scale AI agents effectively. With multiple agentic workflows operating independently yet collaboratively, ensuring accountability is a major challenge. Without a well-defined governance model, businesses risk a lack of oversight, which can lead to noncompliance, financial discrepancies, and reduced trust in AI-driven processes. Agents need to understand the rules of business that humans follow - rules that are defined by legal frameworks, ethical practices, and captured in contracts between customers, suppliers, and partners. By "gut checking" decisions against contractual terms before taking action and ensuring clear audit trails are in place across the business, agentic decision-making becomes transparent and traceable, and far less likely to result in unnecessary liability. In any enterprise system, it's critical for organizations to handle sensitive information responsibly and securely. Before deploying agentic workflows, ensure that data is clean and structured so sensitive information may be used by multiple agents simultaneously without exposure. This applies to bank account details that are necessary for supplier payments, employee personal information, and contract data, as prime examples. Businesses should also establish secure data pipelines and continuous compliance measures to mitigate risks while enabling AI agents to function effectively and responsibly. Adopting agentic workflows requires more than just technical capability - it demands cultural change. Many organizations struggle with trusting AI agents due to concerns about reliability, accuracy, bias, ethical implications, and lack of transparency. In fact, a recent study revealed data output quality and security and privacy concerns are among the top 10 barriers to AI adoption. Resistance to change within organizations, combined with a lack of understanding of how AI agents work, can create obstacles. For businesses to fully embrace agentic AI, increase AI literacy and awareness around how AI agents operate with internal training and a top-down call to action driven by leadership. Emphasizing security protocols and privacy protections will also help to build confidence. So where can businesses realize immediate value from AI agents and agentic workflows? AI agents are only as good as the data they train on. If enterprises want to drive profitability and capture returns from their AI strategy, they should start by looking at the data that drives the flow of commerce. Commercial agreements and the critical data they contain are foundational to how enterprises buy and sell, while also providing the compliance constraints agents need to do their jobs well without adding layers of risk. The path to agentic AI is not a straight line. Yet by strategically addressing challenges, businesses can unlock new levels of intelligence and operational efficiency to embrace their future as an autonomous enterprise. We list the best performance management software.

Agentic AI and the future state of enterprise security and observability

With its ability to reason, adapt, and take action autonomously at machine speed, agentic AI has the power and potential to dramatically change how enterprises maintain their digital resilience. It also redefines how they secure and deliver reliable performance for their digital ecosystems, where data pattern recognition and decision-making need to happen in real time and at machine speed. With agentic AI, companies get the benefits of a conversational analysis experience from LLM reasoning and adaptation plus the automation of task execution from the agentic framework. Together these shift IT teams from reactive fire-fighting mode to proactive planning mode. Here's how. The promise of agentic AI for digital resilience 1. Pinpoint root-cause (almost) instantly Agentic AI can cross siloed application boundaries to bring data insights together for more complete visibility. For example, agentic AI can use LLMs to analyze logs, metrics, events, and trace data; call upon different monitoring systems in your ecosystem; apply reasoning to the data; and recommend or take actions to remediate. In minutes, the agentic AI can complete what used to take a site reliability engineer hours to pinpoint and troubleshoot potential issues. For security threats, agentic AI can analyze data streams to identify threats in real-time, including zero-day exploits or insider threats; automate multi-step investigation workflows from multiple security applications; and execute appropriate remediation responses to contain the threat and prevent lateral movements. Investigations that took the SOC analyst hours can now be done in minutes. 2. Preempt disruptions and downtime The power of agentic AI can prevent incidents and disruptions in more proactive ways. By studying historical data and current trends, agentic AI can forecast vulnerabilities -- such as unpatched software or weak encryption -- before they are exploited. It can detect subtle user behavior anomalies and flag suspicious activity before damage occurs. It can also analyze real-time data streams -- such as logs, metrics, and traces from multiple sources -- to provide a comprehensive view of system health and detect issues such as resource bottlenecks or latency spikes before they escalate. In short, the speed and scale at which root cause analysis can be done by agentic AI means more alerts can be analyzed -- and resolved -- before they become bigger issues. 