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AI agents can plan, use tools and carry out multi-step work, but that does not make them owners of a company’s strategy. They can help leaders analyze choices and execute bounded workflows; people must still set the organization’s purpose, decide which risks are acceptable, resolve competing priorities and remain accountable for the consequences.
That distinction matters as adoption accelerates. A 2025 World Economic Forum and Capgemini report says 82% of executives plan to adopt AI agents within the next one to three years, while warning that oversight is not keeping pace with experimentation. The practical question for a company is not simply whether an agent can act. It is which actions it may take, under what limits, and who must intervene when the situation no longer fits the plan.
What are AI agents, and how are they different from chatbots?
A chatbot generally responds to a person’s prompt with information or generated content. An AI agent can be given a goal and use tools to work through multiple steps toward it. Depending on its design and permissions, it may plan tasks, retrieve or update information, call software, and continue acting without a person directing every move.
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The distinction is about the system’s role and authority, not a guarantee of intelligence or reliability. An agent can still misunderstand a goal, use poor information, or fail when circumstances change. Giving a model access to tools can turn a weak answer into an action with consequences; it does not make the underlying judgment sound.
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A Google Research systematic review describes agentic systems as capable of autonomous, goal-directed reasoning, planning and execution. The review also finds that memory, adaptability, governance and security remain areas of limited maturity, and that human-in-the-loop practices are implemented inconsistently. In other words, the capability to act is advancing faster than the evidence that every system can act safely across changing situations.
Can AI agents make strategic decisions for a company?
Agents can support strategic work, but they should not be treated as the authority that decides what the company ought to pursue. They optimize objectives people delegate; they do not independently own the organization’s mission, values, risk appetite, legitimacy or responsibilities to stakeholders.
That boundary is visible in the work itself. An agent may compare scenarios, surface assumptions, monitor indicators, draft alternatives or execute an approved process. A human leader must determine whether the objective is worth pursuing, which interests take priority, what constraints cannot be traded away, and whether the evidence is strong enough to act.
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- Expand analysis: organize available inputs, generate options and expose assumptions for review.
- Test a stated plan: help examine how a choice might perform under specified conditions, while making clear what evidence and assumptions the analysis relies on.
- Monitor bounded signals: flag changes against criteria a person has selected.
- Carry out approved work: complete defined steps within its permissions, then report what it did and where it encountered uncertainty.
What leaders must retain
- Purpose: choose the organization’s direction and the objective the system is meant to serve.
- Trade-offs: decide how to weigh competing interests, including costs that cannot be reduced to a single metric.
- Risk limits: determine which actions are unacceptable even if they could advance a target.
- Accountability: own the decisions, permissions and operating conditions under which the agent acts.
This is not a claim that every human judgment is better than every machine output. It is a distinction between analysis or delegated execution and the authority to choose ends, accept consequences and answer to affected people.
What does the evidence say about agents improving strategic work?
The evidence supports a conditional benefit, not a general promise that adding an agent improves strategy. In a 2025 randomized experiment published in Strategy Science by INFORMS, 976 managers worked with a theory-based agentic system. Experienced managers improved their strategic work with the system, while some highly educated participants became more confident without corresponding gains in the quality of their work.
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The contrast points to user-system-problem fit. An agent’s usefulness depends on the task, the way the system is designed and the user’s ability to judge its output. Confidence is not a substitute for quality: a polished recommendation can feel persuasive without being more accurate or appropriate.
For a company evaluating an agent, this means testing the actual work and users rather than assuming a general-purpose capability will transfer to every strategic decision. A system that helps an experienced manager structure an analysis may not be suitable for making or approving that decision on the manager’s behalf. Measure the resulting work against business-relevant criteria, and check whether people can recognize weak output rather than merely accept it.
How much autonomy should an enterprise give an AI agent?
Give an agent only the authority its task, evaluation and recovery arrangements can support. Autonomy is not a single setting: it is a set of choices about what the agent may access, what it may change, when it must ask for approval and how its actions can be inspected or reversed.
A useful starting point is to separate low-consequence, bounded work from decisions that have high impact, are difficult to reverse or depend on contested values. The more uncertain the context and the harder the action is to undo, the stronger the case for a human approval gate. Expand authority only when the system performs reliably in the relevant conditions and the organization can detect and recover from failure.
| Decision factor | Question to answer | What it means for autonomy |
|---|---|---|
| Action and approval | Which actions can happen without a person, and where are approval gates required? | Keep approval for high-impact exceptions and actions that should not proceed on delegated authority. |
| Task boundaries | How clearly can the task, inputs, tools and acceptable outcomes be specified? | Broader or less predictable work calls for narrower permissions and more human review. |
| Changing conditions | Can the agent recognize when the situation falls outside its expected context? | Require it to pause, surface uncertainty or escalate rather than improvise beyond its mandate. |
| Evaluation | Are quality, calibration, robustness and recovery measured against the intended business outcome? | Do not expand authority based only on speed, fluent output or user confidence. |
| Reversibility | Can an action be undone, and can its effects be contained? | Prefer tighter approval for irreversible actions or those whose effects could spread. |
| Interaction with other agents | Can another system or organization’s agent act on, amplify or respond to this agent? | Assess the combined workflow, not just each agent in isolation. |
For high-consequence choices, a human-on-the-loop arrangement is often more appropriate than unreviewed autonomy: the agent carries out bounded work while a person monitors performance, handles exceptions and retains authority to stop or change the process. A human-in-the-loop arrangement instead requires a person to approve a specified action before it proceeds. The correct placement depends on the consequences of delay as well as the consequences of a mistake.
