Tag: agentic AI

  • 2026 Work Trend Index by Microsoft

    2026 Work Trend Index by Microsoft

    About the paper

    Microsoft’s 2026 Work Trend Index Annual Report examines how A.I. agents are changing work, arguing that as agents take on more execution, human agency shifts towards intent-setting, judgement, orchestration and accountability.

    It is a mixed-methods report based on Microsoft 365 telemetry, a 20,000-person online survey of A.I.-using knowledge workers across 10 markets, a separate 1,800-person global survey on managers and agentic A.I., and expert perspectives; the main survey covers Australia, Brazil, France, Germany, India, Italy, Japan, the Netherlands, the UK and the US.

    Length: 29 pages

    More information / download:
    https://www.microsoft.com/en-us/worklab/work-trend-index/agents-human-agency-and-the-opportunity-for-every-organization

    Core Insights

    1. What is the central argument of the report?

    The report’s central argument is that AI and agents are not merely productivity tools; they are forcing a redesign of the operating model of work. Microsoft frames this as a shift in which agents take on more execution, while humans gain more “agency”: more capacity to define intent, direct work, exercise judgement and own outcomes.

    The report is explicit that the key question is no longer simply whether organisations are adopting AI. The larger question is whether they are structurally capable of capturing its value. Its thesis is that many workers are already using AI in advanced and resourceful ways, but their organisations have not yet redesigned the surrounding systems — leadership alignment, incentives, governance, performance evaluation, culture and management practices — to match what AI now makes possible.

    A useful way to express the report’s logic is this: AI raises individual capability, but organisational design determines whether that capability becomes institutional advantage. The report repeatedly contrasts “AI adoption” with “AI absorption”. Adoption means people or teams use tools. Absorption means the organisation changes how work is designed, evaluated, governed, learned from and scaled.

    This is why the report’s three-part structure matters. At the employee level, AI expands what individuals can do. At the leader level, the task becomes rearchitecting work rather than simply deploying tools. At the organisational level, the most advanced firms become “Learning Systems” that capture lessons from AI-enabled work and turn them into repeatable practices.

    2. How does AI change the role and value of individual employees?

    The report argues that AI lifts the ceiling on individual potential by helping people do more complex, higher-value work. In Microsoft’s privacy-preserving analysis of more than 100,000 Microsoft 365 Copilot chats, 49% of classified conversations supported cognitive work such as analysing information, solving problems, evaluating and thinking creatively. The rest were split between working with people, finding information and producing work.

    Survey findings reinforce this. According to the report, 66% of AI users say AI has allowed them to spend more time on high-value work, and 58% say they are producing work they could not have produced a year earlier. Among “Frontier Professionals” — the most advanced AI users in the research — that latter figure rises to 80%.

    However, the report does not present this as simple automation. Its stronger claim is that human value moves. As AI takes on more execution, the employee’s differentiating contribution becomes judgement, quality control, critical thinking, intent-setting and the design of the human-AI workflow. The report says AI users themselves recognise this: 50% name quality control of AI output as an increasingly important human skill, while 46% name critical thinking. A striking 86% say they treat AI output as a starting point, not a final answer, and that they remain responsible for the thinking.

    The report’s page 9 framework is particularly useful. It describes four modes of working with AI: asking, delegation, collaboration and exploration. The key point is not that one mode is superior. The most advanced users know which mode a task requires. Quick factual queries may be “asking”; recurring reports or structured summaries may be “delegation”; proposals and judgement-heavy communication may require “collaboration”; and unfamiliar workflows may call for “exploration”. This is an important conceptual distinction because it moves the discussion beyond “prompting” towards work design.

    3. What is the “Transformation Paradox” identified by the report?

    The “Transformation Paradox” is the report’s term for the gap between workers’ AI readiness and organisations’ ability to support it. Microsoft maps respondents across two dimensions: individual AI capability and organisational readiness. The result is a five-zone model.

