It’s hard to overstate just how quickly Artificial Intelligence (AI) is evolving. Driven by a combination of cheaper and more powerful cloud infrastructure, richer data sets, and new analytic techniques, each new year seems to be a banner one for AI innovation.
Advances in tasks like image and sound recognition, improvisational thinking and deep learning, for instance, drive new predictions for growth in areas like autonomous driving, voice recognition and other cutting-edge capabilities.
When Google implemented its AI upgrade to Google Translate one weekend last November, the sudden improvement was such that its foreign language translations of passages from “The Snows of Kilimanjaro” were virtually indistinguishable from Hemingway’s original text!
Recent news about machines beating humans at poker, or the ancient strategy game Go, also point to more complex questions – like how AI and AI-driven automation could displace jobs in mining, administrative, manufacturing, finance and many other sectors. Some of today’s headlines even point to profound changes to societal trends. For instance, serious thought is now given to the prospect of a universal basic income.
A Broader Lens
Each innovation is impressive on its own, but I think we get an even fuller understanding of them all when we look to some broader context.
Whether this innovation juggernaut strikes you as thrilling, frightening, or both, how do we get a handle on where it’s taking us? I’m convinced that the first step is to abandon any hope of precise long-term predictions or forecasts. Anyone who tells you exactly what the future will look like is actually living in the past – a bygone era before Moore’s Law vaulted us into permanent innovation overdrive.
Instead, whether the goal is competitive advantage for one company, or social harmony for the whole world, I believe we should chart AI trends in terms of capabilities and how they mature. By doing so, we create a useful framework – if not necessarily all the fine details – of what’s to come.
In fact, that’s what’s at the heart of the Sentient Enterprise model for enterprise analytics at scale that I co-developed with Kellogg School management scholar Mohan Sawhney. We actually refer to the Sentient Enterprise as a capability maturity model. Particularly in the latter parts of our five-stage journey, we look past what even the most advanced companies are doing today – and ahead to the issues we’ll all be navigating tomorrow.
For instance, a prominent theme we address in the Sentient Enterprise is the human/machine balance in making decisions. As AI-driven capabilities and connections continue to mature and scale, more and more decisions will be made by algorithms. That means an enduring challenge will be maintaining the right feedback loops between autonomous decisioning and the humans who continue to tweak and optimize how those decisions are made.
This example shows how we may not be able to predict the exact nature of tomorrow’s business problems, but we know the human/machine balance around decisioning will remain crucial to the solution. There are many such factors and – as we see AI progress even further – I encourage you to consider the Sentient Enterprise framework for understanding where all this AI innovation is taking us.