AI

What the Future Holds for Decision Optimization

A Guest Post by Neill Crossley, ACIB James, thank you for the opportunity to guest blog in your series on Decision Optimization. First to introduce myself….. I’m a veteran of 35 years in Retail Financial Services, 25 of which working in Decision Management, the last 17 focused on Decision Optimization, I currently work as a […]

New Analytic Approaches in Decision Optimization

There’s a lot you can do right now to optimize your decisions. You can model your decision-making to understand it better, experiment to gather data about what works and for whom. You can engage in continuous improvement, making small changes regularly. And you can optimize your decisions mathematically. But what will you be able to […]

Getting The Claims Outcomes You Want With ML/AI and Digital Decisioning

Neal Silbert of DataRobot had an interesting post on the DataRobot blog last week – Outcome-Based Claims Assignment: The “New Grail” of Insurance. He talks about using ML and AI to improve the assignment of claims adjusters to commercial claims. As he says “why make exceptions [to ensure the best assignment] when you can make […]

Driving Business Value from Advanced Analytics, Machine Learning and AI: New Webinar on Digital Decisioning

The International Institute for Analytics (I’m a faculty member) recently hosted me for a webinar on Digital Decisioning: Driving Business Value from Advanced Analytics, Machine Learning and AI. In the webinar I discussed the opportunities and challenges of Machine Learning (ML) and Artificial Intelligence (AI), introduced Digital Decisioning – it’s value, principles and how to […]

Delivering boring AI with Decision Management

A great article appeared in Information Age recently based on an interview with Tom Davenport If you want to see the benefits of AI, forget moonshots and think boring. In it, Tom argues that “if enterprises ever want to see the benefits of AI, they must embrace the mundane”. This is particularly true for companies […]

What is an Acceptable Analytic Failure?

Many speakers on predictive analytics, machine learning (ML) and AI talk about the need to allow data science teams to fail. Without failure, without a willingness to fail sometimes, it’s very hard to build a successful data science program. This is true and often a barrier for companies that find it hard to accept that not all analytics initiatives succeed.

This Week at Think 2019: Delivering Excellent Customer Experiences with Analytics and Automation

Think 2019 is here! Sharpen your skills. Get hands-on experience with the latest technology. Extend your professional network. It’s virtually impossible not to learn something new among this celebrated community of technologists and thought leaders. And have some fun while you’re at it. Explore the technologies that are redefining industries, learn from the experts, and get […]