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Enterprises are extending generative AI toward systems that can work through multi-step tasks, retrieve information, use business tools and, in some cases, take bounded actions. That is an expansion of AI use—not evidence that companies have replaced generative AI or that reasoning AI has become a consistently measured market category.
The distinction matters: a model’s reasoning ability is only one part of a business system. Context, data retrieval, tool permissions, workflow orchestration, evaluation and human oversight shape what the system can safely accomplish.
What is reasoning AI, and how is it different from generative AI?
Generative AI produces content such as text, code or summaries, and it can also perform reasoning. “Reasoning AI” is not a consistently measured category in the enterprise reports discussed here, so it is more useful to describe the work a system does than to treat the term as a separate class of product.
A simple interaction might answer a question or draft a response. A more involved system can repeatedly reason through a task, retrieve relevant information, call a tool, check the result and continue. If permitted, it may also update a record or trigger a workflow. That pattern is often called agentic AI, but an agent need not be fully autonomous, and not every reasoning-capable model is an agent.
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As systems gain access to tools and business data, implementation choices become central: which information the system can access, which actions it may take, how results are checked, and when a person must approve or handle the work.
Are enterprises actually moving beyond generative AI?
The evidence points to deeper use and growing experimentation with agents, not a clean transition away from generative AI. The available figures measure different populations and activities, so they should not be combined into a single adoption curve.
| Finding | What it measures | How to read it |
|---|---|---|
| ChatGPT message volume grew 8× year over year | OpenAI business-customer usage reported in 2025 | Evidence of increased activity within OpenAI’s customer base, not a market-wide adoption rate. OpenAI, The State of Enterprise AI |
| API reasoning-token consumption per organization increased 320× year over year | OpenAI business-customer usage reported in 2025 | A company-specific usage measure; it does not establish how many enterprises adopted reasoning models. OpenAI, The State of Enterprise AI |
| 36% were piloting or using agentic AI | Capgemini Research Institute survey of 1,505 organizations, fielded in November 2025 and reported in 2026 | Combines pilots and use; it is not equivalent to scaled production deployment. Capgemini Research Institute |
| 82% used generative AI at least weekly | Respondents to the 2025 Wharton Human-AI Research and GBK Collective survey | Describes generative AI use, not adoption of reasoning AI. Wharton and GBK Collective |
| Average activated agents per organization rose from 5 in February 2025 to 13 by April 2026 | Salesforce platform activity reported in 2026 | Measures activity on Salesforce’s platform, not a representative estimate for all companies. Salesforce, Agentic Enterprise Index |
These reports do not measure the same thing: one tracks usage by a vendor’s customers, another surveys organizations about agentic AI, another asks about generative AI use, and Salesforce reports activity on its platform. None alone establishes that enterprises generally have moved beyond generative AI.
What can AI agents do in a business workflow?
OpenAI’s enterprise report names several areas where organizations use AI. These are reported categories, not a claim that every company has deployed an agent in each one.
- Assistants and search: Answer questions or help employees find information.
- Workflow automation: Coordinate steps across a process, potentially using tools to complete bounded tasks.
- Coding and developer tools: Support software development work.
- Customer support: Assist with service interactions and related tasks.
- Data analysis: Help examine or interpret data.
- Summarization and extraction: Condense information or pull structured details from documents and other content.
The practical difference between generating a response and acting within a workflow is consequential. An inaccurate draft can be reviewed before use; an incorrect record update or workflow action may have immediate effects. The more consequential the task, the more important it is to limit access and define review points.
How should a company evaluate reasoning models and agents?
Evaluate the system on the job it is meant to do, not only on a general model ranking. The KAMI paper describes an enterprise-oriented benchmark built from 170,000 test items across 35 model configurations. In that evaluation, traditional benchmark rankings poorly predicted practical agentic performance. That is a finding about the paper’s benchmark and configurations, not proof that every public benchmark is unhelpful.
A practical evaluation begins with representative tasks and the cost of failure. Test the complete workflow, including the model, retrieved context, tools, permissions and human review—not just a model in isolation.
- Task success and correctness: Does it complete representative work accurately, including unusual but realistic cases?
- Reliability across runs: Does it produce dependable outcomes when the same task is repeated?
- Tool-use accuracy: Does it select appropriate tools, provide correct inputs and verify the results?
- Latency and total cost: How long does the workflow take, and what does it cost at the expected volume?
- Privacy and security: What information can the system access, and how is that access controlled?
- Permission boundaries: Can it only perform actions appropriate to its role, with safeguards around sensitive changes?
- Observability and escalation: Can teams review what happened, and does the system route uncertain or consequential cases to a person?
These are practical procurement criteria, not a universal standard. Andreessen Horowitz’s 2025 report, based on a survey of 100 CIOs across 15 industries plus buyer interviews, describes organizations mixing models to balance performance and cost and applying more rigorous evaluation. Its sample is useful context for buyer concerns, not a census of enterprise practice. Read the report.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What are the risks of letting AI agents take action?
Giving a system tools can turn a flawed answer into a flawed action. The appropriate boundary depends on the workflow and the consequences of an error; an agent should not receive broad permissions simply because it can use a tool.
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- Limit access: Provide only the data and tools needed for the assigned task.
- Bound actions: Define which changes the system may make and which require approval.
- Validate outputs: Check important results before they affect customers, finances, records or operations.
- Keep an audit trail: Log actions and relevant tool use so teams can investigate failures.
- Escalate uncertainty: Route ambiguous or high-impact cases to a human reviewer.
These controls should be designed into the workflow, not treated as properties guaranteed by the model. More autonomy increases the importance of clear identities, permissions, validation and oversight.
What the current adoption evidence can—and cannot—show
The sources indicate increasing enterprise activity around generative AI and agentic systems, but their measures are not directly comparable. OpenAI’s figures describe its business customers; Capgemini reports survey responses from organizations; Wharton and GBK Collective report a survey about generative AI; Salesforce reports activity on its platform. Agentic AI adoption also does not isolate which model capabilities enabled a deployment.
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Reported use and platform activity do not by themselves establish causal productivity gains or financial returns. The figures cited here do not provide an independently comparable ROI measure attributable specifically to reasoning models. Nor do they establish a universal definition or threshold for when an enterprise has “moved beyond” generative AI.
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