TITLE: AI: Why innovation must start with the customer, not the technology
CONTENT:
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.
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 marveling at the architecture itself. This is what I call the « I have a hammer, where’s the nail? » syndrome. We push applications based on existing technological capabilities, without ever asking if these applications meet a real, deep, and often unexpressed need of end-users.
This approach, which could be called « technology-push, » inevitably leads to fragmented, sometimes incoherent solutions that fail to integrate harmoniously into users’ practices. Take the example of customer service. A company might be tempted to implement an AI-powered chatbot to « modernize » its interactions. Without a keen understanding of its customers’ frustrations (too long waiting times, re