Artificial intelligence tools continue to proliferate in the workplace, but the best approach to implementation is a deliberate one.
Getting comfortable: Minnesota Timberwolves and Lynx CEO Matthew Caldwell was open about his organization being not “very advanced” in its use of AI. To start his personal journey with the technology, Caldwell carved out 10-15 minutes per day for about a month to experiment with Claude and said he was “blown away” by its potential. He later asked his executive team to do the same. “You can’t delegate AI to an innovation group or your IT team,” he said. “It’s got to start with leadership.” The Wolves and Lynx are also amid an ambitious data warehousing project that Caldwell said will give the organization a strong foundation from which to leverage AI internally.
Best practices: Deepali Vyas, ZRG Partners global head of data and AI sector, laid out a three-level framework for implementing AI into an organization.
- Level 1: Individual users
- Level 2: AI workflows
- Level 3: AI native
Level 2, Vyas said, is where companies find the most ROI. She used ticketing as an example in sports, drawing on an anecdote Caldwell shared about the Timberwolves/Lynx’s marketing team relying on ticket pricing and inventory data shared manually by their business intelligence team when crafting single-game ticket offers. “The [AI] prompt your marketing team would do is, ‘Take this data and upload it to an agent.’ And your [BI team’s] reports also have an agent behind them,” Vyas said. “Those two agents are talking to each other in real time to update that report for you to make decisions at the end of it.”
Vyas added she’s heard of organizations outside of sports beginning to hire what she calls “chief agent operators,” who are tasked with managing a team of AI agents rather than humans. She estimated these roles fetch between $250,000-$600,000 in annual compensation.
What it means for sports: Playfly Sports President Chris Marinak said that while some sports CTOs are charged with managing their organization’s AI initiatives, he’s also begun to notice sports organizations hiring specific heads of AI or innovation, sometimes outside of their traditional technology structure.
Vyas said the chief AI officer’s reporting structure can be a puzzle because of the role’s focus on ROI. “When you have a chief data officer or a CAIO under the CIO, it creates a lot of friction,” she said. “We’ve advised clients that, dotted line, [a CAIO] should have line of sight to CEO. But if you’re going to operate across business lines, [reporting to the] COO makes a lot of sense.”
Something to chew on: Marinak raised an interesting, sports industry-specific counter to the narrative of AI being an inevitable, disruptive force. “On the sports side, I don’t feel like AI has been very disruptive, because AI has been in the sports side for a long time. ‘Moneyball’ was AI,” he said. The industries being most acutely disrupted, Marinak contended, are the ones that have not historically emphasized data and analytics. “The business side of sports is a bit in between that,” he said. “There’s been improvements with AI. But to date it hasn’t been this massive, massive disrupter.”
USA Sports President Matt Hong agreed, arguing that “as disruptive as AI appears to be relative to media, it’s nothing compared to when digital came along in 2008, 2009, 2010.”
