From AI feature to product value
Your first useful AI feature. A product roadmap for the year ahead.
Read the reportExecutive perspective
2025: make AI part of the product, not just the conversation.
Find your first useful feature
An answer grounded in your documentation. A first draft a customer can edit. Information extracted from an uploaded file. Choose one recurring task where AI could make the product more useful.
Prove the value before expanding
Build a complete first experience and compare it with the existing workflow. The question for the year ahead is whether customers can finish the task with less effort and an acceptable result.
Build a capability you can improve
Make review, access and recovery part of the first release. Learn from real use, then decide which adjacent problem deserves the next investment.
01 / 2025
AI is entering the product roadmap.
The opportunity for 2025 is to turn experimentation into something a customer can use.
The starting point
Entering 2025, product teams have a practical decision to make: where could generative AI improve the experience they already offer? A compelling demonstration can open that conversation. The next step is a feature with a clear purpose, a place in the customer journey and a way to judge whether it helps.
What the available research tells us
McKinsey’s May 2024 survey reports AI use in at least one business function at 72%, compared with 55% in 2023. Its early-2024 sample included 1,363 participants. Adoption signals interest and activity; it does not tell us which customer-facing features create value. [1]
The product opportunity
Use this year to make one important task easier: help a customer find a sourced answer, prepare a proposal or turn a document into information they can review. This report sets out how to choose that first feature, build the surrounding experience and earn the decision to expand.
Start 2025 with a customer problem worth solving.
02 / 2025
Pick a job before you pick a model.
Your first AI feature should have a job a customer can recognise.
Find the friction
Choose a recurring task with a clear beginning and end. It might be finding an answer in approved documents, preparing a first draft, extracting information from an intake form, or suggesting a category for review. Observe the current process before deciding which part requires AI.
Keep the first promise narrow
For a document assistant, “draft an answer with supporting sources” is a more inspectable promise than “handle customer enquiries.” Define what information it can use and what it must leave to a person. A constrained scope gives the team a way to compare behaviour across representative attempts.
Check whether AI is necessary
A better search interface, a clearer form or an ordinary rule may solve the problem. Compare the options using the same task and acceptance criteria. Choose AI where its flexibility contributes enough value to justify evaluation and operating work; do not add it merely to make the roadmap look current.
Write the decision down
The initial brief should name the user, the task, the current difficulty, the desired change and the unacceptable failure. It should also name the owner who can decide whether the evidence supports proceeding.
A useful first release answers one important question reliably enough for its intended role.
03 / 2025
Four patterns. Different responsibilities.
Four starting points for an AI-enabled product. Choose the responsibility you are ready to own.
Assist with understanding
Retrieval and summarisation help people inspect information. The product still needs appropriate access controls, visible sources and a useful response when information is incomplete. A fluent summary should not hide an unresolved conflict in the source material.
Prepare a proposal
Drafting and extraction create something a person can review. Make the proposed changes visible beside the relevant evidence. A reviewer needs a practical way to correct, reject or escalate the result, rather than a single approval button without context.
Recommend a next step
Recommendations influence a decision even when they do not execute it. Define the task-specific evaluation, the limits of the suggestion and who remains responsible for accepting it. Monitor how people actually use the recommendation.
Act within a boundary
Actions require a clearer contract: permitted operations, confirmation rules, reversible steps and a recovery path. Increasing autonomy should follow evidence about the complete task. It should not be treated as the natural upgrade for every successful assistant.
More autonomy creates a different operating responsibility, not an automatic improvement.
04 / 2025
Design the system around the feature.
The model is one participant in a product journey.
Context and permission
Define where information comes from, how it is kept current and which user is allowed to access it. Preserve the application’s access boundaries when gathering context. The system should not gain a broader view merely because a model can process more information.
Output and review
Return an output that fits the task. A proposed record update needs a comparison; a sourced answer needs inspectable evidence; an uncertain extraction needs a way to leave fields unresolved. Treat these as product behaviours that can be evaluated, not presentation details to add after the model call.
Exceptions and operation
Decide what happens when the model or a dependency is unavailable, when sources disagree, and when a user reports a wrong result. Record enough information to investigate without indiscriminately retaining sensitive content. Make ownership of quality, access, cost and incidents explicit.
Reference point
NIST’s AI Risk Management Framework is a useful reference for considering risk in context. Our application of that idea is practical: identify the failure that matters in this workflow, define who can act on it, and include the response in the first delivery scope. [2]
05 / 2025
Count the work after the answer.
The unit of value is a completed, acceptable task.
A worked example, not a benchmark
Assume a routine task currently takes 12 minutes: eight preparing information and four reviewing it. In an illustrative AI-assisted version, preparation takes three minutes, review still takes four, and exception handling averages one. Total effort becomes eight minutes, a one-third reduction under these assumptions.
