As we have moved to virtual briefings, I have increasingly been excerpting short video segments (with permission) as part of my Analyst Cam series.
Fred Laluyaux, President and CEO of Aera Technology, presents on the Aera Decision Cloud™
How many personal decisions do you make in a day? According to this article in the Harvard Business Review the average adult makes 33,000 to 35,000 decisions each day. Many of them are what it calls “autopilot” decisions. It encourages daily journaling to interrupt such decisions and to “live our lives more intentionally.”
Could you extrapolate that to an enterprise and its millions of daily decisions – some “autopilot”, some relatively simple yes/no, others much more complex which require interaction with a chain of other processes or groups?
That is Aera’s mission - digitizing, augmenting, and automating enterprise decision making. It aims to become a virtual member of your team, a digital analyst – delivering well-researched and informed business recommendations, and going even further and taking action autonomously to execute them once a decision has been made.
Fred, who has spent three decades “in the world of data analytics, applications. ERP, BI and of course, AI”, at Anaplan, Business Objects, SAP and elsewhere spends 40 minutes talking about the Decision Intelligence branch of the AI science. He talks about relatively simple decisions which can be processed using traditional CPUs, and others way more complex requiring GPUs. He talks about finance processes which are used only periodically over the course of a year compared to one at Unilever where they use daily ingestion of retail store sourced SKU data to drive demand forecasts. He talks about big swings in companies which went from inventory shortages during COVID to surplus inventories. The customer examples he provides are complex, global ones.
I was particularly fascinated by the platform feature of a Control Room where all kinds of decisions can be tracked and continually improved. You can ask questions like “Is there a bottleneck somewhere? Is the quality of data degrading? Is the quality of the algorithm increasing? Why? Why are people rejecting some recommendations? They may have a very good reason to do so.”
With the volume of decision data the platform is accumulating, you can vary blends of man and machine in next-best actions.
Very nicely done. Using the lexicon of the HBR article it would allow employees and groups to “live their corporate lives more intentionally.”
