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AI can improve customer service when it helps people solve routine problems without friction, gives representatives useful context, or safely completes service tasks across business systems. But deploying AI is not the same as improving the customer experience: resolution, customer effort, satisfaction, and trust need to be measured separately.
Where AI can change the service experience
Customer service AI is not one capability. The practical difference is whether a system only produces or recommends language, or can also access approved business systems and take actions. That distinction affects what customers can accomplish without a person, what representatives can do with AI assistance, and what safeguards a company needs.
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Conversational self-service
A conversational system can help customers find information, answer common questions, or guide them through routine requests. This addresses familiar sources of frustration: lack of self-service options, repeated transfers between people or departments, and limited knowledge about a product or service. Salesforce’s October 2024 statistics library says U.S. consumers estimate they are transferred at least once in 87% of service interactions; that is a consumer estimate, not a measurement of all service interactions. Salesforce’s customer-service statistics also identifies those pain points.
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Assistance for representatives
AI can support a representative by surfacing relevant information or drafting a response for review. In this arrangement, the employee remains responsible for deciding what to send or do. The benefit depends on whether the information is accurate and timely, and whether the tool reduces work rather than adding another system to check.
Agents that take service actions
An AI agent connected to business systems may be able to do more than answer or draft: it may retrieve relevant context and carry out an authorized step in a service workflow. Salesforce Service Cloud EVP and General Manager Kishan Chetan described agents as able to “understand context, take action, make decisions, and adapt in real time.” This is Salesforce’s characterization of AI agents, not a guarantee that every product has those capabilities or performs them reliably. Salesforce’s statement was published November 13, 2025.
For a customer, the difference matters: an answer that explains how to change an account is not the same as a system that can make the approved change. Action-taking can remove steps, but it also makes access control, confirmation, error handling, and escalation more consequential.
What the adoption figures do—and do not—show
Available figures point to growing use, but they have different scopes and cannot establish that customer experience has improved.
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| Figure | What it measures | How to interpret it |
|---|---|---|
| 30% of cases handled by AI; 50% projected by 2027 | Service teams’ estimates at the time of Salesforce’s 2025 survey and their projection for 2027 | Survey estimates and expectations, not independently verified shares for all companies. The survey included 6,500 service professionals and decision makers and ran April 25–June 6, 2025. Salesforce’s 2025 State of Service report |
| 2,199% six-month compound annual growth rate | Growth in customer service conversations with AI agents for the average business in Salesforce’s H1 2025 Agentic Enterprise Index | Salesforce-reported activity in its own usage cohort, not a market-wide adoption rate. Salesforce’s index statistics |
| 94% of customers who observed an agent in a chat window engaged with it | Engagement in the product cohort observed by Salesforce’s H1 2025 index | A cohort-specific figure; it does not show that 94% of all customers prefer AI or that their issues were resolved. Salesforce’s index statistics |
| 51% of service leaders | Share who said security concerns had delayed or limited their AI initiatives in Salesforce’s 2025 survey | A survey response, not a measure of security incidents or the prevalence of a specific technical weakness. Salesforce’s 2025 State of Service report |
Salesforce says its index analyzed business activity from February 2025 to April 2026 and included businesses that had activated agents in production in each month of that period. That selection condition matters: activity from organizations with agents continuously in production should not be treated as representative of every company. Salesforce’s index methodology describes the cohort.
Adoption is an input, not an outcome. McKinsey’s 2024 customer-care analysis found varied success among early generative AI deployments. A company can handle more contacts with AI without making resolution easier, faster, or more satisfying for customers. McKinsey’s customer-care analysis supports treating those results separately.
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How to judge whether AI improves customer experience
Evaluate the system against real customer outcomes, not the volume of AI interactions or the number of tasks it can attempt. A useful assessment looks at both the result and the path the customer took to get there.
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- Customer effort: How many steps, repeated explanations, transfers, or follow-ups were needed?
- Time to resolution: Did the customer reach a correct outcome sooner, including after any handoff?
- Satisfaction and trust: Did the interaction leave the customer confident that the system understood the request and handled it appropriately?
- Handoff quality: When a person was needed, did the representative receive the conversation and relevant context so the customer did not have to start over?
Track these measures by request type and by whether AI handled the interaction, assisted a representative, or handed the case off. This helps distinguish a genuine improvement from a system that simply shifts work between channels or people.
Best Value
Questions to ask before choosing an AI approach
There is no basis here for declaring one platform or approach universally best. Compare options against the work your service team needs to do and the controls it requires.
- Task scope: Which requests can it handle? Does it only draft or recommend text, or can it take actions in business systems?
- Context and access: What customer and service information can it use, and how is that access limited to what the task requires?
- Uncertainty and escalation: How does it recognize an unclear, sensitive, or complex request, and how does it transfer the interaction to a person?
- Security and review: What controls govern access and actions, and what review is available for the system’s responses and activity?
- Outcome measurement: Can the organization measure resolution, effort, satisfaction, and handoff quality—not just usage or speed?
These questions follow from the distinction between text generation and system-connected action, as well as the security concerns service leaders reported. They are evaluation criteria, not results from an independent comparison of vendors.
Keep people in the service model
AI is most useful when it takes appropriate routine work off people’s hands without making complex or sensitive cases harder to resolve. Salesforce’s 2025 report presents AI as a way to handle routine cases and give representatives more room for complex work. That is the vendor’s view, not proof that every deployment creates that outcome. Salesforce’s report provides the survey context.
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For the customer, a good service model makes the transition between AI and a person clear. Routine requests may be suitable for self-service or an authorized agent action; a complicated request should have a straightforward path to a representative who can see what has already happened. The measure of success is whether the customer reaches a correct resolution with less friction—not whether a human was removed from the interaction.
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