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AI in Products: Start with Value, Not Feature

Artificial intelligence is no longer a distant possibility. According to the AI Index Report 2026, from Stanford University, 88% of organizations surveyed used AI…

Artificial intelligence is no longer a distant possibility. According to the AI Index Report 2026, from Stanford University, 88% of organizations surveyed used AI in at least one business function by 2025, and 70% used generative AI. At the same time, employment of AI agents has remained in single digits in most roles.

This contrast suggests that experimentation has become commonplace, but turning technology into a consistent capability is still a challenge. There is an important difference between putting AI into a product and creating a better product with AI.

For leaders, this difference starts with the question that guides the project.

The right question is not “where can we put AI?”

When technology comes before the problem, the team tends to look for a place to fit it. An assistant appears without a clear function, automation that does not respect the workflow or a layer of customization that adds complexity without improving the experience.

The most fertile starting point is another: What people still can't do well—and why?

There may be too much information to analyze. Perhaps decisions depend on patterns that are difficult to perceive. Perhaps a repetitive task takes up time that should be devoted to judgment, creativity, or relationships. Perhaps each person needs a contextual response that is impossible to produce manually at scale.

Only after understanding this limitation is it worth asking whether AI offers a unique advantage.

The guide People + AI, from Google, recommends evaluating which problems require AI, which are only improved by it and which could be harmed by its presence. In many cases, a clear rule, better search, or simpler flow will be more predictable, explainable, and easier to maintain.

This does not reduce the ambition of the project. Prevents ambition from being confused with complexity.

When AI really adds something

AI tends to be valuable when the product needs to deal with variety, scale, or uncertainty. It can help recognize patterns, interpret language, organize large volumes of information, recommend paths or adapt an experience to the context.

Imagine an internal platform that brings together thousands of operational records. A traditional search can find exact words. An experience supported by AI can relate occurrences, summarize histories and help the expert to formulate hypotheses. The gain is not in appearing futuristic, but in reducing the distance between available information and possible decision.

Even so, technical ability is not evidence of value. The opportunity is only confirmed when something relevant improves: time, understanding, quality, security, access, consistency or results.

A possible reading is that AI has more strength when it acts on an already known friction. When no one can explain what difficulty will be removed, the project still has a technology — not a product proposal.

Automate or scale up?

One of the most important decisions is between do for the person and help you do better.

Automation may be suitable for tasks that are repetitive, voluminous, dangerous, or with relatively clear success criteria. Classifying documents, organizing requests or preparing a first summary can free up time and reduce effort.

Expanding human capacity tends to make more sense when the task involves context, responsibility, creativity, negotiation or different acceptable responses. In these cases, AI can present possibilities, highlight signs or structure information, while the decision remains with those who know the situation and are responsible for its consequences.

In an enterprise product, this boundary should not be defined only by what the model can perform. It depends on the impact of the error, the need for explanation, and the level of control people expect to maintain.

In practice, the best experience can combine both approaches: automating invisible work and preserving human participation in moments that require intention.

Confiança não é uma mensagem no rodapé

An AI product is not trustworthy just because it claims to be. Trust comes from experience.

People need to understand the role of the system, recognize its limits, and know what to do when the result doesn't seem right. This may require preview, editing, confirmation, history, understandable justifications, and clear paths to undo or dispute an action.

O AI Risk Management Framework, from NIST, presents trust and risk management as issues present in the design, development, use and evaluation of AI systems. The OECD AI Principles, updated in 2024, also highlight values ​​such as transparency, explainability, robustness, security and accountability.

For product leadership, the consequence is direct: governance should not only come in after the experience is ready. Privacy, data quality, usage limits, impact on different audiences and responsibility for decisions need to influence the concept from the beginning.

This suggests that trust is not an external constraint on the product. It is one of its essential features.

Five decisions before developing

Before choosing a model, hiring a platform or building an interface, the team can align the project through five decisions.

1. Which problem deserves to be solved?

Describe the difficulty in human, observable terms. “Using AI in customer service” is a technological intention. “Helping customers find the right information without repeating context” is a product opportunity.

2. What will AI do better than a simple solution?

Identify the specific contribution: interpret language, recognize patterns, personalize, predict, summarize, or generate alternatives. If a clear rule solves the problem with less risk, it may be the smarter choice.

3. What should remain under human control?

Define moments of review, decision and responsibility. The greater the impact of an error, the more important it is to preserve oversight and the possibility of intervention.

4. What conditions make use reliable?

Map necessary data, permissions, limits, possible failures, affected audiences and ways to correct them. The experience also needs to work when the AI ​​doesn't know, can't, or shouldn't respond.

5. What evidence will justify continuation?

Choose indicators linked to the problem: reduction in time, increase in resolution, improvement in quality, reduction in rework, perceived confidence or ability to complete a task. Number of interactions alone measures activity — not necessarily value.

From experiment to learning

An AI project doesn't need to start big to think big.

You can represent the experience before integrating the entire infrastructure. A prototype can simulate responses, recommendations or automations and allow the team to observe how people interpret, correct and use the result. This initial test helps separate the attractiveness of the idea from its actual usefulness.

The goal of the pilot is not just to demonstrate that the AI ​​works. It's about finding out under what conditions it improves the journey, where it fails, how much monitoring it requires and what changes the process will need to absorb.

Then, the team can expand the scope based on evidence. This progression preserves speed without turning users and operations into an involuntary laboratory.

Intelligence is also knowing where not to use AI

The future of products will not be defined by how much artificial intelligence we can add to them. It will be defined by the quality of the choices we make with this ability.

Relevant products use AI to make something more understandable, possible, safe, or valuable. They don't take away autonomy just because they can automate. They do not hide uncertainty to appear efficient. And don't confuse newness with transformation.

For leaders, the most innovative gesture can also be the most disciplined: starting with the problem, protecting what needs to remain human and demanding evidence before escalating.

When there is purpose, AI stops being a promise attached to the product. It becomes a coherent part of the experience.

There is an opportunity for AI in your product, but you still lack clarity on where to start?

Coradini Design helps transform intent into concept, prototype and validatable experience.

Sources consulted

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