The road to AI leads through information architecture
1The road to AI leads through information architecture.
Ford drove the first automobile down the streets of Detroit in 1890. It would take another 30 years before the company streamlined production and made cars available to the mass market. The obvious lesson: Sometimes technology has a long gestation period before we can scale it for everyday use. But, digging a bit deeper, there is a more profound lesson.
Over the first hundred years of the self-propelled vehicle, manufacturers established essential building blocks — standard components like the combustion engine, steering wheel, and axle. These building blocks enabled scale, which led to wider adoption. And, as is often the case, the building blocks catalyzed complementary innovations, which then helped improve the building blocks.
Consider that in the first generation of vehicles (1750-1850), if a person wanted a means of transport, they had to design and fabricate every component. This “design” phase produced unique artifacts such as the Cugnot Steam Trolley, the first self-propelled land-based vehicle. Having gone through several additional phases, including build and repair, we’re now at a point in which we can pick out a car and drive it off the lot.
The evolution of the auto industry is similar in form to the currently nascent world of artificial intelligence. And like the auto industry, in order for AI to flourish, organizations must adopt and embrace a prerequisite set of conditions, or building blocks. For example, AI requires machine learning, machine learning requires analytics, and analytics requires the right data and information architecture (IA). In other words, there is no AI without IA. These capabilities form the solid rungs of what we call the “AI Ladder” — the increasing levels of analytic sophistication that lead to, and buttress, a thriving AI environment.