Customer support is moving toward digital-first service, broader self-service, and more AI within support workflows—but not toward a human-free model. The emerging operating pattern pairs automation and agent-assist tools for routine work with people handling complex cases, applying judgment, and maintaining the knowledge that both customers and AI depend on.
What is changing in customer support?
Several connected shifts are shaping service teams: organizations are prioritizing digital channels and self-service, AI is becoming more embedded in case handling, and expectations for human agents are evolving toward complex support and knowledge work. The pace and extent of these changes vary by survey, organization, and definition. Reported plans and opinions should not be mistaken for completed deployments or guaranteed results.
Digital-first support is gaining importance
In an April–May 2025 survey of 265 customer-service and support leaders, Gartner found that respondents expected digital-first technologies to gain value relative to phone and email. Live chat, self-service portals, and knowledge-management systems were among the important technologies. That is a forecast about perceived value, not evidence that every organization should remove phone or email support.
Gartner’s J.J. Moncus described these channels and systems as “solidifying their place as essential tools for fast, scalable support.” The practical implication is to make digital support easier to find and use while preserving the channels customers need, especially for issues that are urgent, sensitive, or difficult to resolve through self-service.
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AI is entering workflows, but adoption measures differ
In Salesforce’s seventh State of Service survey, fielded April 25–June 6, 2025 among 6,500 service professionals and decision makers, respondents estimated that AI handled 30% of service cases at the time and projected 50% by 2027. Those are survey estimates and expectations, not independently audited case counts.
A separate Salesforce survey points to increased agentic-AI use: in a double-anonymous survey of 3,075 service professionals fielded March 9–April 4, 2026, 66% of customer-service organizations reported using agentic AI, compared with 39% in 2025. The two Salesforce findings are not directly interchangeable: one concerns estimated case handling and a projection; the other concerns reported organizational adoption under a different survey and definition.
Human roles are expected to shift
AI’s likely effect on service work is not simply fewer human interactions. Salesforce reported that reps who used AI spent 20% less time on routine cases. This is a survey finding, not proof that AI caused the change in every operation. Gartner’s October 2025 survey of 321 customer-service and support leaders found that nearly 80% planned to transition at least some agents into new roles, 84% planned to add skills to the agent role, and 58% aimed to upskill agents into knowledge-management specialists. These are reported intentions, not completed changes.
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Gartner’s Kim Hedlin summarized the direction as a period “where AI and human expertise must work in tandem.” Teams still need people to investigate ambiguous cases, handle sensitive interactions, make judgment calls, and improve the information that supports both agents and automated systems.
What the 2025–2026 survey findings say
| Finding | Source, sample, and timing | How to interpret it |
|---|---|---|
| 73% forecast to implement agent-assist by the end of 2025 | Gartner, survey of 265 customer-service and support leaders, April–May 2025 | A forecast of planned implementation, not a measured adoption result. |
| 30% estimated AI case handling at the time; 50% expected by 2027 | Salesforce, seventh State of Service survey of 6,500 service professionals and decision makers, April 25–June 6, 2025 | Respondents’ estimates and projections, not audited case volumes. |
| 51% of service leaders said security concerns delayed or limited AI initiatives | Salesforce, seventh State of Service survey, 2025 | A reported obstacle that makes data security and governance relevant to rollout decisions. |
| 66% using agentic AI in 2026, compared with 39% in 2025 | Salesforce, double-anonymous survey of 3,075 service professionals, March 9–April 4, 2026 | Reported organizational adoption; this is a different measure from the 2025 estimated share of cases handled by AI. |
| 70% of organizations using AI service agents said they observed measurable value within 60 days | Salesforce, AI Agents Edition survey, 2026 | Respondents’ reported experience, not a controlled demonstration of typical results or ROI. |
| 91% reported executive pressure to implement AI | Gartner, survey of 321 customer-service and support leaders, October 2025 | Reports leadership pressure, not proof that a specific AI deployment is necessary or effective. |
| 85% of surveyed CX leaders believed customers would drop brands unable to resolve issues on first contact; 86% of surveyed consumers said responsiveness and accurate resolution strongly influenced purchase decisions | Zendesk’s 2026 CX Trends report, summarized in its public release | The figures describe different respondent groups and their reported beliefs or views. |
Why service teams are changing their priorities
Resolution matters alongside speed
Zendesk’s 2026 CX Trends release reported that 85% of surveyed CX leaders believed customers would drop brands unable to resolve issues on first contact. In the same report, 86% of surveyed consumers said responsiveness and accurate resolution strongly influenced purchase decisions. These are reported views from distinct groups, not a guarantee that first-contact resolution alone determines customer retention.
