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Clear out junk files and repair common Windows errorsFree Scan →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Enterprise AI agents are moving into real workflows, but adoption is uneven: some organizations report scaling them, while many are still experimenting. The harder work is making an agent reliable in a specific process—connecting it to usable data and systems, setting clear limits on its actions, and redesigning the work around it.
What counts as an AI agent at work?
In its 2025 survey, McKinsey describes agentic systems as foundation-model-based systems that can act in the real world and plan and execute multiple steps in a workflow. In practical terms, a conversational assistant responds to a prompt; an agent workflow can take a sequence of steps toward a goal, potentially using tools or systems along the way.
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That distinction does not mean every product marketed as an “agent” has the same autonomy or capability. For an enterprise, the useful question is what the system can actually do in the workflow: retrieve information, prepare a recommendation, update a record, or take another action—and where a person must review or approve it.
Are companies using AI agents in real workflows?
Survey results suggest movement beyond isolated experiments, but they describe different populations and measures. They are not a census of enterprises, and the figures below should not be combined into one adoption rate.
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| Source and population | Reported result | How to read it |
|---|---|---|
| McKinsey, 2025 global survey respondents | 23% said their organization was scaling an agentic AI system somewhere in the enterprise; 39% said it was experimenting with agents. | McKinsey defines scaling as expanding deployment and adoption in at least one business function. Most reported scaling was limited to one or two functions. McKinsey’s survey |
| Deloitte, 2026 report; survey of 3,235 business and IT leaders across 24 countries and six industries, conducted August–September 2025 | 21% of companies planning agentic AI deployment within two years reported mature agent governance. | This is a governance finding among organizations with deployment plans, not the share of all companies already using agents. Deloitte’s survey release |
| Anthropic and Material, 2026 report based on a late-2025 survey of more than 500 technical leaders | 57% reported using agents in multi-stage workflows; 16% reported using them across multiple teams. | The results describe the survey’s respondents across organization sizes and industries, not all enterprise buyers. Anthropic and Material’s report |
For context, McKinsey also found that 88% of respondents reported regular AI use in at least one business function. That figure covers AI broadly; it is not an agent-adoption measure. McKinsey’s survey
These findings support a measured conclusion: agents are being tried and, in some organizations, are part of expanding workflows. They do not establish that most companies have deployed agents broadly, or that reported use is producing reliable results.
How are companies using AI agents?
The reported applications cluster around information-heavy work and internal processes, but survey selections indicate interest or use—not a guaranteed best fit for every organization.
IT and knowledge work
McKinsey’s 2025 survey found agent use most commonly reported in IT and knowledge management, including service-desk management and deep research. These tasks can involve gathering information, applying rules, and routing or preparing work for a person.
Data analysis and reporting
In the Anthropic and Material 2026 report, 60% of respondents selected data analysis and report generation as a use case, while 65% of enterprise respondents selected data analysis and reporting as a high-impact use case. The measures have different respondent scopes and should not be treated as interchangeable. Anthropic and Material’s research insights
Internal process automation
In the same report, 48% selected internal process automation. That label can cover very different workflows; the value and risk depend on what actions are automated, which systems are involved, and how exceptions are handled. Anthropic and Material’s research insights
What makes an enterprise agent useful rather than just impressive?
A bounded workflow is a better starting point than a general mandate to “use agents.” Before selecting a platform or expanding autonomy, define the process, the people affected, the task or decision the system supports, and the result that would count as success.
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Connect it to usable data and systems
An agent can only work with information and actions it can access. Anthropic and Material’s 2026 survey found that 46% of respondents cited integration challenges and 42% cited data-quality requirements as scaling challenges. These are survey-reported obstacles, not universal rates. Anthropic and Material’s report
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In practice, map the systems the workflow depends on, check whether the data is current and consistent enough for the task, and restrict access to what the agent needs. McKinsey also emphasizes adapting infrastructure and productizing data as part of making AI work at scale. McKinsey’s survey
Make human review specific
“Human in the loop” is not a complete review plan. Decide which actions can happen automatically, which require approval, who handles exceptions, and who is accountable for the outcome. For example, an agent might prepare a service response for approval rather than send it itself; the appropriate boundary depends on the consequences of an error and the organization’s controls.
Build governance into operations
Deloitte’s finding that 21% of companies planning agentic deployment within two years reported mature agent governance points to a gap between plans and governance readiness. A useful operating model assigns owners for the business outcome, technical system, data, and risk controls, and sets out how actions are authorized, monitored, reviewed, and changed.
Redesign the work and measure the result
McKinsey argues that impact depends on embedding agents in core workflows and changing operating models, rather than scattering isolated pilots. Choose measures that fit the process—for example, quality, completion time, exception rate, operating cost, or user impact—and compare them with a clear baseline. These are practical measurement options, not reported survey results.
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Scale only after the workflow earns it
Expand to more users, tasks, or teams only after the initial workflow demonstrates acceptable performance and controls. Anthropic and Material’s late-2025 survey found a difference between reported use in multi-stage workflows (57%) and across multiple teams (16%); it illustrates that use within a workflow does not automatically mean organization-wide scaling. Anthropic and Material’s report
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What are the risks of AI agents in business?
The main operational risks follow from giving a system access to information and the ability to take actions: poor or outdated data can lead to poor outputs; weak integrations can break a process; overly broad permissions can expose information or trigger unintended changes; and unclear ownership can leave errors without an accountable response.
Change management is another practical challenge. In Anthropic and Material’s survey, 39% of respondents cited change-management needs as a scaling challenge. Introducing an agent can alter handoffs, review responsibilities, and staff expectations, so employees need to understand what the system does, when to intervene, and how to report failures. Anthropic and Material’s report
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How should a business compare agent approaches?
There is no evidence here for a universal vendor ranking. Compare options against the workflow and the controls it requires, not against the label “agent” alone.
- Workflow fit: Can the system complete the actual process, and what changes to roles or handoffs would be needed?
- Integration: Does it connect to the systems the workflow depends on, with a dependable way to handle failures?
- Data and access: Can it use sufficiently reliable data while respecting access boundaries?
- Action permissions: Can you set which actions are allowed automatically and which need human approval?
- Monitoring and auditability: Can the organization inspect what the agent did, identify errors, and review important decisions?
- Exceptions and reliability: What happens when information is missing, instructions conflict, or the process falls outside its usual path?
- Operating cost: What are the costs of running, integrating, supervising, and maintaining the workflow?
- Measured outcomes: Can the organization show improvement against an appropriate baseline without degrading quality or user experience?
What to take from the current evidence
The adoption figures show experimentation alongside some reported scaling, but they do not prove that enterprise agents are broadly mature or delivering consistent returns. The practical dividing line is whether an organization can make an agent part of a well-defined workflow—with usable data, bounded permissions, clear human accountability, and results that can be measured.
OpenAI Chief Economist Ronnie Chatterji wrote that “the next phase of enterprise AI will be shaped by stronger performance on economically valuable tasks, better understanding of organizational context, and a shift from asking models for outputs to delegating complex, multi-step workflows.” This is his view in OpenAI’s 2025 report, not an independent market finding. The report combines aggregated usage from OpenAI’s enterprise customers with a survey of 9,000 workers across almost 100 enterprises, so its results are examples from that ecosystem rather than market-wide estimates. OpenAI’s report
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