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Start with a real user goal, not an AI feature. An AI product is worth building when it helps people accomplish a task in a way that simpler alternatives do not. From there, decide which work AI should handle, what users need to understand or control, how they can recover from errors, and how you will evaluate the complete experience.
1. Define the user, task, and desired outcome
Describe who is trying to do what, where the task happens, and what constraints shape it. Include people affected by the product—not only the buyer or account holder. Then define success as a human outcome: for example, completing a task accurately with less effort, rather than merely receiving an AI-generated response.
NIST’s human-centered design principles emphasize explicitly understanding users, tasks, and environments, and drawing on multidisciplinary perspectives. The page quotes ISO 9241-210:2010(E): “The design is based upon an explicit understanding of users, tasks, and environments.” NIST: Human Centered Design
2. Check whether AI adds distinct value
Identify the part of the task AI could improve and why it is better suited to that part than a simpler interface, rule, or non-AI process. Google PAIR frames the opportunity as the intersection of user needs and AI strengths, and asks teams to consider downstream effects when defining success.
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As the Google PAIR guidebook chapter “User Needs + Defining Success” puts it: “Even the best AI will fail if it doesn’t provide unique value to users.” That is a useful test before committing to an AI feature: if the user benefit is hard to name, the technology alone is not a reason to redesign the workflow. Google PAIR Guidebook
3. Decide what AI does—and what stays with the user
Map the task and choose where AI should suggest, draft, rank, summarize, or take action. Then decide which steps need user review, which can be automated, and what control users need. Ask whether people want the task done for them, help doing it, or a faster way to do it themselves. There is no universally correct balance; it depends on the task and should be evaluated with users.
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For each AI-supported step, make the division of work clear in the interaction. A suggestion that needs review should not feel like a completed action. A system that acts should give users a suitable way to understand what happened and, where the task allows, correct or reverse it. Google PAIR treats automation versus augmentation as a core design decision, not a default setting. Google PAIR Guidebook
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Users need enough information to understand the AI’s role and relevant limitations in context. Do not let a polished tone or confident presentation stand in for reliability. Give people an appropriate way to inspect, correct, reject, or recover from outputs when the consequences of the task call for it.
Trust is better treated as calibrated understanding than as a target of persuasion: people should be able to tell what the system is doing and make informed choices about relying on it. Google PAIR covers trust, explanation, and automation; Microsoft’s HAX Toolkit organizes guidance around the interaction over time, including first use, ordinary use, errors, and changes. Google PAIR Guidebook · Microsoft HAX Toolkit
5. Plan for errors and recovery before polishing
For a language-based interface, list likely ways the interaction can go wrong and prototype what users can do next. Include ambiguity, missing context, incorrect output, unexpected changes, and unsupported intent. Consider what the user sees, how they can clarify or correct the request, and when the product should make it easy to stop or try another path.
Microsoft HAX includes a playbook for anticipating natural-language failures and planning recovery, alongside design guidance and a workbook for prioritizing relevant guidelines. Use these resources to prompt concrete scenarios, not as a substitute for judgment about your product’s users and risks. Microsoft HAX Toolkit
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Involve users throughout design and development. Test proposed interactions early, observe whether people can complete the task, and refine the experience based on what happens. Evaluate the outcome and the surrounding workflow—not just whether an AI response sounds plausible or whether a feature was used.
NIST’s human-centered design activities include understanding context, specifying requirements, designing solutions, and evaluating them; evaluation can begin in early stages. This makes iteration part of the design process rather than a final check after implementation. NIST: Human Centered Design
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use frameworks as prompts, not recipes
Several resources can help teams structure decisions, but none guarantees adoption or tells you in advance which design will fit a particular product.
| Resource | Useful for | What it does not establish |
|---|---|---|
| NIST human-centered design | Framing users, tasks, environments, requirements, solution design, and evaluation. | A universal recipe for product-market fit. |
| Google PAIR Guidebook | Working through user needs, datasets, trust, onboarding, explanation, automation and augmentation, and failure support. | Validation of any particular product or feature. |
| Microsoft HAX Toolkit | Applying interaction guidelines, prioritizing them with a workbook, and planning failure scenarios for natural-language systems. | Proof that every guideline works for every product or user group. |
| NIST AI Use Taxonomy (NIST Trustworthy and Responsible AI 200-1) | Describing AI’s contribution to human goals through 16 activity categories across domains and techniques. | A universal product-design method or evidence of user preference. |
NIST published the AI Use Taxonomy in 2024; it was authored by Mary Frances Theofanos, Yee-Yin Choong, and Theodore Jensen. Use its categories to make the activity and evaluation needs clearer, while letting the actual task, users, environment, and consequences guide decisions. NIST AI Use Taxonomy
Compare design options against the task
When choosing between concepts or interaction patterns, assess each against the same practical questions:
- Does it solve a meaningful user need, and is the benefit better than a simpler approach?
- Which parts of the work are automated, and which are augmented or left to the user?
- Can users understand the AI’s role and limitations, and do they have suitable control?
- What happens when the system is wrong, and can users recover?
- Does the full experience help people achieve the intended outcome?
Microsoft Research’s 2019 HAX paper describes 18 proposed human-AI interaction guidelines. The paper page reports multiple evaluation rounds, including a user study in which 49 design practitioners applied the guidelines to 20 popular AI-infused products. Those details describe evaluation context; they do not show that every guideline works in every product. Microsoft Research: Guidelines for Human-AI Interaction
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