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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesAI is spreading through customer service, but the headline adoption figures do not all mean the same thing. Surveys report broad exploration and planned pilots, while a large field study found that an AI assistant helped support agents resolve more issues per hour on average. Those findings describe different applications and outcomes; none proves that every deployment improves service.
At a glance: what the figures measure
| Finding | What it measures | Population and timing |
|---|---|---|
| 85% planned to explore or pilot customer-facing conversational GenAI in 2025 | Stated plans, not deployments | 187 customer service and support leaders surveyed by Gartner in July–August 2024. Gartner, December 9, 2024 |
| 86% had implemented GenAI, started pilots, or begun exploring it in customer service | A combined measure across three maturity stages | 1,002 executives surveyed by Capgemini Research Institute in November–December 2024. Capgemini Research Institute, 2025 |
| 15% average increase in issues resolved per hour with an AI assistant | Observed operational productivity in one field study | 5,172 customer support agents. The working paper was first dated 2023; its arXiv page also includes a March 2026 manuscript version. Brynjolfsson, Li, and Raymond |
| 51% said they would be willing to use a GenAI assistant on their behalf in customer service interactions | Customer willingness, not actual usage | 4,879 customers surveyed by Gartner in January–February 2025. Gartner, June 25, 2025 |
| 45% reported overall satisfaction with service; 71% said chatbots had improved in quality over the previous one to two years | Consumer survey responses | Capgemini’s 2025 survey summary covered 9,500 consumers. Capgemini, March 13, 2025 |
Adoption statistics: exploration is not deployment
Gartner: 85% planned to explore or pilot conversational GenAI
In a survey of 187 customer service and support leaders conducted in July and August 2024, Gartner found that 85% said their organizations would explore or pilot customer-facing conversational GenAI in 2025. This is a forward-looking intention reported in 2024—not a count of organizations that actually deployed the technology during 2025.
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The survey’s voicebot responses show why maturity categories matter: 44% said they were exploring a customer-facing GenAI voicebot, 11% were piloting one, and 5% reported one deployed at the time of the survey. Exploration, testing with users, and live deployment are distinct stages, so they should not be combined into a single “using AI” rate. Gartner’s survey announcement
Capgemini: 86% were at one of three maturity stages
Capgemini Research Institute reported that 86% of surveyed organizations had implemented GenAI, initiated pilots, or started exploring it in customer service. The executive survey took place in November and December 2024 and included 1,002 respondents. Because the figure combines implementation, pilots, and exploration, it does not establish that 86% had put AI into live customer-service operations.
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The Gartner and Capgemini figures should not be read as a trend line or as competing estimates of the same thing. They differ in question wording, survey population, timing, and which maturity stages they count. Capgemini Research Institute’s report
What AI may change for support agents
A field study found a 15% average productivity gain
Erik Brynjolfsson, Danielle Li, and Lindsey Raymond studied 5,172 customer support agents using a generative AI assistant. Their working paper reported an average 15% increase in issues resolved per hour. This is operational evidence from a particular deployment context, rather than a survey asking companies what they expect AI to do.
The average concealed substantial differences between workers. Less experienced agents saw larger improvements in speed and quality, while the most experienced group had small speed gains and small quality declines. The result therefore does not mean every agent became 15% more productive, nor does one company’s setting establish the effect in other support operations. The paper was initially dated April 2023; the arXiv page also lists a March 2026 version, so readers comparing detailed findings should note which manuscript version they consult. “Generative AI at Work”
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In Capgemini’s 2025 findings, 73% of surveyed customer service agents said GenAI reduced the time they spent on mundane tasks, and 70% reported a reduction in overall workload. These are agent self-reports, not universal estimates of time saved across all companies or a controlled measure of productivity. The report’s executive, consumer, and agent results come from different respondent groups; these two workload figures refer to agents. Capgemini Research Institute, 2025
Reported benefits versus expected benefits
Capgemini asked executives at organizations exploring, piloting, or implementing GenAI about service outcomes. These responses separate benefits respondents said they had already started to realize from benefits they expected. The surveyed base for these outcome figures was 861 executives, rather than the report’s full 1,002-executive maturity sample.