    Only 19% of AI users fall into the “Frontier” zone, where both individual capability and organisational readiness are high. Another 10% are in “blocked agency”: they have strong individual AI capability, but their organisations are not ready to support or absorb it. A further 5% represent “unclaimed capacity”, where the organisation is more ready than the individual. Sixteen per cent are “stalled”, with both low individual capability and low organisational readiness. The largest group, 50%, sits in the “emergent” zone, where both individual practice and organisational conditions are still forming.

    The paradox is that the pressure to use AI is rising, but the organisational systems still reward old ways of working. The report says 65% of AI users fear falling behind if they do not adapt quickly with AI, yet 45% say it feels safer to focus on current goals than to redesign work with AI. Only 13% say they are rewarded for reinventing work with AI even when results are not immediately achieved.

    This is one of the report’s most important findings because it reframes AI transformation as a management and systems problem. The obstacle is not simply lack of tools or lack of individual skill. It is the mismatch between what employees are capable of doing and what their organisations measure, encourage, permit and reward.

    4. What role does leadership play in turning AI use into organisational value?

    The report places substantial responsibility on leaders and managers. It argues that the job of every leader is now to “rearchitect work” — not just introduce AI tools, but redesign workflows, roles, incentives, metrics, governance and expectations around what humans and agents should each do.

    Leadership alignment appears to be weak. Only 26% of AI users say their leadership is clearly and consistently aligned on AI. The report also finds a perception gap between leaders and employees: leaders are more likely than employees to say AI-driven reinvention feels safe and rewarded. That suggests that senior leaders may believe the organisation is more supportive of AI transformation than employees experience in practice.

    Managers are presented as especially important because they translate strategy into everyday behaviour. A separate Microsoft-led study of 1,800 employees globally found that when managers actively model AI use, employees report a 17-point lift in AI value, a 22-point lift in critical thinking about AI use and a 30-point lift in trust in agentic AI. When managers create psychological safety around experimentation, employees report higher AI readiness and value, and are more likely to be high-frequency users of agentic AI.

    The report also shows that Frontier Professionals tend to work in stronger managerial environments. Compared with non-Frontier Professionals, they are more likely to say their manager openly uses AI, sets quality standards for AI-assisted work, creates space for experimentation and encourages more ambitious work redesign. This supports the report’s broader argument: individual AI skill matters, but it compounds only when leaders create the right organisational conditions.

    5. What does the report mean by saying every firm must become a “Learning System”?

    The report uses “Learning System” to describe an organisation that captures what AI-enabled work is teaching it and turns those insights into shared, repeatable and improving practices. This is the report’s organisational endgame: the firms that win are not simply those with the most AI tools, but those that learn fastest from their own work.

    The evidence behind this claim comes from Microsoft’s AI Impact Analysis. The report finds that organisational factors — culture, manager support and talent practices — account for more than twice the reported AI impact of individual mindset and behaviour: 67% versus 32%. The top single factor is organisational AI culture, followed by talent practices and manager support. The methodology is careful to state that these are statistical associations, not causal effects, because the variables are self-reported at the same moment.

    The report argues that as agents become more active, they generate valuable signals: what worked, what failed, where quality drifted, which hand-offs broke down and where workflows need redesign. In weaker organisations, these signals stay local. In stronger ones, they are captured, shared and encoded into routines. This is what Microsoft calls “Owned Intelligence”: institutional know-how that compounds over time, is specific to the firm and is difficult for competitors to copy.

    The report identifies three questions every advanced organisation must answer:

    1. who reviews agent performance
    2. who has authority to update agent workflows
    3. and how local wins are captured and scaled.

    It also argues that this requires coordination across four roles: employees who redesign their work around intent and review; leaders who redesign processes around outcomes and agent autonomy; IT teams that manage agents as entities with identities, permissions and lifecycle controls; and security teams that build monitoring, auditability and policy enforcement into the system.

    The implication is clear: AI transformation is not finished when employees start using AI. It becomes strategically meaningful only when the organisation builds the infrastructure to learn from AI-enabled work, codify that learning and continuously improve how humans and agents work together.

  • The Wharton Blueprint for A.I. Agent Adoption by Wharton

    The Wharton Blueprint for A.I. Agent Adoption by Wharton

    About the paper

    The report is a mixed-methods synthesis rather than a single original study: it combines recent academic research on human-AI interaction, practitioner perspectives from executives at firms deploying AI agents, and recommendations from Wharton faculty.

