Explore ten AI feature ideas, failure modes and evaluation measures. Test whether they help users complete a task and return to your product.
An AI feature earns its place when it helps a user complete a useful task and gives them a reason to return. It can also introduce wrong suggestions, unwanted messages or extra steps. The ten ideas below are hypotheses to test, not client results or promised gains. Define retention for your product, compare similar user groups and track negative effects alongside engagement.
1. Relevant content recommendations
Recommendations can help people find a useful item without browsing an entire catalogue. Start with explicit interests, recent activity or simple business rules before assuming a complex model is needed. Explain the recommendation where helpful, let users change their preferences and provide a sensible result when no history is available.
Implementation approach: define eligible items, the signals you may use and the behaviour for new users. Compare a recommendation model with a straightforward baseline. Keep availability, access restrictions and other required business rules outside the model’s discretion. Check that repeated suggestions do not hide useful alternatives.
What to measure: completed useful actions and return visits among comparable users, alongside dismissals, repeated items and preference changes. A higher click rate alone does not establish that recommendations improved retention.
2. Adjustable difficulty and pacing
A learning or training product can adapt the next activity to progress and stated goals. A simple choice of difficulty may be enough. If a model is involved, give users a way to change the recommendation and make clear what information influenced it. Avoid treating a short interaction as a reliable assessment of ability.
Illustrative example: a practice app offers an easier question after repeated errors, with an explanation and a way to keep the original level. The team tests whether users understand the change and complete the activity. This is a product example, not an Afiniti Global client result.
Implementation approach: define learning objectives, allowed difficulty changes and the evidence needed before changing a path. Test new users, inconsistent performance and people who decline personalisation. Review whether the objective rewards meaningful progress rather than simply keeping someone in a session.
What to measure: task completion, demonstrated progress and voluntary return, alongside frustration, abandoned sessions and overrides. Compare results over a suitable observation period before deciding whether adaptation is useful.
3. More useful notifications
A notification should support a task the user recognises. Start with relevant events, clear preferences and limits on frequency. More opens can coexist with more annoyance, so a model should not optimise only for the next tap.
For example, a booking app might remind a user about an appointment at their chosen time. A suggestion based on past activity should still respect notification permission, quiet periods and opt-outs. Define which events justify contact and when a message should be cancelled because the task is already complete.
Implementation approach: begin with approved timing rules and message types, then test personalisation if enough reliable data exists. Keep a record of why a notification was eligible and test duplicate events, changed preferences and delayed delivery.
What to measure: completion of the intended task, repeat use and notification opt-outs. Include complaints and duplicate sends; an improved open rate is not a retention result by itself.
4. Search that helps users finish a task
Semantic search can complement keywords when users describe a need differently from the source text. It still needs relevant content, accurate filters and access controls. Test ordinary searches, vague requests and questions with no valid result before adding generated answers.
Illustrative example: a property-search app turns a natural-language request into proposed location, budget and property filters. The user reviews those filters before searching. Descriptions such as a good area are ambiguous, so the app should ask for clarification rather than turn them into an unexplained ranking.
Implementation approach: compare keyword, semantic or combined retrieval on representative queries. Apply structured constraints and permissions consistently. If an answer summarises results, show the underlying sources and check that the summary does not add details absent from them.
What to measure: successful searches, useful follow-on actions and return use, alongside empty results and incorrect matches. Review failures by query type rather than treating every search click as success.
5. An assistant with clear boundaries
An in-app assistant can help users find information or prepare a task through conversation. Give it a defined job, approved sources and a route to ordinary controls or a person. Account access and external actions need explicit server-side authorisation; a conversational interface does not establish permission.
Illustrative example: a service app answers a booking-policy question from an approved source, then offers the existing booking screen. If the policy does not cover the request, it passes the question to staff with useful context. This does not assume an assistant can safely make account or payment changes.
Implementation approach: test source quality, access boundaries, uncertain questions and failed handovers. For any action-taking feature, define the allowed action, confirmation step and recovery path separately. Keep staff review where the consequence of an incorrect response requires it.
What to measure: resolved tasks, correct answers, successful handovers and repeat use, with sampled quality reviews. Fewer support tickets can also mean that users cannot find help, so investigate the cause before claiming improvement.
