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AI can make customer service faster and more convenient, but a good experience still needs a clear route to a person. The strongest model is hybrid: let AI handle well-defined questions and actions it can complete reliably, while keeping human support easy to reach for complex, sensitive, consequential, or uncertain situations. For customers dealing with a bank, insurer, or other financial provider, that means automation should help with routine work—not become a barrier when the issue matters.
Customers want AI convenience and access to people
Customer preference is not a choice between AI and human service. In Gartner’s 2026 survey of 3,566 business-to-business (B2B) and business-to-consumer (B2C) customers, conducted in February and March, 50% said interactions are easier when companies use generative AI (GenAI). At the same time, 87% said it is essential for companies using GenAI in customer service to offer a way to reach a human agent.
Those results can coexist: AI may simplify a routine task, while a person remains important when the answer is unclear or the stakes are high. In a separate 2025 survey of 5,000 consumers across seven countries, fielded in October and November 2024, Verizon Business found that 47% cited being unable to speak or chat with a live human when needed as their top annoyance with automated interactions. These are survey findings, not guarantees about what every customer wants, but they point to a practical design principle: automation should not hide the human option.
“Service leaders should not use GenAI as a mandatory first step for every issue,” said Eric Keller, senior director analyst at Gartner, on August 4, 2026. A customer with a simple question may choose self-service; someone disputing a charge or trying to resolve a sensitive account problem should not have to fail repeatedly with a bot before reaching a person.
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Move from answering questions to completing tasks
AI service is moving beyond generating answers. Gartner’s 2026 customer survey found that 58% of customers who use GenAI had used it to complete a task on their behalf; among B2B customers, the figure was 74%. The reported examples include booking, ordering, submitting documents, managing subscriptions, and escalating a request. These figures describe surveyed users’ reported behavior, not the performance of every AI service.
For a financial provider, a conversational assistant might help a customer find the right process, collect the details needed for a request, or guide them through a routine account task. The system that actually changes an account or moves money should still apply the provider’s approved checks and permissions. A persuasive response is not proof that an action was completed.
Separate conversation from execution
A safer service design distinguishes between understanding what a customer wants and carrying out the consequential action. The conversational system can clarify intent, gather context, and explain what will happen. A reliable transaction or case-management system should execute only actions the customer is authorized to request, then confirm the result or explain what remains incomplete.
- State clearly which actions the assistant can and cannot perform.
- Confirm important details before an account change, payment, or submission.
- Show whether the request succeeded, failed, or needs a person to finish it.
- Provide a recovery path that preserves what the customer has already explained.
Make the human handoff visible and useful
A handoff is part of the service, not a failure to be concealed. Customers should be able to request a person without being forced through repeated unsuccessful prompts. When a conversation moves to an agent, pass along the customer’s stated goal, relevant steps already taken, and information they have agreed to share. That reduces the burden of starting over.
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Route to a person when the system is unsure, when an exception falls outside its approved scope, or when the customer signals distress or asks for human help. For financial matters, a consequential or disputed decision may call for human review even if AI can explain the usual process. The aim is not to automate every contact; it is to resolve the customer’s need through an appropriate channel.
Gartner’s 2025 survey of 4,879 customers, conducted in January and February, found that 51% said they would be willing to use a GenAI assistant for customer-service interactions on their behalf. That is stated willingness, not observed adoption, and it does not negate the later finding that customers want human access. Gartner’s Kathy Ross described the intended balance in June 2025: “A hybrid approach, where AI and human agents work in tandem, is the most effective strategy for delivering exceptional customer experiences.”
Design for journeys that start outside your own channels
Customers may begin looking for help in a third-party AI assistant rather than on a company’s website or chatbot. Gartner reported in 2026 that customers were approximately three times more likely to use third-party GenAI than company-provided chatbots for service issues. It also said third-party GenAI use had nearly doubled over the prior year, while company chatbot use had remained statistically unchanged since 2022.
That changes the service journey: a customer may first encounter a general AI tool, then need to reach a company’s secure system to discuss an account or complete an action. Keep official policies, product information, and service instructions accurate and accessible so customers—and AI tools drawing on public information—can find current guidance. Account-specific work still needs a secure route that verifies identity and authorization; general discovery is not a substitute for that control.
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The scale of this shift is uncertain across industries. Capgemini Research Institute’s 2026 study surveyed 9,500 consumers in 16 countries, as well as 1,200 executives and front-line staff. It found that 68% of organizations believed AI agents would outperform traditional CX channels, while 58% of consumers believed AI agents could save them time on routine monthly purchases and payments. These are beliefs reported by different respondent groups, not measured proof that AI agents outperform every channel.
