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Operational efficiency is the clearest common business driver for enterprises adopting AI agents. Organizations expect agents to complete work faster, raise productivity or capacity, and reduce manual effort. Cost savings and better decisions are closely related goals, while customer experience, competitive advantage, growth, and innovation can also matter. The evidence does not show one motive shared by every enterprise—and expected gains are not the same as proven financial results.
What business value do enterprises expect from AI agents?
Survey findings point to a cluster of related objectives rather than a single universal payoff. In a May 2025 survey of 300 US senior executives, PwC found that 88% planned to increase AI-related budgets over the following 12 months because of agentic AI. Among companies already adopting agents, respondents reported measurable productivity value (66%), cost savings (57%), faster decision-making (55%), and improved customer experience (54%). These are self-reported outcomes, not independently audited proof that agents caused the results. PwC’s US AI Agent Survey
A 2026 Anthropic/Material survey asked enterprise respondents about what they expected agents to deliver over the next 12 months. The most common expectations included higher quality or accuracy (43%), increased efficiency or faster task completion (42%), cost savings or lower operating expenses (40%), and improved employee productivity or capacity (39%). These figures describe anticipated benefits, not realized results. Anthropic and Material’s 2026 report
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11IBM’s June 2025 summary of research by the IBM Institute for Business Value highlights decision-making and automation as prominent benefits: 69% of executives cited improved decision-making, and 67% cited cost reduction through automation. IBM describes two underlying surveys: one spanning 2,500 executives across 18 industries and 19 regions, and another covering 400 C-suite executives across 15 roles, 11 industries, and six countries. The populations and question wording differ from the PwC and Anthropic/Material surveys, so these percentages should not be combined into a single ranking. IBM’s summary of the study
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Where do enterprises expect agents to have the most impact?
Use cases involving frequent, repeatable work appear often in survey responses, but the order depends on who was asked and what the survey measured. Anthropic/Material enterprise respondents expected the greatest near-term agent impact in software development (61%) and customer service (56%), followed by marketing and sales (47%) and supply chain, logistics, and operations (45%). Gartner’s May–June 2025 survey of 360 IT application leaders at organizations with at least 250 employees across North America, Europe, and Asia/Pacific found that respondents most often placed analytics and business intelligence among the three domains expected to be most affected (64%), followed by customer service (55%) and office productivity (39%). These are expectations, not evidence of realized ROI. Anthropic and Material’s 2026 report; Gartner’s survey findings
Why productivity gains do not automatically mean enterprise ROI
Several distinct claims are often blurred together: an organization may expect a benefit, report a benefit, adopt an agent, or achieve a measurable company-wide financial result. One does not establish the next. For example, McKinsey’s August 2026 global survey found that 80% of respondents said AI improved their individual productivity, while 37% attributed at least some EBIT impact to AI use. About 6% qualified as AI high performers under McKinsey’s definition: they attributed at least 5% of EBIT to AI and described its impact as significant. These are AI-wide figures, not agent-only measures. McKinsey’s 2026 State of AI report
The gap matters for business planning. A faster task or more output per employee may be valuable, but the company-level effect depends on whether the time saved is used productively, whether costs actually fall, and whether the workflow can deliver more or better work. Productivity is a plausible starting point for the business case, not a substitute for tracking the outcome the enterprise ultimately wants.
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How to evaluate an AI-agent opportunity
Before selecting a workflow, define the intended outcome and how the organization will know whether it has been achieved. The following checks help distinguish a persuasive use case from a general promise of efficiency:
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- Name the target outcome. Specify whether the aim is less cycle time, greater employee capacity, lower cost, better decision quality, improved customer experience, or revenue growth.
- Check the work pattern. Identify how often the task occurs and how repeatable its steps are. A recurring workflow is easier to evaluate than an occasional, loosely defined activity.
- Establish a baseline and metric. Record the current performance before deployment, then define the measure that will show change—such as time per case, error or rework rate, cost per transaction, or customer-resolution time.
- Map data and integrations. Determine what systems and information the agent needs, whether that information is accessible and reliable, and where context must pass between tools.
- Set oversight and governance. Decide what the agent may do autonomously, where a person must review or approve work, and how errors, sensitive data, and exceptions will be handled.
- Assess whether the workflow can change. An agent inserted into an unchanged process may not capture its full potential; consider whether the process, user experience, and handoffs need redesign.
Workflow redesign and oversight are part of the business case
IBM’s account of client deployments emphasizes that value may require changing how a core process operates, rather than simply placing an agent inside an existing one. McKinsey likewise describes high-performing organizations as combining efficiency goals with growth or innovation, redesigning workflows enabled by AI, and backing deployments with leadership commitment and operational discipline.
Autonomy also needs to be treated carefully. Gartner reported that 75% of surveyed IT application leaders said their organization was piloting, deploying, or had deployed some form of AI agents, but only 15% were considering, piloting, or deploying fully autonomous agents. Gartner also identified governance, vendor trust, hallucination protection, and organizational readiness as concerns. In practice, an agent that assists a person or handles a bounded step can still support an efficiency objective; full autonomy is not a prerequisite for a business case.
How to interpret the survey evidence
The percentages above come from different respondent groups, geographies, dates, and questions. PwC surveyed US senior executives; IBM summarized studies with stated international scope; Gartner surveyed IT application leaders in three regions; and Anthropic/Material reported expectations among enterprise respondents. McKinsey’s productivity and EBIT findings concern AI broadly. They provide useful signals about priorities, but they are not interchangeable measures of adoption, causal impact, or market-wide financial returns.
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