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How Can AI Improve Customer Experience? What the 2026 Customer Data Actually Shows
Half of customers now say their service interactions are easier when a company uses generative AI. In the same Gartner survey of 3,566 customers, 87% said an option to reach a human agent is essential.
Both findings hold, and the distance between them answers the question in the title.
“How can AI improve customer experience?”
By resolving what it can resolve with confidence, supporting agents on everything else, and never standing between a customer and a person. When AI does the opposite, customers notice fast: only 27% would try a chatbot again after a bad experience.
This post covers where the evidence shows AI improving CX, where it makes the experience worse, and what to decide before adding AI to any customer journey. It draws on CCaaS and conversational AI work across NiCE CXone, Genesys, Five9, Zoom Contact Center, and Amazon Connect, in environments we implement and then stay to manage.
Where AI Measurably Improves Customer Experience
The strongest evidence sits in 4 places. None of them is "replace the contact center with a bot."
The common thread: AI improves the experience when it is scoped to what it does reliably and connected to the systems that let it act. The model matters less than the integration and the handoff design around it.
See how we approach AI implementation →
Where AI Makes Customer Experience Worse
The failures are just as consistent, and customers remember them longer than the wins.
The same pattern shows up in customer intelligence programs: narrow scope on clean data outperforms broad promises on data that is not ready.
5 AI Use Cases That Improve Customer Experience
Each of these maps to the evidence above: bounded scope, a clear outcome, and a person within reach.
1. Virtual agents for high-confidence, transactional requests. Conversational AI platforms such as NiCE Cognigy can authenticate a caller, read account data, and complete the transaction. Start with requests that already have a clear end state: payment, order status, appointment changes. Expand only when resolution rates hold.
In production: Ferrellgas.
Ferrellgas's contact center was running at 90% call abandonment, with 10 to 15 minute waits and no self-service for payments or routine account questions. We deployed Cognigy across its NiCE CX environment to classify and route calls, complete payments without an agent, and give agents real-time context on every escalation. The AI-to-agent handoff was designed so callers hear no transition. Call abandonment fell from 90% to 30%, and AI-handled payments rose 200%.
Read the Ferrellgas case study →
2. Handoffs that carry context. When the AI reaches its limit, it should route to the right queue and pass the transcript, intent, and account details. The agent starts mid-conversation, not from zero. Customers weigh this more heavily than the model behind it.
See how we design AI-to-agent handoffs →
3. Real-time agent assist. AI surfaces the relevant policy, next step, or compliance prompt during the call. The research gains are largest for newer agents, so this matters most in high-turnover or fast-growing teams.
4. Automated after-call work. AI drafts the summary, sets the disposition, and updates the CRM. Records stay complete, and the next customer waits less.
5. Quality analysis across every interaction. Manual QA reviews a sample. AI can score every call and chat against the same criteria, so coaching and process fixes come from the full picture rather than a handful of recordings.
Three Questions to Answer Before Adding AI to a Customer Journey
- What does resolved mean for this interaction, and how often do agents achieve it today?
If the human resolution rate is unknown, there is no baseline to judge the AI against. If the answer is "handle everything," the scope is not ready. - Can the AI see and act on what the customer needs?
A virtual agent that cannot read the order system will give a confident answer it cannot back up. Integration between CCaaS, CRM, and back-office systems decides more outcomes than the model does. Our agentic AI analysis covers the governance side of this in more depth.
In production: IBM.
IBM needed Watson AI to handle more than 75,000 healthcare inquiries a month through NiCE CXone, and no integration between the two existed. We engineered it from scratch, so the AI could work inside the same contact center environment as the agents it supports. The integration had to exist before the automation could.
- How many steps stand between the customer and a person?
Count them in the actual journey, not the design document. If the AI cannot resolve with confidence, the handoff should be one step, visible, and carry everything already collected.
In production: Tri Counties Bank.
Tri Counties Bank serves a bilingual customer base that includes many older callers. We built a custom Omilia and NiCE CXone integration, engineered around licensing constraints, that delivers voice biometric authentication in English and Spanish. It includes a keypad fallback for callers who prefer it and clean transitions from AI to agent. The bank replaced SMS-based two-factor authentication with voice biometrics without replatforming.
Read the Tri Counties Bank case study →
Where to Start
Pick 1 or 2 interaction types with high volume, a clear end state, and data the AI can already reach. Put agent assist in front of your newest agents first, where the research shows the largest gains. Measure resolution, repeat contact, and escalation rate alongside containment, so the dashboard cannot report success while customers call back.
Then plan for the months after go-live. Intents drift, policies change, and a virtual agent that no one owns falls out of step with the business within months. That is why AI belongs in a managed operation, not a one-time launch.
