Do these 3 things before closing this tab:
1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesDesign an AI product around a real human goal—not around the fact that a model can do something. Identify the task and its context, confirm that AI adds distinct value, choose what the system should do versus what the person should control, and test whether the complete experience helps users succeed. This process improves the odds of usefulness; no framework can guarantee adoption.
Start with the person’s task and the outcome they need
Describe who is trying to do what, where the task happens, and what constraints shape it. Include people affected by the product, not just the account holder or purchaser. A useful design principle, quoted on NIST’s Human Centered Design page from ISO 9241-210:2010(E), is: “The design is based upon an explicit understanding of users, tasks, and environments.”
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Define success in human terms: what should someone be able to accomplish, and how will the team recognize that the experience helped? Google PAIR’s Guidebook frames the opportunity as an intersection between user needs and AI strengths, while also prompting teams to consider downstream effects. Its chapter “User Needs + Defining Success” puts the point plainly: “Even the best AI will fail if it doesn’t provide unique value to users.”
Check that AI adds value over a simpler option
Identify the part of the task AI could improve, then compare it with a straightforward interface or non-AI process. AI may help when it can meaningfully reduce effort or extend what a person can do; its mere ability to generate, rank, or predict is not a user benefit by itself. Set success criteria around the task outcome rather than model activity, response plausibility, or feature usage alone.
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PAIR is a useful prompt for this decision, not a validation of any particular product. Its guidebook has been used by more than a quarter million people across roles worldwide, according to the Google Codelabs page; that figure is not a current usage count or evidence that products built with the guidebook succeed.
Decide what the AI does and what the person controls
Break the task into parts and decide where the system should suggest, draft, rank, summarize, or take action. Ask whether people want the task done for them, help doing it, or a faster way to do it themselves. Those are different needs, and they call for different balances of automation and augmentation.
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For each AI action, make a deliberate choice about whether a person should review it, change it, reject it, or let the system proceed. The right division depends on the task and its consequences; there is no universal amount of automation that works for every product. Google PAIR treats this balance as a core design question.
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Give users enough context to understand what the system is doing, what it can and cannot do in this situation, and how its output fits into their task. A confident-sounding answer should not be mistaken for a reliable one. Design both the explanation and the available controls to suit the stakes and the user’s needs.
Microsoft’s Human-AI Experience (HAX) Toolkit offers interaction guidance organized around first use, ordinary interaction, errors, and changes over time. Its 18 proposed guidelines were evaluated over multiple rounds, including a study in which 49 design practitioners applied them to 20 AI-infused products, as reported on the Microsoft Research paper page. This is evidence of practitioner evaluation, not proof that every guideline works in every product.
Design for mistakes and recovery before polishing
List likely ways the interaction can go wrong, especially for language-based systems. Consider ambiguous requests, missing context, incorrect output, unexpected changes, and unsupported intent. Then prototype what a person can do next: clarify, correct, reject, undo, or recover, as appropriate for the task.
HAX includes a playbook for anticipating natural-language failures and planning recovery, along with a workbook to help teams prioritize relevant guidance. Use these resources to uncover gaps in the interaction, not as a substitute for deciding what this particular user and task require.
Evaluate the whole experience and iterate
Involve users throughout design and development. Test proposed interactions early, observe whether people can complete the task, and refine the design from what you learn. Evaluate the full experience—including how users understand, control, and recover from AI behavior—not just whether a response looks plausible.
NIST’s human-centered design principles describe activities including understanding context, specifying requirements, designing solutions, and evaluating them; evaluation can happen in early stages. For a shared vocabulary when describing what AI contributes to a task, NIST’s 2024 AI Use Taxonomy identifies 16 activities across domains and techniques. It helps teams articulate human goals and evaluation needs; it is not a product-market-fit recipe.
Use a practical decision checklist
- Human outcome: Is the user, task, environment, and intended result clearly specified?
- Distinct value: Does AI improve this task over a simpler alternative, and how will that improvement be assessed?
- Division of work: Is it clear what the AI handles, what the person reviews, and what remains under the person’s control?
- Understandability: Can users tell what the system does and where its limits matter?
- Recovery: Have likely errors and unsupported requests been considered, with an appropriate next step for users?
- Evaluation: Has the complete interaction been tested against user outcomes and improved through iteration?
These checks are decision prompts, not a scorecard with a universal pass mark. The task, people affected, environment, and consequences determine which design choices make sense.
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