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Who is accountable when an autonomous agent makes a mistake?
The organization cannot hand accountability to the agent. A company chooses the objective, access, operating boundaries and oversight arrangement; its leaders and designated owners must therefore make responsibility for those choices explicit. The relevant question is not only what the model did, but who authorized the workflow and who was responsible for monitoring and responding to it.
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Before deployment, assign named roles for the following responsibilities:
- Objective owner: defines the delegated goal and confirms that it serves an approved business purpose.
- Permission owner: grants and reviews access to data, tools and actions.
- Decision approver: handles actions or exceptions that require human authorization.
- Operational monitor: reviews performance, alerts and unusual behavior.
- Incident lead: can pause the workflow, coordinate investigation and authorize recovery.
These roles may sit with different people, but they should not be left implicit. If a system makes a mistake, a useful audit trail should help establish what goal it was given, what information and tools it used, what action it took, and whether an approval or escalation step worked as intended.
Why can a multi-agent system be riskier than one agent?
Because interactions can create hazards that do not appear when each agent is evaluated alone. The Australian AI Safety Institute warns that individually safe and reliable agents do not necessarily form a safe and reliable system. Errors can cascade and amplify, agents may converge on collusive behavior, and interactions across organizational boundaries can weaken any one company’s control.
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That changes the scope of governance. A company should map not only the permissions of each agent, but also the handoffs, shared data, incentives and downstream actions in the full workflow. A system may behave differently when one agent’s output becomes another agent’s instruction or when several systems respond to the same signal.
Controls for connected workflows
- Identity and permissions: make clear which agent is acting and restrict each identity to the data and tools it needs.
- Tool boundaries: define which actions are allowed, which are prohibited and which need explicit approval.
- Monitoring and escalation: identify conditions that require a pause or human review, including behavior outside the task’s defined limits.
- Independent evaluation: assess the combined workflow, including how one agent’s errors affect other agents and people.
- Rollback and incident response: plan how to stop or reverse actions where possible and who coordinates a response when containment is needed.
- External coordination: agree how incidents, changes and responsibilities are handled when a workflow depends on agents operated by other organizations.
These are operating-model controls, not a final compliance step. The Google Research review’s finding that human oversight mechanisms are inconsistently implemented reinforces the need to make decision rights and escalation paths part of system design.
How should a company introduce agents into strategic work?
Start with a defined workflow and an explicit human decision right, rather than a broad instruction to “use AI for strategy.” The aim is to establish where the system adds value, how its work will be judged and what happens when it encounters uncertainty before granting it authority to act.
- Choose a bounded task. Specify the work the agent may perform, the intended outcome and the situations that are outside scope.
- Name the decision owner. Identify the person accountable for the objective and the person who approves any consequential action.
- Set access and action limits. Grant only the data and tools needed for the task. Separate analysis or recommendations from permission to commit the organization to an action.
- Define stop and escalation conditions. State when the agent must pause, ask for review or report that it cannot proceed safely within its authority.
- Evaluate real work. Test output quality, calibration, robustness and recovery against the intended business outcome. Include the users who will judge or act on the output.
- Review the whole system. Check handoffs to people, software and other agents, including where errors could propagate or authority could become unclear.
- Expand cautiously. Increase autonomy only when the evidence supports it and monitoring, audit and recovery remain adequate for the new scope.
A useful test is whether the company can answer, before a workflow runs: What goal is the agent pursuing? What can it do without approval? What will make it stop? Who can intervene? How will the organization know whether the result was good? If these answers are unclear, the system is not ready for broader authority.
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The strongest evidence here points to augmentation in defined conditions, not a general replacement of strategists. Agents can help expand analysis and carry out approved work, but strategy also involves choosing goals, navigating trade-offs and accepting responsibility—functions that remain with human decision-makers.
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The 976-manager experiment is a useful caution against treating augmentation as automatic: experienced managers benefited from a theory-based agentic system, while some highly educated participants gained confidence without a corresponding improvement in quality. Companies should therefore judge whether a particular agent helps particular users perform a particular task, rather than infer strategic competence from fluency or the ability to execute multiple steps.
The organizational adjustment is broader than adding a new tool. As the MIT Sloan Management Review and Boston Consulting Group study’s “tool” and “teammate” framing suggests, teams need a clear account of where agent work fits, who reviews it and who owns its consequences. The leader’s role does not disappear when execution becomes more automated; the need to set direction and retain decision rights becomes more visible.
Frequently Asked Questions
What is the difference between a human-in-the-loop and a human-on-the-loop AI agent?
In a human-in-the-loop process, a person reviews or approves a specified action before it happens. In a human-on-the-loop process, the agent can perform bounded work while a person monitors it and can intervene; the choice depends on the consequences of both mistakes and delay.
Can AI agents work with confidential company data?
Whether a particular agent can safely handle confidential information depends on its data access, tool permissions and the organization’s controls. The evidence summarized here does not establish a universal rule for data handling, so companies should assess the actual system and access arrangement rather than assume that agent capability guarantees confidentiality.
What should a board ask before management deploys autonomous agents?
Ask who owns the delegated objective, which decisions remain reserved for people, how performance and incidents will be reported, and whether the organization can pause or reverse consequential actions. For connected systems, ask how risks are assessed across agents and organizational boundaries, not only within each component.
Why might an AI agent make a manager more confident without improving a decision?
A system can make analysis feel more complete or persuasive without improving its underlying quality. The 2025 manager experiment reported this pattern for some highly educated participants, which is why evaluation should measure the work itself rather than confidence alone.
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