3. Make better decisions with contextual, real-time insights Agentic AI has the ability to process new information in its environment and adapt its reasoning and course of action in real time. Contextual data refers to the rich, multidimensional information about users, devices, applications, and environments -- such as user behavior patterns, device states, network conditions, and data flows. Agentic AI can process contextual data and patterns to make rapid, informed decisions to detect and remediate incidents and optimize operational performance. 4. Upskill and optimize the workforce With agentic AI, you get both a natural language interface and automated task execution through the agency framework. Workers at all levels can use it to upskill their knowledge across domains, whether identifying security threat vectors or navigating complex application stacks in observability. Humans are ultimately responsible for managing AI agents. As more AI agents augment the work of analysts and managers, organizations will need technical analysts to learn new skills to manage agents and incorporate them into enterprise workflows (human-on-the-loop). Automating the full detection-investigation-response workflow is appealing -- but as workflows grow more complex, with multiple agents and steps, so does the risk of compounding errors and hallucinations. Inserting humans at critical points in the automated analysis workflow (human-in-the-loop) enables you to ensure the agent(s) is on the right track, provide real-time feedback and use reinforcement learning to improve model performance. 2. Avoid hallucinations with domain-specific, specialized agents There's a real cost to model hallucinations. This McKinsey AI Report estimates $67.4B was lost globally due to hallucinated AI output. OpenAI's o3 and o4-mini were shown to hallucinate between 51% and 79% of the time on reasoning tasks. Narrowing the agent's purpose -- combined with fine-tuning and augmenting the model with RAG using domain-specific data -- improves output accuracy. Specialized agents for areas like security and observability and even more targeted ones for detection, investigation, and response will deliver greater precision. These agents will also benefit from lower inference compute costs and latency compared to larger general-purpose LLMs. 3. Ensure seamless integration and compatibility in agentic ecosystems Integrating agentic AI into your IT environment requires rethinking of data flows, processes, and security protocols, and adapting user interaction models to maintain system integrity while harnessing AI's potential. Three emerging protocols will help accelerate this: 4. Agent access control and data privacy governance The volume and speed for agent access management will far exceed the traditional human access management. It's critical to define clear access levels for autonomous agents that maintain compliance, and establish a plan of record for audits and governance. The goal: boost operational efficiency without introducing risk so AI acts as a secure, augmentative force within the IT ecosystem. Splunk AI for digital resilience Splunk, a Cisco company, is redefining enterprise security and observability with AI at its core to accelerate insights, automate critical workflows, and boost analyst productivity. Building on a long history of machine learning capabilities, Splunk is embedding generative and agentic AI across its industry-leading security and observability solutions. With a unified data platform for operational data, Splunk is building an AI-ready platform to turbocharge enterprise security and observability outcomes. Visit www.splunk.com/ai to learn more. Cory Minton is Field CTO - AI at Splunk. Sancha Norris is Product Marketing Leader at Splunk AI.

How Agentic AI transforms enterprise automation

Agentic AI enables secure, adaptive automation within enterprise constraints There's a lot of noise in enterprise AI right now. Under mounting pressure to deliver faster, safer digital services, businesses are turning to the next evolution in automation: Agentic AI. No, this isn't bolting on a chatbot and calling it digital transformation. AI agents are built to understand your organization, operating within your domain constraints with real autonomy. These agents operate inside your business, using your data to automate decisions, adapt to real-world problems in milliseconds, and embed themselves directly into operational workflows. They blend the general reasoning power of today's large language models with domain- specific intelligence grounded in company data. That might be clinical records, compliance frameworks, or engineering logs - whatever your business runs on. The result? Systems that take action: surfacing insights, automating tasks, and adapting based on your company policies and workflows. Demand for automation is growing, as are expectations around compliance, transparency, and data governance, especially in Europe. Agentic AI offers a response to both: scalable intelligence, designed to work inside complex regulatory frameworks. That matters in sectors like healthcare, manufacturing, and financial services, where data security, explainability, and reliability aren't negotiable. These aren't markets where "good enough" is acceptable. Customers simply can not tolerate hallucinated responses or unreliable systems where their data hits the public domain. Agentic AI is safer. Not because it's slower or more cautious, but because it's built for the environment it's deployed into. Agentic systems rely on a layered approach, with