As part of the current AI trend, many teams are adding AI capabilities to existing solutions without thinking carefully enough about how the interaction between user and AI should work.

This is where AI features often fail today, and where many more will fail as AI becomes more embedded in products. Not because the model cannot produce an answer, but because users do not know when to use it, do not trust the output, or have to leave their workflow to get value from it.

Done poorly, AI becomes another feature people try once and ignore.

This is a product design problem.

The shift: AI is moving from answering to acting

The first wave of AI in products was mostly conversational. Users typed a question, the model answered, and the interaction ended there. The product design question was mostly: what should the chatbot say?

That has changed. AI is increasingly able to read context, work with product data, fill in forms, update records, and complete multi-step tasks.

Example: A sales rep updating a pipeline may not need an AI chat window. They may need AI to summarize the last call, pull relevant deal data, prepare the follow-up, suggest the next step, and update the CRM after approval.

Some of those actions are safe to automate. Some should be reviewed first. Some should never happen without explicit confirmation.

The question is no longer only “what should AI say?” It is: what should AI be allowed to do, and how does the user stay in control?

That is now a product decision.

Product decisions behind every useful AI experience

There are several product decisions that need to be made before any development of AI features takes place.

The most important ones are:

1. What role should AI play?
2. Where should AI appear in the workflow?
3. How much should AI be allowed to do?
4. How should AI behave when it is uncertain, wrong, or blocked?
5. How will users verify, control, and correct it?

These sound simple, but they determine whether AI becomes something users rely on and trust or something they try once and ignore.

This article assumes the team has already identified an AI opportunity worth solving. We covered that step in a separate guide.

Decide what role AI plays

The first design decision is what the AI is actually there to do. There are several useful roles to choose from, each implying a different relationship with the user:

• Assistant. Helps the user complete a task they would have done themselves.
• Advisor. Gives guidance or decision support when the user is uncertain.
• Recommender. Surfaces a next-best option from a larger set.
• Generator. Produces a draft, summary, or first version the user refines.
• Evaluator. Reviews, scores, or flags issues.
• Copilot. Works with the user across a whole workflow, not just one step.
• Background intelligence. Improves the experience without direct interaction at all.

The same situation can support several different roles.

Example: A real estate agent matching a buyer with the right property may need AI to play several roles in one flow. It can act as an Assistant by helping search in natural language, a Recommender by suggesting related matches or next steps, and an Advisor by explaining why a specific listing fits the buyer’s needs.

This example perfectly illustrates home in the same workflow, different AI roles can exist. Each one needs a different interface. Each one with a different purpose, and different behavior.

Choose the right interaction pattern

Once the role of AI is clear, the next question is how the user should interact with it.

AI does not always need to appear as chat. Conversation can be useful when users are exploring, comparing, or deciding. But when they are completing a known task, reviewing records, or moving a process forward, chat can create extra work.

The right pattern depends on the job the user is trying to do.

If the user is exploring options, a conversational advisor may work well. If they are completing a known task, inline assistance or a pre-filled draft may be better. If they are reviewing many records, ranking, summaries, or smart warnings may be more useful than a chat window.

The table below outlines common AI interaction patterns and when to apply each one.

Pattern	Appearing to the user as	Works best for	Be careful that…
Invisible intelligence	The product gets smarter in the background	Ranking, fraud detection, prioritization, personalization	Users may not understand why something changed or was recommended
Inline assistance	AI helps inside the current task	Autocomplete, smart suggestions, form help, summaries	Suggestions can interrupt the workflow or feel too generic
Contextual advisor	AI explains, compares, or helps the user decide	Product advisors, onboarding, decision support, complex choices	Users may not trust the advice if the reasoning is not clear
Embedded copilot	AI works alongside the user across part of a workflow	Drafting, editing, analyzing, configuring, reviewing	It can become another side panel instead of being part of the work
Workflow agent	AI prepares or completes multi-step tasks with approval	Scheduling, CRM updates, order flows, support processes	Users can lose trust if status, permissions, or approvals are unclear
Ambient agent	AI monitors context and acts within defined limits	Operations monitoring, recurring admin, alerts, exception handling	It can feel intrusive, unsafe, or overconfident if boundaries are unclear

Autonomy: How much AI can do on its own

This is one of the most important product decisions in any AI experience.

A useful way to think about it is as an autonomy ladder:

Example: In a procurement platform, AI might automatically categorize suppliers, suggest which offers look strongest, draft a comparison summary, prepare a purchase order, and then stop before sending it for approval. The user does not need to control every small step, but they do need to control the decision and the action that creates commitment.

Match autonomy to risk.

Low-risk actions, such as sorting or summarizing, can often happen automatically. Medium-risk actions, such as drafting an email or recommending a product, usually need review. High-risk actions, such as sending a message, changing account data, or submitting a decision, need preview and confirmation. Critical workflows need strong human control and audit trails.

The mistake is giving AI more autonomy than the user is ready to trust. Most AI products should start lower on the ladder and earn their way up.

Designing how AI behaves

AI behavior is not only about how the system talks.

It is about how AI operates inside the product: when it appears, when it stays silent, what it notices, how it prioritizes, when it asks for input, when it uses different tools, and what happens when it reaches a limit.

This needs to be defined before the feature is built.

Product teams should be clear on questions like:

• Should AI appear proactively or only when asked?
• Should it give one recommendation or several options?
• Should it ask for clarification or make an assumption?
• Should it explain every suggestion or only risky ones?
• Should it remember user corrections?
• Should it escalate to a human when confidence is low?

The behavior is shaped by prompts, interface rules, permissions, data access, workflow logic, feedback loops, and fallback paths.

If the team does not define it, the behavior still exists. It is just defined by default, by the model, by rushed prompts, or by implementation constraints.

Design for trust, transparency, and control

Trust is what makes adoption happen. People do not rely on something they cannot verify. This is especially true in enterprise software, where users are responsible for decisions, customers, data, approvals, or operational outcomes.

To trust an AI feature, users generally need to see:

• What the AI is doing
• What data or sources it used
• How confident it is in the output
• What they can edit, approve, or reject
• How mistakes get corrected
• What they remain responsible for

This is where many AI features fail. The output looks good. The user doesn’t know where it came from, can’t tell how confident the system is, and has no clear way to push back. So they stop relying on it. Sometimes they stop using it altogether.

Trust does not come from making AI sound more confident. It comes from making the system easier to inspect, control, and recover from when it is wrong.

Prototype and validate before building

These decisions are much cheaper to make in a prototype than after launch. Once AI is connected to workflows, systems, permissions, and data, changing the experience becomes harder.

That is why we prototype AI experiences before full development: to test whether users understand the role, trust the output, and feel in control before the technical investment becomes too heavy to change.

Before building, validate whether users understand what AI is doing, trust the output enough to act on it, and feel that it fits naturally into their workflow.

After launch, the metric is not whether users clicked the AI feature. It is whether AI helped them make progress: task completion, time saved, correction rate, approval rate, user confidence, and the business outcome the feature was supposed to improve.

A key takeaway 

Designing an AI experience is not about adding intelligence to an existing interface.

It is about designing the relationship between the user and the system: what AI knows, what it can do, when it asks, when it acts, how it explains itself, and how the user stays in control.

For many teams, this is the missing layer: not AI implementation, but AI experience design.

That is the difference between adding AI to a product and designing an AI product experience people actually trust and use.