Building AI products for industrial use is nothing like creating the next social media app. The stakes are higher, regulations are stricter, and a single misstep can cost millions. Ahmad Fattahi has spent the past 17 years learning these lessons firsthand, watching machine learning evolve into today’s AI-powered world while developing products that Fortune 500 companies rely on.
Why Most AI Projects Crash and Burn
Fattahi does not sugarcoat his early failures. Some products seemed exciting at first but never found users. Others were beaten to market by faster competitors. “I have built products that were super exciting to start, but then they gathered dust. I have also started building things that never materialized,” he says. Each misstep, however, taught him something critical about taking smarter risks. Not all failures are created equal. There is a big difference between testing a half-baked AI model on your biggest customer and running controlled experiments with trusted partners. “You don’t want to put a cutting-edge, barely tested model in front of your customers on the first run,” Fattahi explains.
Smart failures happen when the environment is controlled and the learning is intentional. The key is designing experiments that will not sink the business if they go wrong. That often means working with a small group of customers who know they are part of something new. “We are going to invest a specified amount of resources into this new methodology and put it in front of a small group of trusted partners or customers,” he suggests. When problems arise, the lessons are real, but the relationships remain intact. In other words, you would fail forward.
Understanding Industrial AI Challenges
Consumer apps and industrial AI operate in completely different worlds. For newcomers, the biggest shock is how seriously industrial companies take data security. Every conversation begins with questions about intellectual property. “There’s a lot of sensitivity in the industrial environment about protecting the company’s IP,” Fattahi says.
The smartest AI builders anticipate those concerns instead of scrambling to address them later. Security needs to be baked into the design from day one. Companies can tell when you have already thought through their risks, and that preparation often makes the difference between looking professional and amateur. Pace is another key difference. Generally speaking. tech firms will try a new AI model tomorrow if it sounds promising, while industries like pharmaceuticals or semiconductors can take months or even years to decide. “Some are more forward-leaning. A prime example is the tech sector, as opposed to highly regulated industries like pharmaceuticals or chip makers. They measure three times before they cut,” Fattahi explains.
Start with Problems, Not Technology
Here’s where many AI teams get it wrong. Engineers see a shiny new model and rush to find a use for it. The trouble is that impressive technology does not automatically solve real problems. “You want to start from the real-world problem, not from the technology,” Fattahi says. “It may sound trivial or easy. You’d be surprised how many times people nod and say yes, but as soon as they start doing things, your engineers and your data scientists naturally are driven by the newest, shiniest model.”
The antidote is simple: talk to the people who live with the problems you are trying to solve. These champions know which issues truly matter and which ones only look important on paper. “Those people can tell you exactly which areas will move the needle and which ones won’t,” Fattahi explains. Sometimes the most valuable opportunities do not look glamorous from the outside. But they are the ones that make the biggest difference.
Bridging Technical and Industry Gaps
Technical teams and industry experts often speak completely different languages. Data scientists focus on model accuracy, while factory managers worry about downtime. Success, Fattahi says, depends on finding people who can translate between both sides. “If you have people on your team or in your customer base who can speak both languages, those are the golden resources because there is a need for that translation.” Once that translation is in place, it is just as important to continuously check your work with real users. Discovering you built the wrong feature after two weeks hurts, but it is manageable. Discovering it after six months can sink the project. “Two weeks is relatively nothing. So, you can course correct and come back,” Fattahi notes. The real danger is investing months into something nobody wants. Better to fail fast and learn than fail slowly and waste everyone’s time and resources.
Fattahi’s success comes from controlled experiments and constant reality checks. AI technology may keep advancing, but building useful products still depends on understanding real problems and testing solutions early. Start with genuine needs, partner with people who face those challenges every day, and resist falling in love with your first solution. In the end, industrial AI is not about having the most sophisticated algorithms. It is about building something people actually want to use.
Connect with Ahmad Fattahi on LinkedIn to explore his practical approach to building AI that solves real business problems.