different types of agents operating across an organization: Key to all of this is the use of custom vector databases. Vector databases enable AI agents to fetch relevant, security-controlled context from sensitive data without actually exposing that data in its original form to the agent. This is a game-changer for regulated industries. Rather than relying on generic training data from the public internet, this draws directly from the institutional knowledge inside your firewalls. That means better accuracy, stronger compliance, and fewer surprises. It also means outputs that reflect your standards, rather than what's statistically likely. Agentic systems are already transforming highly regulated sectors in Europe. In healthcare, they reduce administrative overheads, improve triage, and accelerate innovation while protecting patient privacy. In manufacturing, they're powering predictive maintenance, supply chain optimization, and real-time field service. Within finance, these agents enhance fraud detection, refine compliance, and provide hyper- personalized services. Agentic AI adoption is particularly strong in regions with tighter data controls - namely France, Germany, and the Nordics - because these systems respect the boundaries enterprises are required to operate within. These systems increasingly rely on serverless inference, which allows businesses to scale their AI infrastructure without wedding themselves to their maximum theoretical usage. That's critical in Europe, where innovation budgets are often tight, and sovereign infrastructure matters. Agentic AI is being built to meet those regulatory requirements from day one. Yes, Europe's regulatory environment slows things down. But that friction forces better thinking. It pushes enterprises to build with trust, accountability, and explainability. Creating market conditions where sustainable AI can thrive. GDPR, the EU AI Act, NIS2 and other regulatory frameworks define the standards by which responsible AI can scale. As US start-ups chase MVPs and launch before the proper guardrails are in place, European enterprises may end up with AI that's more compliant and generally more effective in the long term. Agentic AI marks a turning point in how businesses interact with their data and workflows. It moves beyond static automation to deliver systems that act, learn, and improve within the constraints enterprises define. This is not a plug-and-play future. It's a future that demands thoughtful design, domain- specific strategy, and an unflinching focus on outcomes. The rewards will be sustainable and significant for the organizations that build smart and scale responsibly. The hype in off-the-shelf, plug-and-play solutions will fade. Agentic AI infrastructure is built for the latest ways of working. Enterprises that invest now and build with intent will lead in the next stage for what's next. We've featured the best AI writer.

UiPath's agentic automation strategy - moving enterprise AI beyond the productivity plateau it's stuck on

The conversation at UiPath on Tour London 2025 made one thing clear - generative AI, while pervasive, has yet to deliver material value at scale. Gen AI is "everywhere except in the P&L," as McKinsey's Brendan Gaffey put it, pointing to an 80% adoption rate among enterprises but minimal measurable impact. The message from UiPath executives, customers, and partners was consistent in that agentic automation - where AI agents, robots, and humans collaborate across workflows - may offer the structural breakthrough needed to move beyond this paradox. Daniel Dines, CEO of UiPath, underscored this disconnect: Most of the PoCs (proof of concepts) that we are seeing today... they call it an agent, but in fact, it's just putting an LLM in the context of a very small task. He argued that the problem isn't intent or investment but orchestration. Enterprises aren't yet structuring AI deployments in ways that scale. UiPath CPO Graham Sheldon quantified the landscape. In a McKinsey study of 12,000 CIOs, 92% had initiated generative AI projects, yet few had realized a return on investment. Three themes kept surfacing - AI is often confined to narrow, fragmented tasks; organizations are still cautious about trusting autonomous agents in critical workflows; and legacy complexity - with an average of over 175 systems in place - makes integration challenging. Unlike generative AI, which typically reacts to user prompts, agents operate proactively. UiPath's platform now enables orchestration across agents, robots, and humans using Maestro, a new control interface designed for managing long-running, cross-departmental workflows. UiPath's AI-powered document understanding and extraction tool, uses Large Language Models to extract, classify and process data from complex documents including loan origination paperwork, brokerage statements, and patient referrals. In a demo of a mortgage loan origination process, Senior Director of Product Marketing Yanis Broustas showed how UiPath's BPMN-modeled (Business Process Model and Notation) agents and robots now handle document extraction, eligibility validation, task execution, and system updates - with humans looped in only when expert judgment is needed. The demo conveyed how the platform can make workflows look less like a handoff between systems and more like a coordinated digital team. This reflects a layered approach to automation - think of robots doing the repeatable work, agents handling the reasoning, and humans focusing on