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AI can improve customer support when it helps agents diagnose issues and draft replies, or handles straightforward customer tasks with accurate information and a fast route to a person when needed. Evidence shows productivity gains and some improvements in customer ratings, but not a guaranteed improvement in resolution: effects differ by worker, and one field experiment found no significant change in repeat attempts to resolve an issue.
Where AI can improve the support experience
AI contributes in two main ways: it can assist a human agent, or it can interact directly with the customer. These approaches solve different problems, and a service can use both.
Agent assistance
An assistant can help an agent interpret a customer’s issue, find relevant information, and draft a possible response. The agent can adopt, edit, or ignore the suggestion. This can reduce time spent searching or composing routine replies while leaving judgment and communication with a person.
In a 2026 field experiment in Alibaba e-commerce after-sales chat, agents used an assistant that diagnosed issues and proposed solutions. Researchers reported faster issue identification and shorter chats, alongside better subjective customer ratings and dissatisfaction measures. The experiment found no statistically significant change in customer retrial rates, an objective measure of whether customers tried again to resolve the issue. Results also varied: lower-performing agents improved most, while top performers saw declines in subjective and objective service quality that the authors associated with multitasking.
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Customer-facing automation
A chatbot or AI agent can answer common questions and attempt bounded tasks, such as gathering information or processing a routine service request. Gartner recommends that these systems interpret intent, collect relevant details, and attempt resolution when confidence is high. For more complex or consequential situations, the system should make human escalation clear and available.
Agent assistance and customer-facing automation should not be judged by the same measure alone. Faster replies may indicate less waiting, but they do not establish that a problem was solved correctly or that the customer will not need to contact support again.
What the evidence says about productivity and quality
One widely cited study, the 2023 working paper Generative AI at Work by Erik Brynjolfsson, Danielle Li, and Lindsey R. Raymond, examined the staggered introduction of a conversational assistant among 5,172 customer-support agents. Agents with access to the assistant resolved 15% more issues per hour on average. That is a study result, not a promised gain for every team or a measure of customer satisfaction.
Outcomes differed across workers. Less experienced and lower-skilled agents saw larger gains in speed and quality. The most experienced and highest-skilled agents had small speed gains and small quality declines. The authors also found evidence of worker learning and improved English fluency, especially among international agents, and larger gains on relatively rare problems.
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Taken together with the Alibaba experiment, the evidence points to a conditional benefit: AI can help some agents work faster and can improve some customer-rated outcomes, but its effect depends on the worker, workflow, and task. Customer ratings, speed, and objective resolution are separate outcomes. A program should measure them separately rather than treating any one of them as proof that support has improved.
Customers still need access to a person
In a Gartner survey of 3,566 B2B and B2C customers conducted in February and March 2026, 87% said access to a human agent is essential when companies use generative AI for customer service. The same survey found that 50% reported easier interactions when companies use GenAI; among customers using GenAI, 58% had used it to complete a task, rising to 74% among B2B customers. These are Gartner survey findings, not universal customer preferences.
Gartner analyst Eric Keller advised service leaders: “Service leaders should not use GenAI as a mandatory first step for every issue.” A customer should not have to exhaust a chatbot’s options before reaching a person, especially when the matter is sensitive, unusual, or already causing frustration. Clear escalation rules and a usable handoff are part of the service design.
Match AI authority to the task and its consequences
The more consequential a task is, the less appropriate it is to let a system act without review. A bot answering an approved-hours question has a different risk profile from one approving a refund, changing an account, or responding to a health or legal concern.
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The UK Department for Business and Trade’s March 2026 report describes most consumer-facing AI as decision support and says agent deployments remain early, bounded, and cautious. It notes that agents may handle multi-step tasks such as service requests or refunds, but consumer-facing authority is limited and human escalation remains common. The report also states that UK consumer law applies whether decisions are made by people or AI; this legal statement concerns the UK and should not be generalized as a summary of US law.
The Government of Japan’s March 2026 AI Governance Practical Manual recommends human escalation for cases including complaints, high-value transactions, cancellations, and health or legal consultations. These examples illustrate sensible escalation categories; organizations should define rules suited to their services and applicable obligations.
