H2O.ai Innovation Day Summer ‘22 included a customer insights panel made up of Prince Paulraj, AVP, Data Insights and Chief Data Officer at AT&T , Chris Throop, Managing Director and Global Head of Data Science at Castleton Commodities International and Sean Otto, Director of Advanced Analytics at AES .
One of the questions panelists were asked was “what advice would you give those getting started on their AI journey?” As panelists from AT&T, Castleton Commodities International and AES reflected on their own experiences of getting started in their AI journey, there was an overwhelming emphasis on proof-of-concept as well and managing expectations across business users.
Paulraj opened the discussion with his perspective on AT&T’s AI journey. He reflects how in the beginning AT&T “didn’t worry about the automation, we didn’t worry about using sophisticated tools and technology, but we started very simple.” Rather than focusing on advanced capabilities, AT&T focused on simply getting data into the platform in order to build a model and show results. A “logic-driven approach” was paramount for AT&T to gain buy-in from the business. They focused on a POC that demonstrated what value AI will bring to the business, what cost avoidance and what cost savings. This drove a change in company culture around AI at the enterprise level.
Castleton Commodities International then addressed the question. Throop strongly agreed with Paulraj’s emphasis on a POC and starting simple, but added “start with data, but also start with a methodology and governance model.” Throop then addressed potential challenges namely that “business users may feel disenfranchised.” CCI faced obstacles around team bandwidth and limited capacity, which left “a few people very frustrated because their projects never get to the top of the list.” Throop then proposed “a hybrid engagement model, where maybe two thirds of the team is focused on strategic projects. And then one third of the team is focused on tactical point in time, you know, really rapid market ascent, working with commercial leadership to set aside a kind of capacity in that fashion. I think it can be helpful because as good as the tool [ H2O AI Cloud ] is to accelerate the analysis, there’s always more, there’s always more to do and so prioritization is important.”
Lastly, we hear from AES. Otto agrees with Paulraj and Throop, adding “part of me wants to say don’t design a model for 12 months. Okay, start off talking about it, advertising it [internally]. Get the foundation set up, which is how are you going to run your compute? How are you going to create your endpoints? Where’s your data? What’s it doing? You know, start bringing all of that in. Start working with all the DBAs. Get them in from an infrastructure level to then start pulling that data in, and then start creating some models.” Otto emphasizes the importance of “minimum viable governance.” AES does an exercise with their business called “design thinking sessions” where the team has many conversations about what is working, what is not working, and what needs to be prioritized, which helps the group align on what is important and manage expectations.
Proof-of-concept was an integral part of AT&T, Castleton Commodities International and AES starting on their AI journey. However, it is clear that methodology and managing expectations were also critical in advancing AI maturity.
Watch the customer insights panel and playback of H2O.ai Innovation Day Summer ‘22 here . Sign up for a complimentary use case consultation workshop with some of our top AI experts. Learn how you can get started with your AI Journey .