oversight and decisions that require trust. UiPath's approach centers on what Sheldon termed "controlled agency" - orchestration that allows organizations to deploy agents where they're confident in the outcomes while maintaining the ability to "dial up or dial back the amount of agency" based on performance and trust levels. Sundar Ganesh, Director of Global Process Automation at Barclays, shared the bank's journey from traditional automation to agentic readiness. The bank's Process Automation Center of Excellence (PACE) was established in 2019 to provide end-to-end delivery across Robotic Process Automation (RPA), Machine Learning, and workflow tools. Referring to both effort reduction and customer experience gains, Ganesh noted: We were able to operate on a minimum 30% benefit. Now, Barclays is working with UiPath to explore how agents can unify fragmented workflows, particularly in complex journeys like mortgages. The priority is integration without compromising governance, as Ganesh noted: The differentiator is not the infrastructure, it's the amount of automation we can deliver and deploy. Barclays' move to UiPath was based on alignment of platform roadmap and vision. But Ganesh emphasized their cloud-first, vendor-agnostic approach, noting that real transformation depends on trust, stakeholder engagement, and AI governance. The bank has developed an approach it calls "process parties" - a departure from traditional hackathons. As Ganesh explained: We have a period of deciding which one goes into the party, and then we have a couple of weeks to prepare... We work with all the teams involved, right from the governance to our operations and the tech guys... then we take three days to actually execute, and then we move it into production. What makes the difference? Ganesh: This is a real problem with real capabilities that we have. It's not an ideation, but to move things into production. Barclays' methodical approach to building automation capabilities while preparing for an agentic future reflects broader industry patterns. This progression from foundation-building to strategic transformation was central to McKinsey's analysis of how enterprises can move beyond the current AI adoption paradox. Gaffey made a broader case for agentic AI as a business model shift, not just a productivity tool. He explained that SaaS platforms address common, repeatable workflows but struggle with the "long, fat tail" of bespoke, cross-domain processes that define enterprise uniqueness. As he observed: Agentic hits a different value pool. By sitting above the business logic layer, agents can connect structured and unstructured data, trigger autonomous workflows, and enable proactive orchestration. This allows enterprises to move from productivity enhancements to strategic advantage. Gaffey described this as a clear progression that enterprises typically follow rather than discrete categories. The journey usually begins with basic individual support tools like copilots, which might improve efficiency by 10% to 20%, but the gains remain limited to personal productivity. The next stage involves more organized task and workflow automation, where discrete processes start seeing meaningful ROI in the range of 30%. Companies then advance to automating whole domains such as HR or finance, unlocking bigger benefits by consolidating across organizational silos. The transformative shift happens when automation spans the full customer or business journey. This is where orchestration tools and AI agents can make their most significant impact, moving beyond departmental efficiency to enterprise-wide transformation. The ultimate goal remains autonomous AI operators that act with oversight but minimal human intervention. While still aspirational, Gaffey emphasized that the trajectory toward this capability is clear. To get there, five themes kept surfacing in Gaffey's framing of what successful enterprises are doing: The potential for measurable impact is already emerging. Fiserv, a leading global FinTech, implemented UiPath's orchestrated approach to streamline merchant category code determination and validation - previously a painstaking manual process. What they used UiPath for was to orchestrate robots for simple data extraction and cross-checking information with web search, then deployed generative AI and agents to update and select the right codes. The validation rules they implemented delivered concrete results: 12,000 hours saved through these automations and 98% straight-through processing. The remaining two percent where agents weren't confident were handled by humans in the loop to check and update those results. The case for agentic automation extends beyond making today's workflows faster - it's about fundamentally reimagining how enterprises operate. As Dines emphasized: We delegate more and more some core aspects of our business... a mistake done by an agent can be extremely costly. This reality underscores why trust and orchestration were key points throughout the day as major challenges to adoption. Organizations like Barclays are building systematic approaches to agent deployment, moving from "process parties" that solve real problems with real capabilities to bank-wide programs that span entire customer journeys. ROI won't come from deploying another chatbot or automating isolated tasks. It will come from auditable, documented automation that spans systems, processes, and decisions while maintaining human oversight where judgment and trust matter most.