How to evaluate whether AI is improving support
Set a baseline before deployment, then compare outcomes for the tasks and customer groups where the tool is used. Track speed alongside whether the issue was resolved and what happened afterward.
- Resolution: Was the issue resolved, or did the customer contact support again or retry the task?
- Customer experience: Track satisfaction, complaints, and dissatisfaction—not just response time.
- Accuracy: Review incorrect guidance, unsupported claims, and cases where the AI failed to recognize uncertainty.
- Escalation: Measure when handoffs occur, whether customers can reach a person, and whether relevant context reaches the agent.
- Agent impact: Compare outcomes across experience and performance levels. A tool that helps newer workers may distract experienced agents, as the Alibaba experiment illustrates.
- Task and group differences: Check results across issue types and customer groups so an average does not conceal a worse experience for a subset.
Japan’s governance manual recommends monitoring complaints, misguidance, escalation, resolution, and satisfaction, with thresholds that trigger remediation. If those measures show recurring errors or harm, teams need a defined response—such as correcting the knowledge source, narrowing the system’s permitted tasks, adding review, or suspending a function.
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Protect customer data and build trust
Support conversations can contain personal, financial, or confidential information. Limit what the system receives to what it needs, understand how the vendor handles and retains data, and restrict access appropriately. Japan’s manual specifically recommends minimizing personal and confidential data and training operators to understand AI limitations.
NIST’s voluntary AI Risk Management Framework describes trustworthy AI in terms that include validity and reliability, safety, security and resiliency, accountability and transparency, explainability and interpretability, privacy, and fairness with mitigation of harmful bias. NIST released its Generative AI Profile in July 2024. These are design and governance considerations, not a certification that a particular customer-service tool is safe.
The FTC’s 2022 report on AI used to address online harms identifies risks including inaccuracy, bias and discrimination, and surveillance incentives. It is not a customer-support performance study, but those risks are relevant cautions when organizations decide what data to collect and which decisions to automate.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical decision framework
| Question | What to establish |
|---|---|
| Who is using the AI? | Distinguish an agent-assist tool from a customer-facing chatbot or agent; the user, failure modes, and handoff design differ. |
| What can it do? | Define the task boundary, permitted actions, and consequences of an incorrect answer or action. |
| What information grounds its answers? | Use approved, current knowledge and evaluate accuracy, uncertainty handling, and unsupported responses. |
| How does a customer reach a person? | Make escalation available and specify which cases require it, without making AI a mandatory first step for every issue. |
| What counts as success? | Measure resolution and repeat contact alongside speed, satisfaction, complaints, and misguidance. |
| Who benefits or loses? | Review outcomes by agent experience, performance, task, and customer group rather than relying only on an overall average. |
| How are risks controlled? | Set privacy and security protections, monitoring owners, remediation thresholds, and accountability for decisions. |
Frequently Asked Questions
How can AI improve customer service?
It can help agents diagnose issues and draft replies, or handle common questions and bounded tasks directly. Improvements are most credible when answers are grounded in reliable information and customers can escalate to a person.
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Does AI improve customer support quality or just make it faster?
It can do either, but speed alone does not prove better quality. Studies have found productivity gains and some improvements in customer ratings, while objective outcomes such as retrial rates may not change. Measure resolution and repeat contact as well as speed and satisfaction.
Will AI replace customer service agents?
The evidence here supports AI assisting agents and handling bounded tasks, not a general conclusion that it replaces support teams. Human judgment and escalation remain important, particularly for complex or consequential cases.
What should a company monitor after adding AI to support?
Track resolution, repeat contacts, complaints, incorrect guidance, escalation, customer satisfaction, and response speed. Review differences across tasks, customer groups, and agent experience, and define what action follows when results cross a risk threshold.
Is AI Risk Management Framework compliance mandatory?
NIST describes its AI Risk Management Framework as voluntary guidance for incorporating trustworthiness into AI design, development, use, and evaluation. It should not be presented as a legal requirement or certification.
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