AI Is for Everyone

I was driving my 82-year-old mother to the doctor this week when she opened the subject of artificial intelligence. We talked about the good, the bad, and the ugly of AI. After 10 minutes the big takeaway was that people are ready for it. Everyday applications like chatbots on Bank of America’s website, or Google Maps offering a faster route as you drive demystify this amazing technology, and yet regular people are still very much on the receiving end of innovation.  

Now is the time to get everyone – consumers, patients, citizens, and CEOs – involved directly. Once we do, we’ll speed up the pace of improvement, and add fairness and transparency to a system that is already running much of our lives.  

 An AI Census for Closed Loop Supply Chains  

Bringing AI to the masses starts with showing people where it already is, and what it does. For that, we need something like the 1086 Domesday Book of medieval England – a census. The chart below is basic map of where AI currently works in the end-to-end supply chain to deliver what we as consumers need, while making money for investors.  

The exciting thing about this view of AI is how widely it is being used to improve resilience, responsiveness, and responsibility. On the demand side, AI in sales channels tunes prices and promotions so retailers like Tesco or Walmart can offer better deals and increase sales, while AI analysis of customer use experiences, as reflected in social media or IoT data from the field as in Goodyear tires or John Deere tractors, help improve product performance.   

 On the supply side, AI helps sourcing teams at companies like Unilever and Microsoft ensure supplier compliance with environmental and labor standards. AI also provides predictive maintenance for companies like BMW and Honeywell to keep factories running efficiently. Plus, AI is a key technology enabler for real time route optimization in the logistics networks of companies like Kroger and DHL.   

There is one point on this closed loop supply chain where AI investment needs to increase to make sustainable supply chains a reality: “Regenerate”. This means renewal after the customer has finished using whatever they bought. Materials circularity, including returns and recycling, is one kind of “regeneration”. Revenue renewal via subscriptions or high customer loyalty is another that “regenerates” the business topline.   

 Finally, and from a shareholder perspective most important, is the “regeneration” of planning insight, which improves a CEO’s ability to make strategic decisions with a full lifecycle view of how the business grows and protects its ability to make money. This kind of “regeneration” means harvesting operational lessons learned with real data, churned through a self-improving AI and machine learning system built around big KPI’s like earnings per share, net margin growth, and customer retention.   

Plan to Win with AI  

The central location of planning in this picture is not accidental. It sits across both supply and demand, including not only inventory planning and in-stock performance, but also new product development, channel strategies and M&A. AI allows planners to automate predictions from the entire closed loop supply chain, which should lessen dependence on strategy consultants doing one-time projects.  

AI is the breakthrough that ties it all together in a “Regenerative Business Planning” tech-stack that bottoms out in IoT on the shop floor and social media trends among consumers. On top is an AI-assisted decision support system that lets business leaders apply judgment based on detailed, live operational data.   

Automate Experience, Not Control  

Business data, connected in a closed-loop lifecycle has until recently been too big and unstructured to manage with technology. Now however, when even your grandmother knows enough to have a chat about AI, maybe we are there.   

AI is awesome because it can learn from experience at the speed of light. Trained properly with data sets that characterize strategic and operational questions, AI empowers humans to be creative, incisive, and bold at times when historical data isn’t enough.  

 If we democratize AI so everyone understands how well it handles routine decisions, we might start a bottoms-up wave of innovation that eases adoption by demonstrating, just like Google Maps, that trusting the machine doesn’t mean giving up control.