6. Useful summaries and trends
A summary can help a user understand their activity without reviewing every record. Present the period, available data and limits clearly. Missing data or a change in recording habits can change an apparent trend, and a correlation does not explain its cause.
Illustrative example: a work-planning app summarises completed and overdue tasks for a selected period and links each figure to its records. Users can correct missing entries before sharing the summary. Sensitive health or financial interpretations need appropriate specialist review rather than being inferred from an engagement goal.
Implementation approach: calculate required figures from validated records and use AI only where it adds a useful explanation. Check calculations separately from generated wording. Show missing information and avoid presenting a weak pattern as a confident prediction.
What to measure: whether users understand the summary and take a useful next step, alongside corrections and disputed insights. Compare return behaviour without assuming that people who already use the app more were retained because of the summary.
7. Relevant offers with clear rules
A product may recommend an eligible bundle or service when it fits a stated need. Establish commercial rules, eligibility and user controls before testing a model. A personalised offer can affect trust, so show its terms clearly and avoid implying that urgency or a discount is based on facts you cannot support.
Illustrative example: a shopping app suggests a bundle using published eligibility rules and shows its total price before checkout. The user can dismiss it and buy the original items. This example does not establish a reduction in abandonment or permission to infer sensitive personal circumstances.
Implementation approach: separate model suggestions from the rules that determine an actual price or entitlement. Test eligibility, repeated offers, expired promotions and the checkout total. Include the people responsible for commercial terms and data use in the review.
What to measure: completed purchases, repeat use, margin and complaints against a suitable comparison group. A short-term conversion increase may be unhelpful if customers return less often or find the pricing unfair.
8. Onboarding matched to the task
Ask what a new user wants to achieve, then offer the setup steps needed for that task. Personalisation should simplify orientation rather than guess a person’s expertise from weak signals. Keep a way to view the full product and change the chosen path.
An experienced user may prefer a short setup checklist, while someone unfamiliar with the task may want an example. Let people choose where possible. If the app recommends a path, explain the choice and avoid hiding essential permissions or account settings behind an assumed profile.
Implementation approach: define the first successful task and the minimum information required to reach it. Compare the proposed journey with a simpler onboarding flow. Test skipped steps, interrupted setup, returning users and people who change their stated goal.
What to measure: first-task completion, time to useful value and later return, alongside setup errors and requests for help. Completing a tutorial is a different outcome from successfully using the product.
9. Reviewable defaults and form assistance
Defaults can reduce repeated typing when the likely value is reliable and easy to change. Use saved preferences or deterministic rules where they are sufficient. An AI guess should not silently become a confirmed address, instruction or account choice.
Examples include suggesting a previous delivery address, drafting a repeated order or pre-filling a visit-report category. Show the source or reason where useful and require confirmation for consequential choices. Leave uncertain fields empty rather than making an answer look verified.
Implementation approach: identify which fields may be suggested, when stored information expires and how corrections are saved. Test shared devices, changed accounts and old preferences. Keep personalisation within the access and data-retention rules agreed for the product.
What to measure: completed tasks, corrections, submission errors and repeat use. Fewer taps only help when users still understand and control what they submit.
10. Drafting and editing assistance
AI can help someone start a caption, report or message, but a plausible draft may contain incorrect details. Make assistance optional and keep the original input available. The goal should be an acceptable finished item, not simply a larger volume of generated text.
A field worker might turn a note into a structured report, then review missing details before saving it. A content creator might request a shorter version while preserving the original. Label generated suggestions and keep publishing, sending or updating records behind the required review step.
Implementation approach: define the input, intended audience and required facts. Test omissions, unsupported claims, inappropriate tone and changes that alter meaning. Give users a way to edit, reject and recover their own work. Review data handling before sending confidential content to a provider.
What to measure: accepted finished drafts, correction effort, task completion and subsequent use. Generation frequency and time spent editing do not by themselves show that the feature creates lasting value.
Choose one hypothesis with a clear user benefit. Define the retention event and period, record a baseline and compare eligible groups where feasible. Monitor quality, cost, complaints and opt-outs alongside return use. Keep a feature when the evidence supports its value for your users, and revise or remove it when the extra complexity is not justified.