Use AI to support the people delivering service
Customer-facing automation is only one use of AI. Agent-assist tools can summarize a conversation, surface relevant account information, or suggest a next step while a human representative remains responsible for the interaction. Used well, these tools can reduce the effort of searching for context and leave more attention for listening and judgment.
In Zendesk’s 2025 report, based on data collected in June and July 2024 across 22 countries, 73% of agents believed AI copilots would help them do their jobs better. That is agent-reported belief, not a measured productivity result. Verizon Business also described Exelon’s pilot of generative AI to give representatives timely information and call summaries; Verizon reported the pilot, but that account alone does not establish its impact.
For a representative handling a disputed payment or account-access problem, an assistant could bring relevant procedures into view while the representative confirms the customer’s situation and decides what to do. AI should support the employee’s work, not pressure them to follow a weak suggestion or prevent them from escalating an exception.
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Build privacy and trust into the service
Personalization may make help feel more relevant, but it also requires care with customer data. In Capgemini Research Institute’s 2026 survey, 83% of consumers said they were uncomfortable with AI recording personal data, compared with 38% of executives who said they were comfortable with it. The figures represent different respondent groups and measure different perspectives; they should not be read as a direct like-for-like test.
For services involving financial information, trust depends on more than a friendly tone. Customers need to know what information is being collected, why it is needed, and how it is used. Service teams should minimize collection, protect personal information, and avoid personalization that feels intrusive. Zendesk’s 2025 report found 64% of consumers said they were more likely to trust AI agents displaying traits such as friendliness and empathy, based on fieldwork in June and July 2024 across 22 countries. Warmth may help an interaction, but it cannot replace accuracy, clear limits, privacy protections, or a working human handoff.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Measure whether service actually gets better
Automation volume is not the same as customer value. A high share of contacts handled by AI could mean routine work is resolved efficiently—or that customers are being deflected and contacting the company again. Evaluate the result for customers and the operating cost of achieving it.
| What to measure | What it helps reveal |
|---|---|
| Task completion and accuracy | Whether the requested action was completed correctly, rather than merely answered in chat. |
| Repeat contacts and customer effort | Whether customers had to try again, switch channels, or explain the issue more than once. |
| Handoff success | Whether a customer who needs a person reaches one with useful context preserved. |
| Resolution quality and satisfaction | Whether the service solved the underlying need and met the customer’s expectations. |
| Privacy incidents and data handling | Whether the system’s use of customer information stays within the provider’s safeguards and expectations. |
| Total service cost | Whether any efficiency gains account for AI operating costs, human follow-up, and failed or repeated work. |
The financial case for AI should be demonstrated, not assumed. Gartner’s 2026 survey of 1,303 senior service and support leaders, fielded from January through April, found that only 24% reported positive financial returns across their AI use cases. Service leaders had invested a median 12% of their 2025 budget in AI. Those findings concern surveyed leaders and their reported use cases; they do not establish the return for a particular company or system.
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Workforce plans also warrant caution. In a March 2025 poll of 163 service leaders, Gartner found that 95% planned to retain human agents to strategically define AI’s role. Gartner also forecast that by 2027, 50% of organizations expecting significant service-workforce reductions would abandon those plans. That is a forecast, not a confirmed outcome. Verizon Business’s Daniel Lawson put the human-centered case this way in its 2025 release: “The future of CX isn’t about AI replacing humans; it’s about using AI to make human interactions better,”
What a practical hybrid model looks like
The precise balance depends on the task, channel, customer, and consequences of an error. A useful operating model assigns work according to reliability and the need for judgment rather than treating all contacts as candidates for the same automation.
| Service situation | AI’s role | Human role |
|---|---|---|
| Routine, well-defined request | Answer a common question or complete an approved, clearly scoped task; confirm the outcome. | Remain available if the customer prefers a person or the task does not complete. |
| Unclear intent or failed action | Ask a limited clarifying question or explain the failure without claiming success. | Review the context, resolve the exception, and decide on next steps. |
| Sensitive or consequential matter | Help gather context or find the right process within defined limits. | Provide judgment, reassurance, and any required review or decision. |
| Human-led conversation | Offer the representative summaries, relevant information, and suggestions. | Listen, apply judgment, and own the conversation and its resolution. |
When evaluating a service approach or platform, compare task completion and accuracy, exception handling, handoff quality, continuity across channels, agent-workflow fit, privacy controls, accessibility and language coverage, and total cost. The cited surveys describe customer preferences and category-level trends; they do not establish one universally superior vendor or product.
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