What Are AI Interaction Flows and Why Every AI Feature Needs Them Before Dev Begins?

When a SaaS team designs a traditional feature, the interaction flow is straightforward: user clicks a button, system performs an action, result appears. The interface is deterministic, the same input produces the same output every time. When a SaaS team adds an AI feature, this assumption breaks down immediately. The AI does not produce the same output every time. It may be confident or uncertain. Correct or partially wrong. It may take two seconds or eight. It may decline to answer. It may produce output the user did not expect. Each of these states needs to be designed, not assumed away. AI interaction flows are the UX design artefact that maps these states before development begins. At Inity Agency, AI interaction flows are a mandatory deliverable in every AI feature design engagement — because the teams that skip them consistently ship AI features that lose user trust on the first edge case.

Why Traditional UX Flows Are Not Enough for AI Features

A traditional UX flow answers: what happens when the user does X? The answer is deterministic. Click “Save” → record is saved → success toast appears. The same input produces the same output every time. The designer can enumerate every possible state because the system is rule-based.

An AI interaction flow must answer a different question: what happens when the user asks the AI to do X, and the AI produces an output that may be any of the following?

• Confident and correct – the happy path most AI UX designs for
• Confident and incorrect – the state at which most users stop trusting the AI after encountering
• Uncertain – the AI knows it does not have enough information to answer well
• Out of scope – the query is outside what the model is designed or allowed to handle
• Generating slowly – the model is taking longer than expected
• Failed – the model API is unavailable, rate-limited, or has returned an error
• Filtered – the output violated a safety policy and was blocked

Each of these states is a UX problem that needs a designed solution. The interface that shows only the confident/correct state and fails silently on all others will lose user trust the first time a user encounters an edge case, which, in any actively used AI feature, will happen within the first week.

The 8 States Every AI Interaction Flow Must Design

1. Initiation State

How does the user start the AI interaction? This ranges from explicit (a “Generate” button, a chat input, a “Summarise this” action) to implicit (the AI automatically processes a document when it is uploaded, or surfaces a recommendation without the user requesting it).

Initiation design questions:

• Is the AI feature opt-in or always-on? Opt-in features require clear affordances. Always-on features require clear indicators that AI is active.
• What user input does the AI need before it can begin? Does it need to ask the user a clarifying question?
• What feedback does the user receive at the moment of initiation to confirm the AI has received their request?

2. Loading and Generating State

While the AI is working, the interface must communicate that work is happening. AI generation is slower and more variable than database queries; a response may arrive in 1 second or 10 seconds, and the user has no visibility into why.

Loading state design considerations:

• A skeleton or progress indicator that communicates structure is coming, not just a generic spinner
• For long-running generations, a streaming output pattern, showing text as it generates rather than waiting for the full response, dramatically improves perceived performance and keeps users engaged
• For very slow responses, a status update that acknowledges the delay without alarming the user

3. Output State

How the AI output is presented is the most consequential design decision in the interaction flow. AI output that looks identical to human-generated content creates a dangerous implication: the user may trust it more than it deserves.

Output presentation design questions:

• Is there a visual distinction between AI-generated content and user-created content? (colour treatment, AI badge, border style)
• How is the output structured to make it scannable and actionable, not just readable?
• What information accompanies the output: source citations, the data it was based on, or a confidence indicator?
• Can the user see more details about how the output was generated if they want it?

4. Confidence Indicator

Not all AI outputs are equally reliable. A recommendation based on rich, recent data is different from a recommendation based on sparse or outdated data. The interface should communicate this difference.

Confidence indicator design options:

• Explicit confidence scores (“Based on 45 data points – high confidence”)
• Data freshness indicators (“Based on data from the last 30 days”)
• Hedging language in the output itself (“This is an estimate based on available data, results may vary”)
• Visual confidence signals (colour-coded confidence levels, warning icons for low-confidence outputs)

5. Correction and Override Mechanism

Every AI output must have a mechanism for the user to disagree and take a different action. This is not an admission that the AI is unreliable, it is what makes the AI collaborative rather than authoritative. Users who cannot override AI outputs stop using them.

Override design patterns:

• Editable output – the AI produces a draft that the user can edit directly
• Explicit reject – a “That’s not right” button that removes the output and allows the user to try again or provide better input
• Alternative selection – the AI generates multiple options and the user selects the best one
• Manual fallback – a clear path to completing the task without the AI if the output is consistently unhelpful

6. Feedback Mechanism

User feedback on AI outputs serves two purposes: immediate UX (users feel heard and in control) and long-term model improvement (feedback becomes training signal). Both matter.

Feedback design options:

• Thumbs up/down on individual outputs
• Accuracy ratings (1–5 stars, helpful/not helpful)
• “Flag as incorrect” with optional explanation
• Implicit feedback tracking, monitoring which AI-generated suggestions the user accepts, edits, or ignores

The feedback mechanism should be low-friction, one click, not a form, and should visually confirm that the feedback was received.

7. Error State

AI features have more failure modes than traditional features. Each failure mode needs a distinct, designed error state — not a generic “Something went wrong” message.

Common AI error states and how to design them:

• Model unavailable – “AI features are temporarily unavailable. You can complete this manually.” Provide the manual fallback immediately.
• Rate limit exceeded – “You’ve reached the AI usage limit for today. [Upgrade / Try again tomorrow].” Be specific about why and what to do.
• Safety filter triggered – “This request could not be completed. [Brief explanation without revealing filter details.]”
• Low confidence – “The AI doesn’t have enough data to generate a reliable response for this. [Suggest what additional information would help.]”
• Timeout – “This took longer than expected. [Retry / Cancel].” Do not leave the user wondering.

8. Escalation Pathway

For AI features operating in consequential contexts, health decisions, financial recommendations, compliance determinations, there must be a clear pathway to human review when the user is not confident in the AI’s output.

Escalation design considerations:

• When should escalation be offered? (Low confidence score, high-stakes action, user explicitly requests it)
• What form does escalation take? (Contact support, flag for human review, defer action until a human confirms)
• How does the user know their escalation has been received and will be acted on?

AI Interaction Flow vs Traditional UX Flow: The Key Differences

How Inity Designs AI Interaction Flows

At Inity, AI interaction flows are produced as part of the Feature Design phase in every AI development engagement. They are delivered as annotated Figma flows — the same format as traditional UX flows, but with each AI-specific state documented alongside the standard states.

The AI interaction flow is the primary brief for the front-end implementation of every AI feature. It specifies what the interface must communicate in every state, what the user can do in each state, and what triggers transitions between states. Development builds from this flow, not from an assumption that the confident/correct happy path is the only case that matters.

Conclusion

AI interaction flows are not a luxury deliverable for well-resourced product teams. They are the minimum viable design artefact for any AI feature that will be used by real users in a real product. The eight states, initiation, loading, output, confidence, correction, feedback, error, and escalation — are not edge cases. They are all encountered by real users in the first week of any actively used AI feature. Designing them before development begins is what separates AI features that build user trust from AI features that destroy it.

→ Adding AI features to your product and want to get the UX right first? Inity designs AI interaction flows as part of every AI feature engagement. Book a call.