Sensitivity matters
If review rises to seven minutes and exceptions average three, the same preparation improvement produces 13 minutes of total effort. The feature is now slower overall. These numbers are deliberately hypothetical: replace them with observations from your workflow before making a business decision.
Include quality and cost
Measure accepted completions, corrections, escalation and abandonment. Add model, infrastructure and operating costs. An apparently cheap response can be expensive if it creates unresolved work. Keep high-consequence failures separate from an average score so that common easy tasks do not conceal them.
12 → 8 minutes only when the rest of the workflow cooperates. Illustrative, not a promised result.
06 / 2025
Evaluate the task you will release.
A demonstration is a sample. A release decision needs a deliberate set of cases.
Build a representative evaluation set
Include ordinary requests, ambiguous inputs, missing information and situations that should be declined or escalated. Agree what a useful result looks like with the people who understand the work. Avoid an evaluation set made entirely from examples used while tuning the feature.
Evaluate the surrounding behaviour
Check that the right information is available to the right user, the output is supported, and the reviewer can detect and correct a problem. Include service failures and changed source material. Where an action is involved, test that its scope matches what the user approved.
Define the release gate
Write down which failure categories prevent release, who can accept a known limitation and how a limited rollout will be stopped. A single overall quality percentage is rarely enough to explain those decisions. Keep the acceptance record readable by product, engineering and operating teams.
Keep evaluating
Changes to prompts, retrieval, models and source data can change behaviour. Decide which cases must be rerun and which live signals trigger investigation. The evaluation set should become part of product maintenance rather than a one-off presentation.
07 / 2025
Make review usable. Make recovery possible.
A person in the workflow needs evidence, authority and a next step.
Show what matters
Place the proposed output beside its source and the current state where relevant. Make changed values visible and preserve the distinction between supported information and unresolved fields. Review should not depend on opening several tools and remembering details between them.
Give rejection somewhere to go
A reviewer may need to correct a value, request clarification, route an exception or stop an action. Name the owner of each path. An exception queue without an operating arrangement merely hides unfinished work.
Trace the accepted result
Keep enough history to distinguish what the model proposed, what a person changed and what the system executed. This supports investigation and learning without pretending that approval alone proves the output was correct.
Plan continuity
Agree who reviews quality, controls access, responds to incidents and decides the next improvement. A feature can pass its initial acceptance criteria and still require ongoing product judgment as users, sources and priorities change.
08 / 2025
Your first 90 days with an AI feature.
A starting plan for 2025: one customer task, one complete feature, and evidence for the next decision.
Days 1–20 / Establish the task
Observe the current process, choose the initial use case and collect a representative evaluation set. Agree a baseline and identify data access, ownership and operating constraints. End with a decision to proceed, change the approach or stop.
Days 21–50 / Build the complete slice
Implement the customer journey, including permission, review and recovery. Compare its behaviour with the agreed baseline in a suitable test setting. Resolve the failure categories that prevent release before expanding feature scope.
Days 51–75 / Release with visibility
Introduce the feature to an appropriate limited group. Watch the complete task rather than only model responses. Give support and reviewers the information needed to investigate a case, and keep a clear route to pause or revert the feature.
Days 76–90 / Decide what earns expansion
Review evidence on usefulness, quality, effort and operating cost. Expand only the task categories the evidence supports. Agree the next ownership boundary with the client team. This is a planning framework, not a delivery-time guarantee.
09 / 2025
Put one useful AI feature on the roadmap.
Bring these decisions into your first product planning conversation of the year.
Customer and outcome
Name the person, the task and the change you want to observe. Describe the current workaround and record the baseline. If the business cannot explain why the task matters, resolve that before discussing model selection.
Evidence and boundaries
List permitted sources, who can access them, representative test cases and unacceptable failures. Decide where a person must review or approve. Record what the first version will deliberately leave out.
Ownership and operation
Assign the product decision-maker, engineering owner, reviewer and operating contact. Define the exception path and the conditions for pausing the feature. Make ongoing evaluation part of the maintenance agreement.
The investment decision
Compare AI with the simplest credible alternative. Include implementation, review, correction and operating costs. Choose a bounded next step with explicit acceptance criteria. Vinovo can help shape the task, build the complete capability and stay involved as the product evolves.
The ambition for 2025: a product that helps people do something meaningfully better.
Sources & methodology
Prepared 24 September 2026 · Early-2025 perspective, using sources published before 2025.
Independent synthesis of published research and Vinovo recommendations. No original survey was conducted. Illustrative models are hypothetical, not measured results or forecasts. No affiliation or endorsement is implied.
[1] McKinsey - The state of AI in early 2024: Gen AI adoption spikes and starts to generate value
30 May 2024 · Survey fielded 22 February-5 March 2024; 1,363 participants. Reported organizational adoption, not measured product value or a forecast for 2025.
[2] NIST - Artificial Intelligence Risk Management Framework 1.0
26 January 2023 · A risk-management framework, not an impact benchmark. Used as a reference for contextual evaluation and ongoing responsibility.