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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteNeither is best for every customer-service request. Chatbots can make some interactions easier and handle suitable routine tasks, while human agents performed better on several satisfaction and relationship measures in cited 2024 studies. The strongest approach is to use automation where it can reliably help, make human escalation easy to find, and measure whether customers’ issues are actually resolved.
What customers say they need from AI-powered support
Access to a person remains a major expectation even as customers use generative AI. In Gartner’s survey of 3,566 B2B and B2C customers, conducted in February and March 2026, 87% said it was essential for companies using GenAI in customer service to provide an option to reach a human agent. In the same survey, 50% said their interactions were easier when companies used GenAI. These are customer-reported views, not proof that a particular chatbot improves service. Gartner’s 2026 survey findings
Customers also do not necessarily choose a company’s own bot when they want AI help. Gartner reported that respondents were approximately three times more likely to use third-party GenAI than company-provided chatbots to resolve service issues. That finding describes reported use; it does not establish that third-party tools resolve issues more accurately. Gartner’s 2026 findings on third-party GenAI
How chatbots and human agents compare
| Customer-service measure | AI chatbot | Human support |
|---|---|---|
| Routine, well-bounded requests | Can answer common questions or perform defined actions when its information and capabilities are reliable. | Can handle these requests, though an agent may be unnecessary if automation can resolve them cleanly. |
| Unusual, sensitive or consequential requests | May have difficulty when a request falls outside its defined capabilities or requires judgment. | Better suited to requests needing context, discretion, empathy or individual judgment. |
| Issue resolution | Automation may handle part of a service journey without fully fixing the issue. In a Gartner survey of 5,728 customers fielded in December 2023, 73% had used self-service at some point, but only 14% of issues were fully resolved in self-service. | An agent can investigate and take further action, but whether the issue is resolved still depends on the agent’s access, authority and the service process. |
| Satisfaction and related outcomes | Two 2024 studies found lower customer outcomes after chatbot service than after human service in their study settings. | Human service scored better on several outcomes in those studies, though the results do not establish a universal ranking for every support system. |
| Availability and speed | Can be available for routine requests without waiting for an agent; availability alone does not show that a customer’s issue was solved. | May involve a queue or limited service hours, but can address needs that automation cannot. |
| Operating cost | Can automate some interactions, but the cited customer-outcome studies do not establish the cost of operating a particular chatbot. | Requires agent time; no general cost comparison is established by the cited customer-preference studies. |
The 2023 self-service figures are not a 2026 measurement. They also illustrate why use, containment and resolution should not be treated as interchangeable: a customer can try self-service and still need a person. Gartner’s self-service survey findings
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What comparative studies found about satisfaction
Human service performed better in two 2024 studies
A three-study research program published in the Journal of Retailing and Consumer Services found higher satisfaction, repatronage intentions and acceptance of agent recommendations for human service than for chatbot service in its study setting. Its findings also indicated that chatbot service with light-touch human intervention matched the more costly human-only interaction in that setting. This supports testing a hybrid model; it does not guarantee that every chatbot with an agent handoff will perform as well as human-only support. The 2024 study on chatbot service, satisfaction and trust
A separate 2024 study used three vignette studies involving 714 participants. Participants reported significantly lower satisfaction, repatronage intentions, acceptance of recommendations and likelihood of recommending the provider after chatbot interactions than after human interactions, in both positive and negative outcome conditions. Because these were vignette studies, the results should not be read as proof that every live chatbot performs worse than every human agent. The 2024 study on chatbot versus human service and outcome valence
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Vendor benchmark results are a different kind of evidence
Freshworks reported in 2024 that its benchmark covered anonymized data from 19 million Freshdesk tickets and 37 million Freshchat conversations across more than 25 industries, collected from January 2023 through April 2024. The company said generative-AI summarization and rephrasing could reduce resolution time by up to 38% and improve satisfaction scores by up to 6%. These are vendor-reported benchmark claims, not independent causal estimates, and should not be directly compared with the customer-preference studies as if they used the same method. Freshworks’ 2024 customer-support benchmark summary
Which requests should go to a chatbot or a person?
Use a chatbot when the task is predictable
Automation is most defensible for repetitive, clearly bounded requests when the bot can provide a reliable answer or complete a defined action. Examples might include locating an order, checking a published policy or collecting information for a support ticket—provided the company’s systems let the bot give accurate, current information.
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Offer a person when context or judgment matters
Make human support prominent for unusual requests, emotionally charged interactions, consequential decisions, or cases that remain unresolved after automation. If a bot cannot answer confidently or complete the requested action, it should explain the limitation and give the customer a clear route to an agent rather than repeatedly redirecting them through the same automated steps.
Let customers move between channels
A practical hybrid service lets a bot handle appropriate initial work while allowing an agent to take over with the conversation context intact. Gartner’s Eric Keller, senior director analyst in its Customer Service & Support Practice, said: “GenAI is more helpful when it collects information, understands intent and attempts resolution only when confidence is high, with a clear path to human support.” Gartner, August 4, 2026
Rank #4
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How to judge whether automation is working
A bot’s containment rate—the share of conversations it handles without transferring them—cannot by itself show whether customers received good service. A conversation can end because a customer gave up, not because the problem was fixed. Evaluate the measures separately:
- Resolution: Was the customer’s issue fully fixed, and did they need to contact support again?
- Customer experience: Were satisfaction and trust maintained, and could customers reach a person when they needed one?
- Speed: How long did it take to resolve the issue, not merely to receive an initial response?
- Escalation: Did the handoff happen when the bot could not help, with useful context passed to the agent?
- Cost: What did it cost to resolve an issue successfully, including the agent work required after unsuccessful automation?
These measures can point in different directions. A fast first reply or lower agent workload is not enough to establish a better customer experience if resolution falls or customers cannot reach a person.
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