    It does not present one unified sample or fieldwork design; instead, it draws on multiple experiments, surveys, working papers, and case-informed expert contributions, with sample sizes and geographies varying by cited study and not clearly specified at the report level.

    Length: 47 pages

    More information / download:
    https://knowledge.wharton.upenn.edu/special-report/wharton-blueprint-ai-agent-adoption/

    Core Insights

    1. What is the central argument of the report?

    The report’s core argument is that AI agent adoption is no longer mainly constrained by technology, but by psychology. The authors argue that many people are still unwilling to let AI agents perform meaningful tasks on their behalf, not because the systems cannot do anything useful, but because users hesitate to believe that the agent is competent, to trust it, and to hand over control. The report therefore frames adoption as a behavioural and design challenge rather than a purely technical one.

    It organises this adoption problem into three “psychological frictions”:

    • perceived competence
    • trust
    • and delegation of control.

    These are presented as the three core barriers that developers and organisations must overcome if they want broader acceptance of AI agents. In other words, the report is not asking only whether agents work; it is asking what makes people willing to let them work on their behalf in real settings.

    The report is also quite explicit that this is a practical blueprint. It is designed for people building or deploying AI agents and aims to translate behavioural science into design recommendations. That makes its purpose strongly applied: it is less about theorising AI adoption in the abstract and more about showing how organisations can reduce resistance and increase real-world uptake.

    2. How does the report explain the first friction, perceived competence, and what makes users believe an AI agent can do the job?

    The first friction is perceived competence, which the report defines as the user’s subjective belief in the agent’s ability to perform desired actions. The key point here is that perceived competence is not the same as technical capability. An agent may be highly capable in a technical sense, but if users do not experience it as capable, they will still hesitate to adopt it.

    The report argues that users prefer agents that appear competent rather than warm. Across cited experiments, people were less willing to use AI that sounded cheerful or friendly than AI that signalled expertise, consistency, and reasoning. Competence cues included explaining criteria, showing reasoning, and making recommendations in a way that felt rigorous rather than overly personable. In practice, the report suggests that in serious domains such as health, finance, law, and professional work, a warm personality can actually undermine adoption if it makes the system seem less capable.

    Another major idea is that people judge whether the agent adds value. In the report’s discussion of AI-enabled travel agents, four factors shaped that perception:

    • convenience
    • personalisation
    • ubiquity
    • and superior functionality.

    At the same time, benefits alone are not enough. Privacy concerns, technology anxiety, and the desire for human interaction can still depress adoption even when users recognise obvious advantages. That means perceived competence depends both on positive cues about usefulness and on reducing perceived reasons not to engage.

    The report also places strong weight on explanations. In high-stakes contexts, users felt AI was more reliable and safer when it explained its process in detail, including the steps taken, data considered, or method used. Explanations therefore function not only as transparency, but as a signal of seriousness and quality. Finally, the report argues that agents can borrow competence from humans: when AI is presented as supporting a credible human expert rather than acting as an equal or rival, resistance falls and competence rises. This is one of the clearest examples of the report’s broader logic that adoption depends heavily on perception and framing.

    3. What does the report say about trust, and which factors most strongly increase or weaken trust in AI agents?

    The second friction is trust, defined as the user’s willingness to rely on the AI agent despite uncertainty. The report presents trust as central because AI agents are not just offering information; they are potentially taking action. That raises the stakes, since users must feel confident not only in what the system says but in what it might do.

    One of the strongest findings is that trust improves when users understand the agent’s limitations. The report cites experiments showing that people trusted AI more, and worked more effectively with it, when they were explicitly told where it was likely to fail. Rather than undermining confidence, acknowledging weaknesses helped users feel that they understood the system’s boundaries and therefore knew when to rely on it and when to be cautious. This is an important insight because it runs against the instinct to present AI as broadly capable and seamless.

