Integrating AI Into a Product: Choosing the Right Features for Business Needs
Where should you start with implementing AI in a product? First, define the metric you want to improve: user retention, conversion, or task completion speed. That metric determines which AI feature will deliver results and which will remain just another capability in the interface. Read on to learn how to choose an AI solution for your business needs.
Each AI feature is designed for a specific type of task, so the choice starts with identifying which product metrics it should affect. For example, new users rarely return to the product after signing up; catalog search does not account for how queries are phrased; the team spends a lot of time preparing standard documents.
Each of these situations corresponds to a specific metric: user retention, search conversion, or the time it takes to complete routine tasks. The metric is defined at the task definition stage, before development begins, so the result can be compared with the state of the product before implementation.
AI features for specific business tasks
Smart Recommendations
The feature addresses low user retention and low use of product capabilities. AI analyzes user behavior and the actions of users with similar interests, then suggests the next step, similar products, or related content. Another scenario is a reminder about useful features when user activity declines, or a prompt to try new capabilities.
For example, in a B2B SaaS product, a recommendation appears when a user's actions point to an unresolved task and suggests the feature that addresses it. The result is measured by the usage rate of new features and the churn rate.
Semantic Search
Semantic search finds results based on the meaning of a query rather than an exact keyword match. It helps users find what they need even when their wording differs from category names, attributes, or article titles. AI takes the meaning of the query into account, so in a catalog a product can be found by its description, and in a knowledge base or FAQ, an answer that is phrased differently.
The result is measured by search conversion and the share of queries with no results. Implementation complexity depends on the size of the catalog or knowledge base, the number of languages, and how quickly content changes need to appear in search results.
AI Content Generation
This feature is useful when a team regularly spends a lot of time preparing standard materials. AI generates content directly in the product interface, based on data already in the system. This can include sales proposals and emails, product descriptions based on set parameters, short report summaries, or content translated into several languages.
Users get a ready draft instead of a document they would otherwise have to write from scratch. When a text is published or sent to clients, Human-in-the-Loop review is applied: a person checks the result and approves it before publication. The effect is measured by the time the team spends preparing such materials.
Learn more about each AI feature, including pricing and implementation timelines, on the AI in Product page.
What does the choice of an AI feature depend on?
The choice of a feature is influenced not only by the task but also by how the product's data, APIs, and infrastructure are organized. Implementation complexity depends on them as well.
For example, recommendations require a history of user behavior: clicks, views, or purchases. For semantic search, the volume and structure of content matter. In most standard technology stacks, both features can be added without major architectural changes, but sometimes the data structure needs to be refined before integration.
That is why it is worth checking before development whether the available data is suitable for the chosen feature. This is what the AI Feasibility Sprint is for: the data is checked on real examples before investing in development. It provides a realistic assessment of whether implementation is feasible and helps determine which feature the product needs.
The choice of an AI feature depends on three factors: the task, the metric used to measure the result, and the state of the data. The task determines the type of feature: recommendations address user retention, semantic search helps users find what they need, and content generation handles routine team tasks.
Tell us about your task, and we will help you implement it. Leave your contacts in the form, and we will discuss the details on a free 30-minute call and provide a preliminary estimate of cost and timeline.
