FastForward #70: What baseball teaches us about AI

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Hi everyone. Thanks for reading. I appreciate each and everyone of you. Just a heads up, that I'm headed on vacation at the end of next week, so there will be no FastForward for the next couple of weeks. Just a reminder, if you need a professional moderator for your event, drop me a line at moderate@fastforward.blog. If you like my newsletter, please share this week’s edition with a friend, and encourage them to subscribe, it really helps.💌 Sign up here.<br>ForwardThinking 🤔<br>What baseball teaches us about AI<br>As many of you know, I'm a Boston sports fan, especially the Red Sox. This year they are among the worst teams in the league, and it seems their story is a case study in how difficult transformation around AI can be. Ownership brought in a hardcore stats geek in Craig Breslow in Fall of 2023 to reshape the ball club. He proceeded to put his analytical stamp on the organization, and so far let's say, the results have not been terribly impressive with the worst statistical start at home since 1932 and a historically bad start overall.<br>Any executive who has tried to transform an older organization like the Red Sox knows there is going to be grumbling. But unlike most corporate executives, who answer to a board and shareholders, a pro sports exec like Breslow has to answer to the press and fans too, who think they know more than he does about running a team — and to be fair, given the current state, maybe we do.<br>Not everything has been an abject failure. The changes around pitching development have been successful for the most part, but when it's June, and your team is 33-46, and 13-25 at Fenway Park, well, clearly something is very wrong. We've been hearing for 15 years that data should drive decision-making, and in an age where there's more data than ever with AI to help parse the numbers, success should be at everyone's finger tips, right?<br>At the end of April, less than a month into the season, Breslow decided the problem was the manager and coaching staff and one wild night he fired them all, except for the pitching coach (whom he had hired) and a handful of others, and replaced them with his own guys. Little has changed. The team is still losing.<br>The firings couldn't cover up the weak roster that Breslow and his army of analytics nerds built. While injuries to star outfielder Roman Anthony and ace pitcher Garrett Crochet didn't help, the roster is still dominated by players who don't belong on a big league team, problems all of the front office's own making.<br>The human factor<br>This could be a case of simply over indexing on the analytical side of the equation. At the end of the day, as I've written in the past, players are human beings under those caps, and they bring with them all the emotional baggage and imperfections that we all bring to the table.<br>In an enterprise setting (which to be fair the Red Sox are ultimately), there is a growing belief that AI agents can solve that problem. But they aren't nearly as reliable as you might think, at least in my experience. I have created agents that worked fine for a couple of weeks, only to break or change in undesirable ways. Sounds a lot like us, maybe not for the same reasons, but unreliable all the same.<br>Look, every problem can't be solved by crunching numbers and throwing AI at it. Sometimes it takes the art, taste, subtlety and creativity that only humans can bring to bear on a problem. And I think that's what Breslow may have forgotten. Unforgiving fans refer to him as BresBot in the comments section of the Boston sports pages. It's a clear sign that he missed the fact that he wasn't just pushing numbers around a page, he's dealing with real humans.<br>Even as Breslow seems to recognize this, he hasn't been able to change the outcome. “If you are blindly following a model and knowing that the model is imperfect, you are going to make mistakes. The job that I have is to synthesize all of the information sources that we have. And we want to constantly improve all of that information, including a bunch of our models," he told the Boston Globe's Tim Healey in an article earlier this month.<br>Image by Curated Lifestyle for Unsplash+Businesses face similar decisions about data and AI all the time. You may recall that Klarna learned a harsh lesson in 2024 when it replaced 700 human customer service agents with AI and framed it as an efficiency move. By last year, the CEO admitted he had mistakenly emphasized cost over quality, and some customers let him know in no uncertain terms that they preferred talking to a human, especially when it came to financial issues. Klarna responded by rehiring people. While it didn't abandon the AI altogether, it realized it wasn't applicable in every situation.<br>What AI and modeling can do is help a baseball staff sharpen a good player’s talent. It can’t magically turn a fringe player into a difference maker for the team. In the enterprise world, the same idea...