AI in customer experience: Use cases and results

AI in customer experience can reduce repeat work and speed up service. It makes a much bigger impact when it supports the full customer journey, rather than being limited to a standalone chatbot. An effective program connects customer context across channels, automates routine steps, and gives employees the information they need to resolve complex issues.

Floward‘s WhatsApp assistant moved containment from 64% to 81% and reduced peak staffing needs by 45%. That service result is one part of a broader customer journey. This guide follows AI across acquisition, onboarding, service, and retention. It explains what you need before launch and how to measure impact. It also lays out a path from a small pilot to a governed program.

AI in customer experience uses data and language models to make customer interactions more useful, timely, and consistent. It can support a customer before purchasing, guide onboarding, answer a service question, help a human agent, or identify a retention risk. AI works best when it connects to the channels customers use and the data behind each interaction. It also needs a human path for decisions that call for judgment.

AI in CX has three main forms. Predictive AI estimates what may happen next, such as a customer’s intent or risk of leaving. Generative AI writes or sums up content, such as an answer from approved sources. Agentic AI can plan and complete a set task across approved tools. These approaches can work together, but they aren’t interchangeable. A generated answer isn’t automatically a completed customer request, and a predicted intent isn’t permission to take action.

Map the work across four layers:

• Channels provide the places where an interaction starts and continues, such as Viber, SMS, RCS, voice, email, and web chat.
• Customer data gives AI key context, such as a customer’s identity, preferences, consent, order history, and place in the journey.
• AI classifies intent, retrieves approved information, generates a response, or performs a defined task.
• Human teams set policy, handle exceptions, review outcomes, and take over when automation reaches its limit.

These layers work as one system. AI needs current customer data and access to the channels customers use, while human teams need a clear handoff when a request requires judgment. A gap in any connection can leave customers with poor answers or no clear next step.

Customers expect service teams to remember what they’ve previously shared. Zendesk reports that 83% of surveyed consumers believe CX should be far better than it is today. The finding points to a broad gap between the experience customers get and the experience they expect.

Fast answers won’t earn trust on their own. Salesforce’s State of the Connected Customer report says 42% of customers trust businesses to use AI ethically, down from 58% in 2023. A customer may value a fast answer and still object to unclear disclosure, unnecessary data use, or an automated decision they can’t challenge.

Measure whether customers get the right result with less effort, alongside the number of contacts AI handles. Support leaders need to know whether automation reduces avoidable work without increasing repeat contacts. IT and data teams need to know whether identity, permissions, integrations, and audit logs are reliable. Legal and security teams need to know how the system uses personal data and how people can reach a human. Track these outcomes together rather than optimizing containment alone.

Choose a model after you define the task, its risk, and the data it can use. Many enterprise workflows combine models, with human-in-the-loop review for high-risk answers or actions.

Predictive models can classify a customer’s intent, estimate their next need, rank a case, or flag a customer who may leave. An intent score can route a message to the right queue. A retention team can use a risk score to decide who needs a human check-in. Treat scores as estimates, not facts. Test them across languages, channels, customer groups, and time periods, and let staff override them.

Generative AI can draft replies, summarize a conversation, retrieve a policy answer, translate content, or help an agent prepare a response. In customer-facing service, use retrieval from approved, current sources and test for hallucinations, privacy leakage, tone, and language quality. For employee-facing assistance, measure whether agents resolve issues accurately and spend less time searching, not just whether the model produces fluent text.

The Quarterly Journal of Economics article “Generative AI at Work” reports a field study that included 5179 customer-support agents. In the study, agents using generative AI resolved 14% more issues per hour on average. Gains were larger for new and lower-skilled agents, while experienced, highly skilled agents saw little change. The result makes agent assistance a useful place to test generative AI, with resolution quality measured alongside speed.

Agentic AI can read a request and use approved tools. It can check an order, update a service record, or book an appointment. Start with low-risk actions that are reversible, easy to audit, and limited by permission. Require confirmation before consequential changes, set clear stop conditions, and test how the agent behaves when data is missing or tools fail.

A chatbot can explain a refund policy. An AI agent may check eligibility, start a refund, and confirm the result. That workflow needs stronger controls because it changes the record. LAQO‘s insurance assistant is one example of customer-facing agentic AI. Its assistant handles 30% of customer queries, and 90% of queries are handled within three to five message exchanges. This example shows the value of bounded automation: the assistant handles routine queries and passes complex cases to human agents.

Voice AI can recognize speech, understand intent, respond, and route a call. It may suit high-volume, structured requests, but customers still need a clear path to a person. The system must handle accents, noise, interruptions, and sensitive topics.

Conversational analytics can classify themes, detect friction, and help teams review more interactions than manual sampling allows. Model-generated sentiment or AI-scored CSAT is a proxy, not the same as a customer survey response. Validate it against surveys and human review before using it as a performance measure. Keep transcript access, retention, and redaction rules aligned with privacy and security policy.

