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Companies use AI in customer service to answer routine questions, help human representatives handle cases, route requests, and automate steps across connected systems. Some deployments are customer-facing virtual agents; others work behind the scenes. The important distinction is what the system is allowed to do: suggest a response for a person to review, or take an approved action itself.
What are the main uses of AI in customer service?
Gartner groups valuable customer-service AI uses into four areas: agent enablement, low-effort self-service, operational support, and agentic AI. These categories describe different jobs, not a single all-purpose chatbot.
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| Use area | What AI does | Typical human role |
|---|---|---|
| Low-effort self-service | Answers routine questions, searches approved information, and assists customers through service channels. | A representative handles requests the system cannot resolve or that need human judgment. |
| Agent enablement | Summarizes customer history, finds information, drafts replies, and suggests next steps. | The representative reviews the assistance and remains responsible for the interaction. |
| Operational support | Helps classify and route cases, retrieve relevant knowledge, and coordinate follow-up work. | Staff oversee exceptions and the wider service process. |
| Agentic AI | Handles more complex, multi-step service workflows, potentially acting across connected systems. | People define permissions and escalation rules and intervene when needed. |
Gartner’s framework and examples of these capabilities are described in its October 2025 overview.
How do customer-facing AI agents help customers?
Virtual agents can respond to common questions, provide support across channels and languages, and sometimes resolve requests without a human representative. More advanced systems may also make proactive contact, recommend products, or work through a case across multiple channels. Those capabilities depend on how the company has connected and configured the system; the label “AI agent” alone does not show what it can do.
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For example, AWS says Ryanair’s chat assistant handles 120,000 customer chat interactions a day across seven languages, with an 80% containment rate. AWS also reports 10 million chatbot answers since the system launched in October 2024 and 94% accuracy. These are figures reported for the Ryanair deployment in an AWS case study accessed October 3, 2026. The case-study figure for accuracy should not be treated as an independent quality audit, and containment does not by itself establish customer satisfaction or successful resolution.
How does AI assist human customer-service representatives?
Agent-assist AI works alongside representatives rather than necessarily replacing them. It can condense a long conversation or customer history, surface relevant information, draft an email or chat response, and suggest what to do next. A human can then review the information, correct it, and decide what to send or do.
Microsoft’s AIA customer story describes Copilot in Dynamics 365 Customer Service drafting customer emails and summarizing lengthy chats and case histories to support case management and follow-up. Vodafone says its German call-center agent-assist system provides representatives with an immediate overview of a customer’s communication history. See the AIA customer story, published January 22, 2025, and Vodafone’s November 2025 H1 FY26 results presentation.
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A tool that prepares a draft for an employee to review is assisting the employee. A system that independently changes an account, processes a permitted request, or triggers follow-up in connected software is automating an action. Agentic handling goes further when the system coordinates several steps toward a service outcome.
To understand a deployment, check the actual workflow and permissions rather than relying on product terminology. A useful description says what information the AI can access, which actions it can take without approval, where a person must review or intervene, and what happens when the system cannot complete a task. Gartner’s discussion of operational support and agentic AI distinguishes these wider workflow uses from support that simply helps an agent respond.
What results have companies reported?
The figures below describe different measures, populations, and deployments. They are not directly comparable, and company or vendor case studies do not establish that another organization will get the same results.
| Source and scope | Reported figure | What the measure refers to |
|---|---|---|
| Gartner, survey of 265 service and support leaders fielded in April and May 2025 | 77% | Respondents who said they felt pressure from other senior executives to deploy AI. |
| Gartner, same survey | 75% | Respondents who said their AI initiative budgets had increased compared with the previous year. |
| Salesforce, survey of 3,075 customer-service professionals worldwide, as reported May 20, 2026 | 39% to 66% | Reported adoption of AI agents in customer-service organizations from 2025 to 2026, described by Salesforce as a 1.7-times increase. |
| Salesforce, same survey | 70% | Share of customer-service organizations adopting AI agents that reported observing measurable value within 60 days of deployment. |
| Vodafone, company-reported results in November 2025 | About 60 million conversations per month; 70% end-to-end resolution | Monthly conversations handled by Vodafone’s TOBi and SuperTOBi assistants and Vodafone’s reported end-to-end resolution rate. |
| Vodafone, same results presentation | 8 percentage-point NPS improvement | Vodafone’s reported change versus conversations handled by its previous AI agent. |
| Ryanair deployment, as reported by AWS | 120,000 chats per day; seven languages; 80% containment | Daily chat interactions, language coverage, and reported containment in the AWS case study. |
| Ryanair deployment, as reported by AWS | 10 million answers; 94% accuracy | Answers since launch in October 2024 and the case study’s accuracy figure; the source does not establish that this is an independent audit. |
Gartner’s figures come from its 2025 survey announcement. Salesforce’s figures are from its May 2026 report. Vodafone’s and Ryanair’s measures are reported by the companies or vendors, rather than presented here as independently verified market-wide results.
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How should a business evaluate an AI customer-service system?
Compare systems by the work they actually do and the evidence used to judge them. For each deployment, ask:
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- 100 Two-Part Carbonless Sets in One Book – Each service call log book includes 100 preprinted 2-part carbonless forms Write once and produce a duplicate copy instantly without separate carbon sheets Ideal for service call logs work order records and daily business documentation
- Compact Size for Convenient Daily Use – The 5 5/8 x 8 1/2 inch layout offers a comfortable writing area while fitting neatly on desks service counters and clipboards The portable size makes this service call log book easy to carry for both office staff and field technicians ensuring quick and efficient documentation anywhere
- Includes Writing Shield for Clean Copies – Each book comes with a sturdy backing board that prevents ink bleed-through to other sets and provides a firm writing surface making it convenient for field service technicians and office front desk use
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- Task scope: Does it answer a routine question, retrieve account information, summarize a case, draft a response, or carry out a multi-step transaction?
- Human involvement: Which outputs require review? What confidence thresholds, escalation rules, and recovery paths apply when the AI cannot finish?
- Channels and languages: Does it cover chat, email, voice, or several channels? Are the supported languages consistent across those channels?
- Workflow connections: Can it access customer records, approved knowledge, and case-management tools? Can it only recommend an action, or execute one?
- Outcome measures: Are you tracking containment, end-to-end resolution, first-response time, accuracy, customer satisfaction, NPS, representative productivity, or cost? Keep each measure separate and check its denominator, period, baseline, and reporter.
- Governance and monitoring: What testing, monitoring, and responsible-use controls apply to the system and its actions?
Vodafone describes testing, monitoring, and responsible-AI elements in its operating platform; AIA reports a responsible-use standard and a group AI Council. These are examples of company practices, not a complete standard that every deployment follows.
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