AI is spreading through customer service, but deployment is not the same as customer value—or financial return. Gartner’s 2026 survey found that just 24% of service and support leaders demonstrated positive financial returns across their AI use cases, even though leaders in the function devoted a median 12% of their 2025 budgets to AI. The practical question is not whether a company has launched AI; it is whether customers can use it to get something done and whether the organization can measure the result.
AI adoption and business impact are different measures
“AI everywhere” can describe visibility, experimentation, or regular use. None of those, by itself, establishes that AI improved customer experience or the company’s finances. The surveys below ask different questions of different groups, so their figures are signals about reported adoption and outcomes, not one comparable scorecard.
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Gartner’s survey of 1,303 senior leaders, conducted from January through April 2026, found that service and support leaders invested a median 12% of their 2025 budgets in AI—the highest median share among the ten business functions assessed. Yet only 24% of those leaders demonstrated positive financial returns across their AI use cases. This is a survey finding about reported returns, not a controlled estimate of AI’s effect. Gartner’s July 8, 2026 findings provide the service-leader and customer figures.
McKinsey’s 2026 global survey points to a similar gap at the organizational level. Nearly nine in ten respondents said their organizations regularly used AI in at least one business function, and 44% said AI was scaling across the enterprise, up from 38% a year earlier. But 37% reported a positive contribution from AI to organization-level EBIT, essentially unchanged from 2025. Eight in ten said AI improved their own productivity. Personal productivity can matter, but it is not interchangeable with a measurable company-wide financial contribution.
#1 Best Overall
McKinsey defined its “AI high performers” as respondents attributing at least 5% EBIT impact to AI and reporting significant value. They made up 6% of respondents. Nearly three-quarters of this group said they had fundamentally redesigned workflows because of AI, compared with one-quarter of other respondents. That association suggests workflow change may be part of the path to value; survey data cannot establish that redesign alone caused the higher reported impact.
Customers judge service AI by what it helps them do
Customers are not limited to the chatbot a company puts on its website. In Gartner’s survey of 3,566 B2B and B2C customers, conducted in February and March 2026, respondents were approximately three times more likely to have used third-party GenAI than a company-provided chatbot in their most recent service interaction. Gartner said third-party GenAI use for service had nearly doubled over the prior year, while company-provided chatbot use had remained statistically unchanged since 2022.
Among customers who use GenAI, 58% said they had used it to complete a task on their behalf; the figure was 74% among B2B customers. Gartner’s examples included booking an appointment, placing an order, submitting documents, managing a subscription, and escalating a request. These are actions, not merely answers. A system that explains how to change a subscription is different from one that can securely make the change.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsThere is no contradiction between customers finding AI useful and expecting human help. In the same-size customer survey, 50% said interactions were easier when companies used GenAI, while 87% said it was essential for companies using GenAI in service to provide access to a human agent. Gartner analyst Eric Keller put the implication plainly: “Service leaders should not use GenAI as a mandatory first step for every issue.” The figures and quote appear in Gartner’s August 4, 2026 release and analyst Q&A.
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Useful answers do not automatically resolve customer requests
A customer-service system can produce a fluent response and still leave the customer with the same problem. The gap often sits between understanding a request and being able to act on it. To complete a task, an AI system may need access to accurate account or transaction information and a reliable way to carry out an authorized action. If it lacks that access, or cannot finish the request, the customer needs a clear route to a person rather than a loop of repeated prompts.
That makes workflow design as important as the conversational interface. Before choosing an approach, leaders should determine what the system is allowed to do, what information it needs, which situations require human judgment, and how the customer can reach support without starting over. Gartner’s Eric Keller said the disappointing impact of customer-facing GenAI investments has “less to do with technology limitations and more to do with misalignment with customer expectations.” The statement is an analyst’s interpretation, not a measured causal finding.
Rank #4
What the evidence can—and cannot—say about sales
A working paper, Generative AI and Firm Productivity: Field Experiments in Online Retail, offers stronger causal evidence for particular customer-facing uses than a survey can. The authors describe randomized experiments across seven workflows at a large cross-border online retail platform over six months in 2023–2024. They report sales treatment effects ranging from 0% to 16.3%, depending on the application’s marginal contribution relative to existing practices. The reported mechanism was higher conversion, with larger gains for smaller and newer sellers and less experienced consumers. The paper is available on arXiv.
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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →The range matters: some applications showed no sales effect, while others had a positive one. These results do not forecast what a different retailer—or a bank, telecom provider, or support center—will achieve. They show that effects can vary substantially by use case and context, and that a specific implementation should be evaluated against what it replaces or adds.
Best Value
Salesforce reported a different kind of evidence in a May 2026 announcement based on a survey of 3,075 customer-service professionals worldwide. It said AI-agent adoption rose from 39% in 2025 to 66% in 2026, and 70% of organizations using AI service agents reported measurable value within 60 days; customer satisfaction was the KPI most often described as improved. These are vendor-published, self-reported survey results, not independent causal estimates. They describe respondents’ reported experience, not a guaranteed result from deploying an agent. Salesforce’s announcement provides its figures and methodology context.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Measure customer, operating, and financial results separately
A credible evaluation starts before launch. Define the problem, the current baseline, the intended change, and the period over which it will be assessed. Then keep measures in distinct categories: a faster response is not necessarily a completed task, and a completed task is not automatically a financial return.
- Customer outcomes: successful task completion, customer satisfaction, and whether customers can reach a person when needed.
- Operating outcomes: resolution performance, human escalation, repeat contacts, and the cost of handling the work.
- Business outcomes: revenue, retention, cost savings, or EBIT, selected according to the use case.
Compare results with the baseline and with the process the AI is intended to change. A productivity gain for an employee may reduce handling time, for example, but leaders still need to determine whether that improvement changes service quality, capacity, cost, or another business outcome. Likewise, a high containment rate is not enough if customers abandon the interaction or return later for the same issue.
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- Choose a bounded task. Name the customer problem and the action or outcome the system should deliver, rather than measuring success by launch or usage alone.
- Check its ability to act. Confirm it can use accurate, relevant account or transaction information and perform only authorized actions required for the task.
- Design the human handoff. Define when the system should stop, how customers request a person, and what context transfers so they do not have to repeat the issue.
- Set the baseline and measures. Record existing customer, operating, and financial performance before rollout; specify which measures are primary and how they will be evaluated.
- Test the specific implementation. Compare outcomes with the existing process where practical, and report the result for the tested use case rather than extrapolating it to every AI application.
These checks help distinguish a visible AI feature from a service workflow that customers can actually use and an investment whose effects the organization can verify.
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