AI can help with the first product question through renewal. Choose each use case for a clear customer outcome and a channel that fits.

A campaign can use RCS or other messaging apps to answer product questions, collect a customer’s preferences, and route a qualified lead to sales. For example, Nissan Saudi Arabia used a 24/7 WhatsApp chatbot in Arabic and English to help customers explore car models, book test drives, and get service reminders. This brought about a 138% increase in leads. In a separate seven-week Nissan Patrol campaign, it used WhatsApp carousel messages integrated with Nissan’s CRM and achieved a 26% conversion rate from first interaction to full lead. This story shows why campaigns should make the next step clear and offer a person when a customer needs negotiation or advice.

A conversational flow can ask a small number of qualifying questions, check whether a product fits, and book a callback or appointment. At Ibancar, 60% of valid leads progressed without agent intervention. Keep the flow short, explain why information is needed, and let a customer correct an answer before it reaches sales.

After signing up, an assistant can guide customers through setup, collect missing information, and explain the next step over web chat or a messaging channel. Use customer data to avoid asking for information already supplied, but don’t assume that an incomplete profile is permission to collect more. Track completion, time to first value, and requests for human help so the team can see whether the flow removes friction or only shifts it.

A reminder can help a customer complete a booking, prepare for an appointment, or take the next step in a service journey. Rich messaging can include clear actions, while SMS can reach customers on basic devices. Be sure to respect channel consent and keep reminders relevant.

A service assistant can answer approved questions about policies, orders, or account steps in web chat or messaging apps. Use a knowledge base with an owner and review date, and show the source or next action where useful. If an answer is uncertain, ask a clarifying question or escalate rather than inventing details.

Intent detection can send a request to a team with the right skills, while a human agent receives the conversation history and relevant customer context. At Sparkasse Bank BiH, first-contact resolution improved by 17% after the bank unified voice and web chat in its contact center.

Payment reminders and collections messages can explain an amount due, show approved payment options, and answer common questions without forcing a customer to call. At UME, an RCS chatbot resolved 80% of conversations without human assistance, while collection costs were 54% lower. Route hardship requests and disputes to a person. Don’t let an AI estimate of a customer’s ability to pay override your approved collections policy.

An assistant can respond in a customer’s preferred language, translate a handoff summary, or direct a request to a language-qualified agent. Mukuru, offers 10 language options for their messaging app chatbot which assists in account creation, money transfers, and bill payments. Test meaning and policy accuracy with fluent reviewers rather than relying on literal translation scores alone.

A system can summarize calls, classify outcomes, draft case notes, and help agents complete follow-up work. The goal is to reduce documentation time while preserving accurate, complete records.

Predictive signals can help a team identify a customer who may need a check-in, while messaging can provide a relevant offer or service option. Keep the decision explainable and avoid sending an incentive based only on a model score. At Flamingo, the WhatsApp chatbot handles balance checks, tax certificate requests, store information, and credit questions, then passes unresolved chats to live agents. Customers can self-serve routine requests, leaving agents free to focus on more complex queries.

Map each customer problem to the channel, data, handoff, and controls it needs. A readiness review shows what must be fixed before launching.

Start with the channels customers already use and the jobs they expect to complete there. Check consent, message delivery, templates, backup paths, service hours, access needs, and who owns each channel. A RCS experience may need a different identity check and escalation path from web chat or voice. Don’t assume one module supports every channel, or that every deployment has the same availability.

AgentOS supports deployment across 15+ channels, and Infobip’s network reaches 190+ countries. For enterprises serving customers across markets, that breadth gives teams more options to meet customers on channels they already use.

AI needs enough context to answer the current request, but it shouldn’t receive unrestricted access to every customer’s record. Define the minimum fields needed for each use case, how identity is verified, which system is authoritative, and how consent and preferences are honored. Agree how fresh data must be, what happens when records conflict, and who can approve changes to the model’s knowledge source.

The Conversational CDP is an AgentOS module that unifies customer interactions into real-time profiles. AI agents and service teams can use that context to continue a conversation across channels, rather than treating each interaction as a fresh start.

Design the escalation before writing the chatbot flow. Decide which intents, confidence levels, sentiment signals, or policy exceptions require a person. At handoff, preserve the transcript, verified customer profile, and session data needed to continue without making the customer repeat the request.

Make the transfer visible to the customer, set expectations for wait time, and give agents a way to correct the AI’s classification. Don’t assume that AI suggestions or recommended next actions transfer to a human agent unless the product owner confirms that capability.

Set an owner for the model, approved knowledge, data access, and incident response. Define which use cases are low, medium, or high impact, then match review intensity to the risk. Log prompts, retrieved sources, tool calls, decisions, and human overrides where permitted. Test for inaccurate answers, prompt injection, data exposure, bias, and failure under peak traffic. Give customers an accessible route to challenge an outcome.

