Summary: Let’s start by clarifying the difference between RPA (Robotic Process Automation) and IA (Intelligent Automation). Then we’ll show why AI/ML inside Intelligent Automation is the secret sauce that really makes this work.
Added by William Vorhies on November 18, 2020 at 9:00am — No Comments
Added by William Vorhies on October 24, 2020 at 11:30am — No Comments
Summary: Too many solutions. We are at an inflection point where too many vendors are offering too many solutions for moving our AI/ML models to production. The very real risk is duplication of effort, fragmentation of our data science resources, and incurring unintended new technical debt as we bind ourselves to platforms that have hidden assumptions or limitations in how that approach problems.
Added by William Vorhies on November 25, 2019 at 9:44am — No Comments
Summary: AI/ML itself is the next big thing for many fields if you’re on the outside looking in. But if you’re a data scientist it’s possible to see those advancements that will propel AI/ML to its next phase of utility.
Summary: Based on a McKinsey study we reported that 47% of companies had at least one AI/ML implementation in place. Looking back at the data and the dominance of RPA as the most widely reported instance makes us think that the number is probably significantly lower.
Summary: Digital Decisioning Platforms is a new segment identified by Forrester that marries Business Process Automation, Business Rules Management, and Advanced Analytics. For platform developers it’s a new way to slice the market. For users it eases integration of predictive models into the production environment.
Added by William Vorhies on October 30, 2018 at 8:30am — No Comments
Summary: Advanced analytics and AI are the fourth great lever available to create organic improvement in corporations. We’ll describe why this one is different from the first three and why the CEO needs the direct help of data scientists to make this happen.
If you’re a CEO or any other flavor of top executive leading a…Continue