You usually do not need to rebuild a working software product to add AI. First identify the user task, then check whether search or deterministic logic can handle it. If a model is warranted, start with a bounded feature that fits the existing data and workflow, and evaluate its quality and operating costs before expanding.
What does “add AI” mean for the user?
A request for a chatbot is not yet a product requirement. The user may actually need to find an answer across product documents, understand a particular record, or get information summarized in context. A request to automate work may mean extracting fields, classifying an item, routing it to the right person, or drafting a response for review.
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Describe the task in plain language and name the useful outcome. For example: “Help a support agent find the policy that answers this customer’s question, and show the relevant passage.” That is more actionable than “add a chatbot,” and it gives the team something concrete to test.
Also distinguish observed user demand from pressure to match a competitor or satisfy an investor. Those signals may prompt investigation, but they do not establish that a particular AI capability will help users.
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When is AI the right tool?
Try the simplest method that meets the need. Ordinary search, a filter, or a deterministic rule can be easier to predict, test, and operate than a model. AI is worth considering when the task involves interpreting variable language or producing a useful draft or classification that fixed logic cannot adequately handle.
| Approach | Consider it when | Check before choosing |
|---|---|---|
| Rules or filters | The inputs and decision criteria are explicit and stable. | Whether the rules cover real cases without becoming brittle or costly to maintain. |
| Ordinary search | Users need to locate known information using terms, filters, or indexed content. | Whether better indexing, ranking, or search controls solve the problem without generated answers. |
| AI feature | The task needs interpretation, extraction, classification, or a draft from variable input. | Whether representative outputs can be evaluated, and whether errors can be handled safely. |
This is a practical decision aid, not a published scoring model. Be especially cautious when a wrong output could have serious consequences. A model’s ability to produce a plausible response does not establish that the response is correct or suitable for a high-stakes decision.
Why start inside the existing product?
A focused capability can use the product’s existing records, permissions, and workflow. A user may be able to ask about information they already work with, review a suggested classification beside the relevant item, or edit a draft before sending it. Keeping the capability in context can preserve familiar habits and make it easier to see where the feature helps—or fails.
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A full rebuild is a larger commitment made before the team necessarily knows how users will behave with the new capability. Artemii Tkachuk, founder and CEO of software development agency IvorySoft, argues from his agency experience that a narrow feature can produce more useful learning than a rebuild. His article gives no independently verified delivery-time guarantee, so a small feature should be treated as a way to learn, not as a promise to ship on a fixed schedule.
What to establish before choosing a model
Define the job and success condition
Write down who will use the feature, what input they provide, what output they need, and what a useful result looks like. Include the next step: should the output be displayed for review, saved to a record, or sent into an existing workflow? If success cannot be described in observable terms, the feature will be difficult to evaluate.
Check data access and permission
Map the information the capability needs before designing prompts. Check whether it is technically reachable, clean enough to use, and permitted for the intended purpose. Confirm that access boundaries still apply: a generated answer should not reveal information the user could not otherwise access. Data availability alone does not establish permission to use it.
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Keep the provider replaceable where practical
Avoid coupling the whole product to one provider’s request and response format if a modest interface can isolate that dependency. Tkachuk calls this “A provider you can swap.” The practical aim is to make a provider change possible without rewriting unrelated product logic; it does not guarantee that changing providers will require no adaptation.
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Collect representative examples and define what acceptable behavior means for each: for instance, whether an answer is supported by the available information, whether an extracted field is accurate, or whether the system should decline to answer. Rerun evaluations when prompts, data, or models change. OpenAI’s Working with evals documentation describes evaluation runs, criteria, and result analysis; it supports a way to assess outputs, not a success rate for your feature.
Design a useful failure path
Decide what users will see when an answer is uncertain, incorrect, slow, unavailable, or blocked by a rate limit. Depending on the task, the fallback may be a manual review, a useful plain-language message, ordinary search, or a cached result where that is appropriate. Do not let a failed model call silently become a confident-looking product answer.
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How to plan for production
A prototype bill or a successful demo does not establish what the feature will cost or how it will behave under real traffic. Estimate usage using expected traffic, how often each person will interact with the feature, and how much data each interaction processes. OpenAI’s production best practices discusses projecting token use, planning for rate limits, and options such as shorter prompts, smaller models where suitable, and caching. These are approaches to assess against your quality needs, not guaranteed savings.
Track usage and latency after rollout, and make capacity and cost part of the operating plan. Provider limits and product details can change, so check the provider documentation as you implement rather than treating a previous estimate as permanent.
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A practical sequence for adding a focused capability
- Write the user task. State the input, desired outcome, intended user, and how the result will be used. Separate that need from competitive or investor pressure.
- Test simpler options. Compare a rule, filter, or ordinary search with an AI approach on reliability, cost, and operational complexity.
- Map data and access. Identify required information, verify that it is reachable and usable, and confirm permissions and access boundaries.
- Build the evaluation set and interface boundary. Define representative expected behavior, establish how outputs will be assessed, and isolate the provider behind a replaceable interface where practical.
- Specify failure handling. Decide what the product does for wrong, slow, unavailable, or rate-limited responses, including where a human must review the result.
- Estimate and monitor operations. Project use and cost from traffic, interaction frequency, and processed data; then monitor actual usage and plan for provider limits.
- Roll out narrowly and observe. Watch whether users use the feature, what they ask of it, and where it fails. Use that evidence to decide whether to improve the feature, choose a simpler method, or consider broader architectural change.
How much risk governance is appropriate?
Implementation checks are only one part of managing risk. NIST describes its AI Risk Management Framework as voluntary guidance for incorporating trustworthiness considerations into AI design, development, use, and evaluation. Its AI Risk Management Framework page identifies the Generative AI Profile, published in July 2024, and says the framework is being revised. It is general risk-management guidance, not proof that a particular feature is safe, compliant, or appropriate for a specific decision.
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