Set a baseline before launching and measure the customer outcome, operational outcome, and guardrails together. Compare equivalent intent, channel, customer group, and time period. If the team changes the process during a pilot, record the change so that the AI’s contribution isn’t confused with staffing, policy, or campaign effects.

Define terms before comparing results.

• Containment means the interaction stayed in the automated channel.
• Automated resolution means the customer’s request was completed and verified without a human.
• Deflection means a customer used self-service instead of contacting an agent.

Count it as a success only if the issue was solved and the customer didn’t need to try again. A high containment rate can coexist with poor resolution, repeat contacts, or silent customer abandonment.

Build an ROI model around verified resolutions, not chatbot sessions. Compare the full cost of setup, integrations, model use, knowledge updates, monitoring, human review, and staff training with measurable gains, such as fewer repeat contacts, shorter handling time, more completed requests, or added revenue. Where practical, use a holdout group or phased rollout to test whether the AI drove those gains. BCG’s 10/20/70 rule frames AI transformation effort as 10% algorithms, 20% technology and data, and 70% people and processes. The split is a reminder to include training and workflow changes in ROI estimates, not just technical spend. Treat it as a planning prompt, not a budget formula; base final ROI on actual costs and results.

At the end of each phase, review the results and confirm that the team is ready before expanding.

Weeks 1–4. Choose one high-volume, low-risk customer problem and map the current journey from first contact to verified resolution. Audit the channel, knowledge source, identity flow, data access, consent, escalation route, and baseline KPIs. Name a business owner, technical owner, and human-operations owner. Define what the AI may do, what requires confirmation, and what must always go to a person.

Before moving on: Confirm the source of truth is approved, handoff has an owner, and the team can measure success and guardrails. Get Legal or Compliance review when needed.

Weeks 5–8. Launch to a controlled segment on one channel. Start with retrieval, classification, or a reversible action before allowing higher-impact transactions. Review transcripts and failure cases daily at first, then at an agreed cadence. Compare outcomes with the baseline and, where feasible, a holdout group. Track containment beside verified resolution, repeat contact, customer effort, escalation, and cost.

Before moving on: Expand only if resolution improves or stays stable; guardrails remain within limits, and human teams can handle the escalation volume. Pause or roll back if errors, complaints, or privacy incidents exceed agreed limits.

Weeks 9–12. Extend a proven workflow to a second channel only after adapting consent, identity, content, and escalation for that channel. Then test proactive reminders, onboarding nudges, or service updates with clear customer benefit. Keep a shared view of the journey so customers don’t have to restart when they move between channels. Compare performance by channel and customer segment before combining results.

Before moving on: Confirm channel-specific quality, consent, and operational ownership before expanding. Don’t use a strong result on one channel as proof that the same design will work everywhere.

Quarter 2 and beyond. Add an agentic task only when its scope and access are clear. Errors must be reversible or recoverable. Keep audit logs. Test voice and additional languages with fluent reviewers and clear human fallback. Confirm the product release status before planning a deployment that depends on Early Access capabilities.

Before moving on: Expand autonomy only after the agent works across edge cases; customers understand the interaction, and owners can monitor and stop the workflow. Keep humans accountable for policy, exceptions, and customer-impacting decisions.

Enterprise teams are moving from chatbot launch counts toward a question that matters to customers: did the system complete the request correctly? Gartner forecasts that agentic AI could autonomously resolve 80% of common service issues by 2029. For service teams, that means designing routine workflows the AI can complete, and a clear handoff for exceptions.

Some customers use generative AI to research products before buying them. BCG reports that 22% of GenAI adopters have done this and that 48% of consumers are excited about GenAI’s effect on the buying experience.

In the NBER study “Generative AI at Work”, productivity gains varied by agent experience. AI can help an agent find information, draft a response, or summarize a case. Teams should measure accuracy and customer outcomes by group, then focus on training and review tasks where people need support.

Customers may move between web chat, SMS, RCS, and voice based on urgency, device, and task. A high open rate means little if customers don’t complete the task. Track delivery, response, completion, and escalation separately.

Trust depends on what an AI system does with customer data and whether people know when they’re interacting with one. Article 50 of the EU AI Act includes a transparency requirement for certain AI systems that interact directly with people. It applies from August 2nd, 2026. Plan for clear disclosure, limited data use, human help, and audit logs while you design the system.

Meeting these trends calls for a way to coordinate channels, data, AI, and human support.

AgentOS is Infobip’s platform to connect customer channels, data, AI, and human service.

Use this table to map the four layers to AgentOS capabilities.

For each use case, ask four questions. Which channel fits? What data does the AI need? Which tasks may it do? When must a person take over? Those answers give your team a practical starting point for a focused pilot.