Digital Decisioning

We have discussed in previous blog posts the relationship of BI to AI, and the way advances in data science became foundational to breakthroughs in artificial intelligence. These breakthroughs lead ultimately to automation breakthroughs, and automated business.

There is a virtuous cycle in place today, where BI helps enable AI and then AI changes the way we provide business intelligence to our enterprise. For example, our customer response centers have been using machine learning to optimize the matching of agents to customers, demonstrating machines do this much better than humans. This is not only an example of BI in customer response centers incorporating an AI capability, it is also an example of AI changing the way we achieve BI, and an illustration of that virtuous cycle.

We can extend this idea toward a bigger idea we have begun to call Digital Decisioning. Every process within every business is on a journey to digitally remove the human labor involved in decision-making. That’s the future of BI. Strangely, we might even claim BI is disappearing — being assimilated — as we appreciate a transformation in which intelligence of our business becomes almost entirely based on our information flow and associated algorithms and statistics. This assimilation assumes the thinking human, the human decision maker, is freed from the picture.

The Digital Decisioning trend is appearing all around us as AI becomes pervasive.

Consider the ride-sharing example. Jill decides she needs a ride. She uses her phone to request pickup with her destination as the top of the hill. The Lyft app on her phone determines her nearest available driver, just three minutes away, is Jack. Machine learning has been used to determine not only that Jack is the best choice, but also to ensure there are enough drivers at this hour, and where they should be optimally positioned. Then Jack gets instructions to pick up Jill, while Jill is provided a visual animation of his journey toward her location. The best route to the top of the hill is mapped out for them after pickup. Then when there is an accident on their route causing delays, Jack is provided an alternative route saving them time, as they safely and efficiently reach the top of the hill. No broken crowns. No tumbling down. And their entire journey is engineered by various implementations of AI. The only human decisions required were Jill seeking a ride, and Jack being willing to make some extra money driving for Lyft.

Now what if Lyft, or Uber before it, had stopped short of exploiting AI? What if these ride-sharing companies had just used analytics to chart the closest drivers available for Jill, the best routes for Jack, and then dashboard these possibilities for human dispatchers and for participants, expecting these humans to make many more decisions? This would have limited these companies to becoming only incrementally better than taxi services. Fortunately, these companies embraced the powerful possibilities of a digital decisioning system. Jack and Jill, fetching a pail of water, ride into the future.

As humans, we might regret (or even fear) the way our removal from a system or process increases the power and the transformative impact of new digital possibilities. But don’t sing the digital blues. Removing humans from the plant floor, supply chain, shipping logistics, and shelf-stocking, which shifts the human’s eyes away from dashboards and charts of anomalies, allows us to put our attention toward creative anticipation of tomorrow’s reinvention.

We can imagine a future where the power of digital decisioning just provides us more time to golf, go fishing, or maybe just stack up all the money we make. But the reality is humans must always work. They must always make decisions. The virtuous cycle between BI and AI, and the powerful new digital decisioning that results, is merely changing what we must decide.

And what must we decide? Well, we get to decide what’s next.