01
People cannot tell when AI is involved
Generated responses, recommendations, summaries, routing, or automation shape the interaction without making the system’s role clear.
Product · AI Experience
A confident answer is not the same as a trustworthy experience.
An AI assistant gives a polished answer based on an old policy. The customer acts on it, reaches a human, and has to explain everything again. The output looked good. The experience failed. AI Experience designs what people should expect, what the system can do, where human judgment belongs, and how the organization recovers when AI gets it wrong.
Where it breaks
AI friction starts when people cannot judge the system.
People need more than a fast answer. They need to understand why AI is involved, what it knows, what it can do, and where accountable human help begins.
02
The answer sounds more certain than the evidence
Fluent language hides old sources, missing context, conflicting records, inference, or uncertainty that could change the decision.
03
AI stands between the customer and real help
People must repeat themselves, prove the system failed, or navigate another loop before reaching someone who can take responsibility.
What teams receive
Leave with an AI experience teams can own.
The outputs define the intended human relationship, the complete service path, the required controls, and how the organization will evaluate real use.
01
AI role and experience rules
The human need, intended benefit, AI role, exclusions, interaction rules, decision boundaries, accountable owner, and implications for delivery.
02
End-to-end AI service blueprint
The visible journey connected to data, models, tools, workflows, employees, policies, handoffs, exceptions, recovery, and operating dependencies.
03
Interaction and handoff requirements
Disclosure, context, evidence, uncertainty, controls, confirmation, accessibility, escalation, human support, continuity, and recovery behavior.
04
Evaluation and governance plan
Representative cases, quality criteria, experience measures, outcomes, review behavior, monitoring, complaints, incident triggers, owners, and review cadence.
How the experience moves
Follow the person from first expectation through recovery.
Test the complete situation, not just the AI response. The experience includes what happens before, during, and after the model produces an output.
01
Expect
Does the person know what AI will do?
Need, channel, prior relationship, disclosure, AI role, expected benefit, alternatives, urgency, accessibility, and known limits.
02
Interact
Can the person make sense of the result?
Response, choices, evidence, citations, uncertainty, correction, confirmation, refusal, accessibility, language, and interface behavior.
03
Recover
What happens when AI is wrong?
Escalation, context transfer, complaint, correction, remediation, human support, incident response, customer outcome, and product learning.
What to examine
Measure what people experience and what the system causes.
A strong model score cannot prove the experience works. Evidence should connect the interaction to the data, workflow, human decisions, and real outcomes around it.
Human needs and expectations
Research, goals, context, mental models, trust, accessibility, language, prior experience, vulnerability, and the cost of misunderstanding.
Sources and model behavior
Data, retrieval, provenance, prompts, models, tools, variation, accuracy, groundedness, latency, refusals, limits, and prohibited outputs.
Customer and operating outcomes
Task success, effort, trust, access, fairness, service quality, resolution, capacity, rework, complaints, retention, risk, and unintended harm.
Trust should match the evidence
The goal is not to make AI feel human or earn blind trust. People should have enough information and control to judge the system’s role, limits, evidence, and the accountable organization behind it.
When it fits
Use AI Experience when AI changes a meaningful interaction.
This work fits when AI shapes how a customer, employee, patient, member, citizen, or partner gets information, makes a decision, completes a task, or receives support. It is not the right fit for a demo or an automation target that ignores accountability and recovery.
Start a fit checkRelated paths
Choose the next step based on what is still unresolved.
AI Service Readiness Review
A use case needs a readiness decision across workflow, data, human oversight, safeguards, adoption, measurement, and implementation conditions.
Explore AI Service Readiness ReviewExperience Foundations
Teams need shared customer, journey, ownership, service, and measurement decisions before defining AI’s role in the broader experience.
Explore Experience FoundationsAgent Copilots
The experience supports an employee or specialist with context, recommendations, bounded actions, and accountable human judgment.
Explore Agent Copilots