Sentient Enterprise and Artificial Intelligence (Part 2 of 2)
Over the last few months, I’ve had the chance to engage with customers and industry analysts about a range of topics in the field of Artificial Intelligence (AI), and I’ve been struck on how effective the Sentient Enterprise is in addressing the most common questions and misconceptions about AI. This two part blog series focuses on two of those questions:
- What is the difference between automated and autonomous decisions?
Enterprises can sometimes get ahead of themselves on their way to using AI. Maybe they’ve set up a few machine learning models and have had new algorithms work their way into previously deployed SaaS applications. Inside the organization, it feels like they’re checking all the right AI boxes.
But the true end goal of AI in the enterprise is something much more sophisticated. Ratzesberger and Sawhney expressed it succinctly in their book, The Sentient Enterprise, noting, “Our objective is to position the enterprise in such a way that analytic algorithms are navigating circumstances and making the bulk of operational decisions without human help.”
Imagine if key business functions were being driven by algorithms with the necessary autonomy to self-learn and change tactics at a level of speed and accuracy that far surpasses any human team.
It’s a vastly different idea than algorithms simply making predictions while embedded into a back office or customer facing system. Embedding an algorithm into a system to improve decision making is old news. But autonomous decision making is another story altogether.
When I was at Dell 15 years ago, we could accurately predict the likelihood of churn for millions of customers. We also calculated fully burdened customer profitability figures for every customer. We could also measure the customer satisfaction at 17 different customer support sites around the world, and there was a pretty big gap in quality of service between the best and the worst customer care sites. Rather than randomly routing customers to the 17 different sites, we started to route the most at-risk and profitable customers to the best customer care sites. When a customer called in, keyed in their service tag, the model would identify what cluster they belonged to and route the call to the appropriate call center within milliseconds.
While effective and cutting edge for its time, that example is a simple automation based on a static model. The model never changed unless we deliberately created and loaded a new model. And the world changed a lot for that industry at that time, with the emergence of smart phones, tablets, data center virtualization, and cloud computing, to name a few. Rapid change like that often overwhelms our abilities as humans to sense and react – including us humans in the profession of building algorithms.
What’s new and exciting is that machine and deep learning now allow these embedded algorithms to learn and improve their efficacy through more data, not manual intervention. The final stage of the Sentient Enterprise offers a great long form account of thinking through the value and implications of making this enormous leap. You begin to get a real sense of what it’s like to be part of an enterprise where decisions are being made autonomously.
“Picture the absolute thrill of monitoring and strategically intervening as your business acts as one organism: self-aware, proactive, able to sense micro trends around the next corner, and able to signal the next crisis or the next new thing and respond quickly, with smart strategies to prepare and optimize performance as circumstances inevitably change.”
Chad Meley is Vice President of Solutions Marketing at Teradata, responsible for Teradata’s Artificial Intelligence, IoT, and CX solutions.
View all posts by Chad Meley
Chad understands trends in machine & deep learning, and leads a team of technology specialists who interpret the needs and expectations of customers while also working with Teradata engineers, consulting teams and technology partners.
Prior to joining Teradata, he led Electronic Arts’ Data Platform organization. Chad has held a variety of other leadership roles centered around data and analytics while at Dell and FedEx.
Chad holds a BA in economics from The University of Texas, an MBA from Texas Tech University, and performed post graduate work at The University of Texas.
Professional awards include Best Practice Award for Driving Business Results in Data Warehousing from The Data Warehouse Institute and the Marketing Excellence Award from the Direct Marketing Association. He is a regular speaker at conferences, including O’Reilly’s AI Conference, Strata, DataWorks, and Analytics Universe. Chad is the coauthor of the book Achieving Real Business Outcomes From Artificial Intelligence published by O'Reilly Media, and a frequent contributor to publications such as Forbes, CIO Magazine, and Datanami.