AI product: transforming technical prowess into business value

Senior manager in the data & ai transformation practice at Converteo, Charles Letaillieur supports organizations in their strategic ambitions related to artificial intelligence. A recognized expert in generative ai and agentic ai, he designs and deploys innovative yet pragmatic solutions, perfectly adapted to the specific contexts of each company. His approach helps to realize the transformational potential of advanced ai technologies by anchoring them in a clear business vision and high-value use cases.

Key takeaways

• the “hammer syndrome” pushes companies to look for problems for their ai—instead of looking for an ai for their problems. this is the first trap to avoid.
• a high-performing ai product is based on four inseparable pillars: desirability, viability, feasibility, and responsibility. omitting one means risking the construction of a costly gadget.
• the ai product builder does not build a swiss army knife—they build a scalpel: fewer features, surgical precision on the core value.

Artificial intelligence has created a “hammer syndrome” on an industry-wide scale: armed with this fascinating new technology, many companies are looking for problems to solve. This approach, led by technology rather than need, gives rise to impressive technical feats—but they fail to transform into sustainable business performance.

The reason is simple: an ai product is, above all, a product. Its success is not measured by the sophistication of its model, but by its measurable impact on the user and the company. To orchestrate this transition, it is imperative to return to the fundamentals of product management.

Anchoring every project in reality with ai product discovery

The race to ai often leads to an excessive focus on the technical aspect. To avoid this pitfall, the ai product builder must rigorously apply the four pillars of discovery, adapted to the specifics of artificial intelligence.

1. Desirability: does the ai solve a real “pain point”? technology does not create the need—it must meet a concrete user expectation to generate adoption.
2. Viability: is the business impact greater than the cost? between the cost of tokens, infrastructure, and maintenance, the builder must ensure that the value created—time savings, revenue—ensures the profitability of the ai product.
3. Feasibility: does the company have the necessary data maturity? a premium ai product can only be built on sound data foundations. “garbage in, garbage out.”
4. Responsibility: does the product comply with the ethical and legal framework—gdpr, algorithmic biases, intellectual property?

Omitting one of these pillars means taking the risk of building a costly gadget with no real utility.

Prioritizing for business performance: building a scalpel, not a swiss army knife

A prototype is often a swiss army knife filled with features. The first industrial version of an ai product, on the contrary, must be a scalpel: fewer features, but surgical precision on the core value.

The role of the ai product builder is to decide what to keep—and what to discard. To guide this prioritization, two questions are enough:

• which feature generates a useful and recurring “wow”? this is your must-have.
• which option is impressive in a demo but ignored in testing? this is a won’t have, no matter how complex it is to build.

Refusing to aggressively deprioritize is the best way to deliver a mediocre product. a good ai product builder knows how to say “no” to protect business value.

Aligning the ai product with strategy and long-term ROI

To ensure sustainable performance, each initiative must be aligned with the company’s overall vision. We then distinguish two types of value creation.

Operational value vs. capital value

Operational value optimizes the existing. it relies on “ready-to-use” APIs and LLMs to automate tasks. the profitability is immediate, but the competitive advantage remains limited—what the company can do, its competitors can do too.

Capital value creates a strategic asset. it involves heavier investments—fine-tuning, proprietary RAG, internal models—to develop a unique capability that no one else possesses. an ai product that leverages a proprietary dataset to offer a novel service builds a true sustainable moat.

The ai product builder must consciously position their projects on this axis and arbitrate between immediate profitability and long-term competitive advantage.

Ultimately, transforming a technical feat into commercial success relies on a fundamental change in perspective. The question should never be “what can we do with ai?”—but rather: “what is the most important business problem, and how can ai help us solve it in a unique way?”