Gartner’s October 2025 survey found that customer satisfaction, operational efficiency, and self-service success were among leaders’ top priorities for 2026. Taken together, these findings suggest that teams are being asked to make service both easier to access and more effective at resolving the underlying problem. A faster answer that sends a customer through repeated contacts is not the same as a successful resolution.
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Security and data readiness shape AI plans
In Salesforce’s 2025 survey, 51% of service leaders said security concerns had delayed or limited AI initiatives. Salesforce’s 2026 survey also reported data-readiness concerns among operations staff. AI and self-service depend on the quality, accessibility, and appropriate handling of the information they use. A support team therefore needs to consider governance and data readiness as operational requirements, not treat them as afterthoughts to deployment.
Knowledge management is becoming more central
Knowledge articles, policies, and troubleshooting steps are the foundation for customer self-service and for tools that assist agents. Gartner found that 58% of surveyed leaders aimed to upskill agents into knowledge-management specialists. That intention reflects a practical connection: when agents identify missing, outdated, or unclear answers, capturing and correcting that knowledge can improve future support—provided the organization has a process for review and maintenance.
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| Approach | Where it can help | Questions to weigh |
|---|---|---|
| Self-service portals and knowledge | Repeatable questions customers can solve using clear, current instructions. | Can customers find the right answer and complete the task? Who owns updates and accuracy? |
| Live chat and other digital support | Customers who need an interactive response without starting with a phone call. | Is the channel accessible to the intended customers? Can it resolve the issue or hand it to the right person? |
| Agent-assist AI | Helping a human agent locate information or handle routine parts of a case. | Is the knowledge reliable? Can agents review suggestions and retain control of judgment-heavy decisions? |
| AI agents | Automating defined service interactions when the task, information, and escalation path are suitable. | Can it resolve the request accurately? Is there a clear route to a person, and are security requirements met? |
| Phone and email support | Customers or cases that benefit from these established channels, including situations requiring detailed explanation or a durable written exchange. | Does the channel mix match customer needs and issue complexity? Digital-first forecasts do not establish that these channels should be eliminated. |
No cited survey establishes a universally best channel mix or guaranteed return on investment. A sound comparison considers customer effort and successful resolution, the complexity of cases and need for human judgment, channel availability, knowledge accuracy and maintenance effort, data and security readiness, agent workload and skills, and outcomes such as satisfaction, self-service completion, and operating efficiency.
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What service leaders can do next
- Map the issue mix. Separate repeatable questions from cases involving sensitive information, ambiguity, exceptions, or judgment. This clarifies which work might fit self-service or automation and which should reach an agent.
- Review the customer journey across channels. Identify where customers start, where they abandon self-service, and where they must repeat information when moving between digital and human support. Use this to assess the channel mix rather than assuming that more automation is automatically better.
- Strengthen the knowledge base. Assign ownership for reviewing customer-facing answers and internal guidance. Track gaps agents encounter so that recurring questions can be answered consistently by people and self-service tools.
- Set AI boundaries and escalation paths. Define the tasks an AI system may handle, what information it may use, when a person must review or take over, and how a customer can reach human support. Include security and governance in the design.
- Prepare agents for changing work. Build skills for complex resolution, judgment, communication, and knowledge improvement. Explain role changes clearly; survey-reported plans to add skills or shift roles do not make those transitions automatic.
- Evaluate outcomes together. Monitor resolution quality and customer satisfaction alongside speed, operating efficiency, and self-service completion. A change that improves one measure while making another worse needs to be understood before it is expanded.
Frequently Asked Questions
Will AI replace customer service agents?
The cited surveys do not establish that outcome. Gartner respondents reported plans to shift some agents into new roles and add skills, while Salesforce reported less time spent on routine cases by AI-using reps. These findings point to changing work, but they do not prove that all organizations will reduce agent roles or that every case can be automated.
Are AI agents already handling half of all service cases?
No. Salesforce’s 2025 State of Service survey reported respondents’ estimate that AI handled 30% of cases at the time, with 50% expected by 2027. The projected figure is not a measured current result, and the survey estimate is not an audited count.
Should a company remove phone or email support?
The Gartner survey found that leaders expected digital-first technologies to gain value relative to phone and email. It does not show that phone or email should be removed from any particular operation. The appropriate mix depends on customers, issue complexity, and whether each channel can deliver an effective resolution.
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What is the difference between agent assist and an AI agent?
Agent assist supports a human who remains involved in the case; an AI agent is intended to perform service tasks more autonomously. Gartner’s 2025 figure for agent-assist implementation was a forecast, while Salesforce’s 2026 finding concerned reported use of agentic AI. Those measures should not be treated as the same kind of adoption statistic.
What should a service team measure when it introduces more self-service or AI?
Measure whether customers complete tasks and get accurate resolutions, alongside customer satisfaction, operating efficiency, and agent workload. The cited sources do not establish a universal ROI or a single channel strategy that works for every team.
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