    The report also argues that proof of successful outcomes often matters more than technical explanations. Users were more persuaded by evidence that the agent had successfully performed similar tasks before than by detailed accounts of how the system worked internally. The implication is that many users do not primarily want interpretability in a technical sense; they want reassurance that the system delivers results. Similarly, trust rises when agents reduce uncertainty before, during, and after use by making goals explicit, showing steps as they happen, and demonstrating how feedback improves future actions.

    Other trust-building mechanisms in the report include making the agent seem as though it understands the user’s goals, labelling it as “learning” or “improving,” using precise rather than rounded numbers, and tailoring outputs to specific user criteria rather than generic averages. The report repeatedly warns against the opposite pattern: trust falls when AI feels generic, when it seems to optimise for “most users,” or when it makes the process so effortless that users feel detached from the outcome. That last point is especially interesting, because the report suggests that convenience alone does not guarantee trust; too much automation can reduce psychological ownership and make people less willing to accept the result.

    4. How does the report understand delegation of control, and what level of autonomy does it recommend?

    The third friction is delegation of control, which the report defines as the user’s willingness to grant the AI the autonomy required to act on their behalf. This is where adoption becomes most sensitive, because an AI agent can move from being a helpful assistant to something that feels intrusive or disempowering.

    The report’s clearest conclusion is that people prefer a moderate level of autonomy. Too little autonomy makes the agent feel burdensome and not worth using, because the human has to micromanage it. Too much autonomy makes people feel that their freedom and control are being taken away. The recommended design pattern is therefore “human in the loop”: the agent should do meaningful preparatory or analytical work, but final decisions or key approvals should remain visible and accessible to the user.

    Control is not just about actual permissions; it is also about felt control. The report highlights research showing that concerns about control account for a substantial share of people’s decision whether to adopt AI. It therefore recommends making edit, pause, stop, reverse, and review options highly visible. Users need to know not only that controls exist, but where they sit and how easily they can be used. This is consistent with the report’s wider emphasis on transparency, checkpoints, and reversibility.

    The report adds two further nuances. First, adoption increases when users feel the agent is “theirs,” for example through naming, setup choices, or preferences, because ownership increases willingness to invest attention and effort. Second, the report notes that people rely more on AI under pressure, because repeated evaluation is cognitively costly. Yet it does not recommend manufacturing urgency; instead, it suggests using real workflow pressures to make AI the easier default. The overall message is that delegation works best when autonomy is earned gradually, bounded clearly, and embedded in a structure where the human still feels like the principal actor.

    5. What are the report’s wider implications for organisations, and what limitations does it acknowledge?

    For organisations, the report implies that successful AI agent deployment depends as much on behavioural design, workflow design, and communication as on model performance. The most important practical lesson is that adoption is unlikely to follow automatically from capability. Even if agents can perform useful tasks, users may resist them unless systems are positioned as competent, transparent, bounded, and supportive rather than all-powerful or frictionless. In organisational terms, this means deployment strategies must address psychology deliberately.

    The report’s recommendations point towards a fairly specific organisational philosophy. Agents should be framed as assistants, not authorities; they should explain their reasoning in business terms; they should show users what they are doing before, during, and after action; and they should operate within structures resembling job descriptions, permissions, escalation paths, and measurable outcomes. This effectively treats AI agents less like magical automation and more like junior or specialist collaborators who require oversight, governance, and staged trust-building.

    At the same time, the report is careful to acknowledge limitations. It explicitly states that this is based on the current best scientific evidence in a rapidly evolving field, and that many insights are extrapolated from adjacent AI and organisational behaviour research because AI agents themselves are still relatively novel. The report also notes limitations study by study: many cited findings come from experiments, hypothetical scenarios, specific sectors such as travel, healthcare, e-commerce, or financial advice, or controlled lab environments rather than long-term real-world deployments. That means the blueprint is evidence-informed, but not final or universally settled.

    So the deeper implication is twofold. First, organisations should not wait for perfect certainty before designing for adoption, because the report offers a strong behavioural framework already. Second, they should treat these recommendations as a living playbook, to be tested, adapted, and updated as both the science and the technology mature. That combination of confidence and caution is one of the report’s defining features.