| Outcome | Already started realizing | Expected |
|---|---|---|
| Faster response times | 31% | 58% |
| Enhanced customer satisfaction | 26% | 60% |
| Increased first-contact resolution | 33% | 52% |
“Already started realizing” is still a survey response, not an independently verified causal estimate. The expected-benefit column is explicitly an expectation, not an achieved result. The figures do not show that AI alone caused a change or that every organization experienced one. Capgemini Research Institute, 2025
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Customer attitudes depend on the kind of AI
Customers report better chatbots, but satisfaction remains mixed
Capgemini’s 2025 consumer survey summary says 45% of respondents were satisfied overall with the service they received, while 71% said chatbots had improved in quality over the preceding one to two years. These findings can coexist: perceived improvement in one channel does not mean most consumers are satisfied with service overall.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchThe same summary says virtual agents are valued for speed and convenience, while more than 70% of consumers prefer human agents for empathy and creative problem solving. That points to a task distinction: quick, bounded requests may suit automation better than emotionally sensitive or unusual problems. The survey summary does not establish that every chatbot is fast, convenient, or capable of resolving a given issue. Capgemini, March 13, 2025
Customer-owned assistants are a separate category
Gartner reported that 51% of 4,879 surveyed customers said they would be willing to use a GenAI assistant for customer-service interactions on their behalf. The survey was conducted in January and February 2025. This is stated willingness to use a customer’s own assistant—not evidence that customers had already adopted these assistants or that businesses had deployed customer-facing chatbots.
The distinction matters for businesses: an assistant acting for a customer may change how service requests arrive and how companies handle them, while an agent-assist tool supports a human employee and a customer-facing bot communicates directly with the customer. Gartner analyst Brad Fager warned that widespread customer adoption of AI assistants could create a cost risk that undermines gains from service automation. The survey measures willingness, not that predicted cost effect. Gartner, June 25, 2025
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Knowledge quality and human handoff are practical constraints
Customer-facing AI depends on the information it can retrieve and the boundaries set for resolving or escalating a request. In Gartner’s 2024 leader survey, 61% said their organization had a backlog of knowledge articles to edit, and more than one-third reported having no formal process for revising outdated articles. A conversational system drawing from stale or inconsistent guidance can reproduce those problems at scale.
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Gartner’s Kim Hedlin summarized the tension: “Service and support leaders are eager to deploy conversational GenAI, but they cannot ignore existing issues with knowledge management.” The survey figures do not prove that weak knowledge management caused any particular chatbot failure, but they identify a readiness issue companies should address before relying on automated answers. Gartner, December 9, 2024
- Keep source material current: assign ownership for reviewing and revising support articles.
- Define escalation: route cases requiring empathy, judgment, or exception handling to a human agent.
- Measure separate outcomes: track resolution per hour, response speed, first-contact resolution, satisfaction, and workload independently.
- Disclose the type of result: distinguish observed service metrics from employee or customer self-reports and executive expectations.
How to interpret customer-service AI statistics
- Identify the AI application. Agent assistance, customer-facing conversational automation, and a customer-owned assistant are different uses of AI.
- Check the maturity stage. “Exploring,” “piloting,” and “implemented” describe different levels of organizational use; a combined figure is not a deployment rate.
- Read the outcome and its unit. Issues resolved per hour is an operational productivity measure; response time, satisfaction, first-contact resolution, and workload are separate outcomes.
- Separate results from expectations. “Already started realizing” and “expected” benefits are not interchangeable, and both differ from a controlled causal finding.
- Check who answered and when. An executive survey, agent survey, and consumer survey answer different questions. Dates and sample bases matter when comparing results.
- Account for task and worker differences. A productivity average can conceal uneven effects by experience, skill, and the complexity of the work.
- Look for service safeguards. Knowledge maintenance and a clear path to human help affect whether automation can answer reliably and handle sensitive cases appropriately.
Frequently Asked Questions
How many customer service leaders planned to use conversational AI in 2025?
Gartner found that 85% of 187 customer service and support leaders surveyed in July–August 2024 planned to explore or pilot customer-facing conversational GenAI in 2025. It measured plans, not the share that ultimately deployed it.
Did AI increase customer support productivity?
In one field study of 5,172 support agents, researchers reported a 15% average increase in issues resolved per hour with access to a generative AI assistant. Effects varied by worker experience, so the average should not be treated as a guaranteed gain in every operation.
Do customers prefer chatbots or human agents?
Capgemini’s 2025 survey summary found that consumers valued virtual agents for speed and convenience, but more than 70% preferred human agents for empathy and creative problem solving. Preference therefore depends in part on the task.
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Does the 86% AI adoption figure mean that 86% of companies had deployed it?
No. Capgemini’s 86% combines organizations that had implemented GenAI, started pilots, or begun exploring it in customer service. It is not an implementation-only figure.
What is the biggest limitation of customer-service AI statistics?
Many headline figures describe different stages, respondent groups, applications, or kinds of evidence. A plan, a customer’s stated willingness, a self-reported benefit, and a measured operational outcome cannot be treated as equivalent proof of deployment or impact.
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