People prefer people! Tough learnings for agentic evangelists from Okta's annual customer study, with AI trust in short supply

A lot of the success or otherwise of agentic AI adoption is going to hinge on how the human beings that are engaging with the agents respond to that interaction. If we get a repeat of the 'I hate chatbots' mindset that kicked in very quickly when all too many companies hid their customer service and support ops behind an automated 'don't want to deal with you!' shield, then there's trouble ahead. For the kind of Digital Labor shared human/agent workforces envisioned by the likes of Salesforce CEO Marc Benioff to come to realisation, those of us engaging with the agent element need to be as comfortable as we are with dealing with the human intelligence. If we're still shouting 'give me a bloody human being' down the phone by default, then something's gone very badly wrong. Trust is going to play a large part here. That's something that agentic champions like Salesforce and Workday have been emphasising. The bad news, if a new study from Okta is to be believed, is that there's a lot of groundwork to be done before that trust is extended to agents. The annual Customer Identity Trends report is based on first party data from Okta's Auth0 platform, as well as consumer research conducted by Statista surveying a representative sample of 6,750 consumers in the Australia, Canada, France, Germany, India, Japan, the Netherlands, the UK and the US. It's a far reaching study that covers off more than just agentic AI, but the top line finding on that topic makes for tough, if hardly unexpected, reading for agentic advocates - people just prefer people. Across all countries and all demographics, 70% of respondents said they wanted to interact with a human being, not a "non-human identity (NHI). Only 16% said they'd rather deal with the NHI. Unsurprisingly perhaps, generational demographics pretty much fall into line with what you might expect. Baby Boomers definitely prefer to deal with humans. Some 83% of respondents in this category prefer humans compared to only two percent favoring AI agents. Nearly a quarter of all respondents (23%) say they don't want to use AI agents, of which Baby Boomers made up 42%, GenX 32%. Some 72% of those who are happy to call themselves 'Tech Avoiders' for the purpose of the research have the same view, as do 43% of those who claim to be 'Tech Traditionalists'. Over half of mainstream adopters don't trust AI agents with personal data(51%), just over 40% prefer to complete tasks manually, 40% concerned about their reliability, So why? Is it just fear of the unknown, the old FUD factor (fear, uncertainty and doubt)? (Some 22% of all respondents admit they don't know enough to understand an AI agent). Is it legitimate concern about privacy/trust among AI vendors? Or is it something new? The study suggests that users who prefer to deal with humans think that someone with a pulse is more likely to understand their needs (64%). They also find dealing with AI agents frustrating (38%), while 29% don't trust them. On the other hand, those who want to deal with agents rather than humans reckon they get faster resolutions to issues (55%). (A refreshingly candid 53% also admit they just want to avoid having to talk to other people, which may raise a whole series of other questions too wide for the purposes of this study...) As to what users are comfortable deploying agentic tech to handle, think dull, repetitive, tedious, rules-based stuff - language translation (38%), research/info gathering (34%), writing assistance (31%), data analysis (26%), creative tasks (25%), task automation (22%). The study notes: The four tasks for which users are most likely to employ an AI agent (language translation, research, writing assistance, and data analysis) share the common characteristics of being somewhat tedious and objective -- quite consistent with how computers have been traditionally utilized. Users are less likely to employ AI agents for subjective and personal tasks At the other end of the spectrum, users expressed comparatively little desire to have AI agents handle more personal responsibilities including creative tasks, personalized shopping recommendations, managing calendars/schedules, and providing decision-making support" But it all comes back to the trust issue. The number one concern cited is not trusting AI with personal data, with 60% reporting being either very concerned or concerned about AI's impact on the privacy and security of their digital identities. Indian respondents are most concerned (76%), followed by Canadians (67%), Americans (65%) and Brits (63%). And this lack of trust around personal data extends across age demographics - Baby Boomers 71%, GenX 62%, Millennials 58%, Gen Z 58%. The study states: By and large, users consider themselves unlikely to share personal information with a company's AI agent Even personal preferences (like a favorite color or sports team) -- the most innocuous of the nine options available -- are very likely or likely to be shared by only 39% of survey respondents All that said, over half (just - 51%) of total respondents do believe that AI is the future, so what can be done to alleviate these concerns? Greater oversight, transparency, and ethical guidelines would increase trust, with 38% of survey respondents wanting to know there is a human in the loop. This was the most frequent choice across 14 of the 18 demographic groups examined. In fact, Gen Z was the only demograhic not to have human oversight at the top of its preferences, citing ethical guidelines as more important. The report is an interesting read, although one which I found myself nodding along to in recognition such is the predictability of many of the responses and comments rather than being taken by surprise by any of them. That's not a criticism in itself - at this (very) early stage of the agentic revolution, it would be more surprising if it were otherwise, particularly given the widespread bad taste that so many of the older generation of badly-designed/badly-implemented chat bots has left in its wake. There's a big evangelism program that agentic vendors need to go on to explicitly demonstrate how agents differ from their 'ancestors' in this regard. And of course, as we repeatedly say, there's an urgent and ongoing need for agentic AI use cases to demonstrate the benefits of the technology and the learnings that others have picked up already. It will be interesting to do a compare and contrast in a year's time to see how far attitudes have mellowed/hardened - delete as applicable according to the tech industry's grasp of and execution against the 'mission entirely possible' that faces it.