AI won’t save anyone if it disconnects from reality: the art of « customer-back engineering »
Recently, I found myself in front of my brand new, connected coffee machine, a Christmas gift. A marvel of technology, supposed to revolutionize my mornings. It grinds beans to perfection, heats water to the ideal temperature, and to top it off, it can be programmed from an app on my phone. The problem? To make a simple espresso, I have to navigate through three menus, the app disconnects every other time, and the hot water takes three times longer to come out than on my old basic coffee maker. The result? I put it back in the cupboard and pulled out my good old French press. This is the kind of situation that reminds me how even the most advanced technology can sometimes miss the point. And that’s exactly where the core of the problem we’re going to discuss today lies, weighing heavily on AI innovation. It perfectly illustrates why innovation must start with the customer, not the tech specs. In fact, this is precisely pourquoi innovation doit partir du terrain, des vrais besoins, et non d’une obsession pour des fonctionnalités clinquantes.
McKinsey regularly publishes impactful studies, and the latest one on digitization is particularly telling. It reveals something we all intuitively feel: despite years and billions invested in digital transformation, companies struggle to capture even a third of the value they hoped for. A sobering figure, isn’t it? We layer on technology, optimize processes, swear by algorithms, and yet, the tangible return on investment remains well below promises. But why this colossal gap between intention and reality? The answer, according to their analysis and my field experience, is often the same: the approach is inverted. We start with the technology. We say, « Hey, generative AI is amazing! Where could we ‘graft’ it to shine? » Or, « We developed this super machine learning module, what good could it do? » We build technological cathedrals, not thinking of the faithful who will pray there, but marvelling at our own engineering. And that’s precisely pourquoi innovation doit partir with the human, not the algorithm. If you want to see how this principle applies to virtual spaces, check out our guide on building user-centric metaverse experiences.
The seduction of tech-first thinking (and why it fails)
Let me be honest: I get the appeal. As a writer and tech enthusiast, I’ve fallen for shiny new tools myself. There’s a rush in seeing a neural network generate poetry or a computer vision model identify objects faster than any human. It feels like magic. But here’s the uncomfortable truth: magic doesn’t pay the bills. When a company invests millions into an AI chatbot that can answer 10,000 queries per second, but customers still can’t find a simple return policy, something is deeply off. The tech-first mindset assumes that if you build something impressive enough, customers will flock to it. But real life doesn’t work that way.
Think about the metaverse hype a few years ago. Companies poured billions into virtual worlds, digital avatars, and blockchain land. But most of those projects fizzled because they solved a problem nobody had. They were solutions in search of a problem. And that’s exactly pourquoi innovation doit partir from the customer’s pain points, not from a whiteboard full of buzzwords. When you flip the script and start with real human frustration—like « I can’t find my order status » or « I waste 20 minutes every morning fiddling with my coffee machine »—the technology becomes a tool, not a trophy. And that shift is everything.
Pourquoi innovation doit partir du client : le vrai moteur de l’IA
Let’s dig into the core of this. Pourquoi innovation doit partir du client? Because the customer is the only one who can tell you if your AI is actually useful. I’ve seen teams spend months training models on massive datasets, only to realize that the output doesn’t match what users actually need. It’s like building a Formula 1 car for a city commute—impressive, but useless. The most successful AI deployments I’ve witnessed started with a simple question: « What is the one thing our customers hate doing right now? » From there, you reverse-engineer the solution. You don’t ask « What can AI do? » You ask « What human problem can AI solve? »
Consider a real-world example from retail. A major e-commerce platform wanted to integrate generative AI to write product descriptions. The tech team was thrilled—they built a model that could generate 10,000 unique descriptions per hour. But when they tested it with actual shoppers, nobody cared. The real friction was something else: customers couldn’t compare products easily. So they pivoted. They used the same AI engine to build a side-by-side comparison tool that highlighted differences in features, price, and reviews. Engagement skyrocketed. Why? Because they started with the customer’s frustration, not the technology’s capability. That’s pourquoi innovation doit partir from the human experience, every single time.
How to make customer-back engineering work in practice
So, how do you actually do this? It’s not about abandoning technology—it’s about putting it in its rightful place. Here are three concrete steps I’ve seen work across industries:
- Map the friction points first. Before you even mention AI, sit down with your support team, read customer reviews, and conduct user interviews. Identify the top three things that make people angry, confused, or tired. These are your innovation goldmines.
- Prototype with a minimal viable experience. Don’t build the full AI system. Instead, create a low-tech mockup of the solution—a simple flow chart, a paper prototype, or a basic script. Test it with real users. If they say « Yes, this solves my problem, » then you invest in the technology.
- Measure what matters to the customer. Don’t obsess over technical metrics like latency or accuracy alone. Track metrics like « time to resolution, » « customer satisfaction score, » and « repeat usage. » If your AI improves those, you’re on the right track.
For a deeper dive on this approach, read our article on why customer feedback drives AI innovation. It’s a practical companion to everything we’re discussing here.
The real cost of ignoring this lesson
I’ve seen companies burn millions on AI projects that never saw the light of day. Not because the technology was bad, but because it was disconnected from reality. They built a recommendation engine that suggested products customers already owned. They deployed a voice assistant that couldn’t understand regional accents. They launched a predictive analytics tool that predicted things nobody cared about. Each time, the pattern was the same: the team fell in love with the tech and forgot to ask if anyone actually wanted it.
This is why I keep coming back to the same point: pourquoi innovation doit partir du client n’est pas un slogan marketing—c’est une nécessité économique. In a world where AI capabilities are exploding, the competitive advantage won’t come from having the smartest algorithm. It will come from having the algorithm that solves the most annoying, painful, or time-consuming problem for your customer. That’s the kind of innovation that sticks. That’s the kind that builds loyalty, revenue, and real-world impact.
So next time you’re in a meeting and someone says, « Let’s use AI for this, » pause. Ask the hard question: « Does this actually help a real person do something they’re struggling with? » If the answer is no, go back to the drawing board. Start with the customer. Because that’s pourquoi innovation doit partir—and why it must stay there.