The AI gold rush has arrived.
Every product team feels pressure to integrate it. But Marcello Genovese thinks most of them are asking the wrong question.
The product executive and technology strategist have watched companies scramble to add AI capabilities without considering whether those capabilities serve anyone. The result is a wave of products with AI features that exist because AI is expected, not because AI solves a problem.
“If AI doesn’t solve a problem or help the user, and it’s just to have a button, oh, do this with AI, it doesn’t make sense,” Genovese says. “But if it’s solving a problem and giving some additional help, then this is great.”
Most teams skip the evaluation entirely.
One Question Before You Build
Before any AI integration, Genovese asks product teams to answer a single question.
“What does it help the user for, and what does it make the life of the user easier, or is it really solving a problem?” he says.
The question forces clarity.
AI can automate tedious tasks. It can surface insights humans would miss. It can personalize experiences at scale.
But AI can also add complexity without benefit. It can introduce unpredictability into workflows that users depend on. It can create privacy concerns that outweigh convenience gains. It can distract teams from solving the actual problems their users face.
When a product team cannot articulate how AI makes users’ lives easier, they’re building for the technology rather than the person.
Gartner predicts that at least 30 percent of generative AI projects will be abandoned after proof of concept by the end of 2025. The reasons cited include poor data quality, inadequate risk controls, escalating costs, and unclear business value. That last one is the killer. Teams that cannot explain what problem they’re solving tend to discover the answer too late.
Faster Isn’t Better
AI has transformed what product teams can accomplish. Development cycles that once took months now compress into weeks.
“AI helps a lot to build digital products five times faster, in my opinion, at the moment,” Genovese says.
The acceleration is real. But speed amplifies whatever direction a team chooses.
Moving faster toward the wrong destination just means arriving at failure sooner.
The data support this. According to S&P Global’s 2025 survey of over 1,000 enterprises, 42 percent of companies abandoned most of their AI initiatives this year—up from just 17 percent in 2024. The average organization scrapped 46 percent of AI proof-of-concept projects before they reached production.
RAND Corporation research puts the broader AI project failure rate at over 80 percent. That’s twice the failure rate of non-AI technology projects.
Genovese sees the pattern emerging. Teams leverage AI to build more features faster, often focusing on adding an AI button without pausing to evaluate whether those features belong in the product.
“The biggest problem is that now everybody thinks they can build a good product, but they don’t think the product through,” he explains.
Accessible tooling has lowered the barrier to building. It has not lowered the barrier to building well.
The thinking still matters. The strategy still matters. The discipline to solve the right problem still matters.
When AI Actually Earns Its Place
Not every AI integration is hollow.
Genovese has observed products transform when teams apply AI to genuine user pain points.
“A lot of bad products got strong somehow because they integrated AI in the right way,” he notes.
These weren’t products that added an AI button to their interface and called it innovation. They identified specific friction in the user experience and applied AI to reduce it.
Slow processes. Manual drudgery. Information overload.
The AI disappeared into the product. Users didn’t celebrate the AI. They celebrated that the product suddenly worked better.
Does the AI make the product better, or does it make the product sound better in a press release?
The answer separates meaningful integration from marketing theater.
The User Still Wins
Throughout his career, Genovese has returned to a foundational principle that predates the AI era and will outlast the current hype cycle.
“The user is king,” he says. “The person who is using it is the king of a product and nobody else.”
AI doesn’t change this hierarchy.
It provides new tools, new capabilities, and new ways to serve users. But the purpose remains constant: solve problems for your product’s users.
The pressure comes from multiple directions. Investors are asking about AI roadmaps. Boards are expecting AI strategy presentations. Competitors are announcing AI features weekly. These forces push product leaders toward integration regardless of user benefit.
“I would never change any product because an investor wants to have it,” Genovese says. “Because if it’s stupid, I don’t care who is making money.”
Product leaders face significant pressure to add AI features for reasons unrelated to users. Genovese suggests they resist.
The technology has reached a point where integration is genuinely accessible.
“We’re in 2026 on technology, everything is possible. There are no limits. We have enough computing power, we have everything we need,” he observes.
The constraint is no longer technical. The constraint is judgment.
Product leaders must evaluate where AI creates value and where it creates noise. They must distinguish between features users will appreciate and features users will tolerate. They must resist the temptation to integrate AI everywhere simply because integration is now easy.
“Integrate AI at the right time in the right place,” Genovese advises.
Every technology cycle produces its share of products that chase trends rather than solve problems. Some companies add an AI button just because AI is expected. Their products will feel cluttered, unfocused, and confused about their purpose.
Other companies will ask Genovese’s question first.
What does this help the user do? What problem does this solve?
The best AI integrations are the ones you stop noticing. They disappear into products that simply perform better than before.
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