Artificial intelligence discussions are shifting from theoretical to practical deliverables as the industry recognizes strong data foundations prove critical for realizing AI’s full potential.
“We’ve all been talking a ton about AI, but it’s not just the technology, it’s about what we’re actually delivering now. It’s moving into an outcomes era,” Julie Clark, SVP, Diversified Markets, Media & Entertainment at TransUnion, told Beet.TV Editorial Director Lisa Granatstein, ahead of this year’s Beet Retreat San Juan. “Strong data foundations are critical to realize AI’s full potential, and we’re doubling down on how we’re approaching that to make sure that marketers and our partners are able to accelerate their business.”
TransUnion is also examining how organizations cultivate talent that powers AI implementations.
Identity prevents garbage outputs
Identity systems must build on correct data to avoid garbage-in, garbage-out scenarios that undermine AI effectiveness, while solid data foundations cut through fragmentation noise.
“From a measurement perspective, you might be working across multiple different parts of the media ecosystem and you’re going to have competing methodologies,” Clark said. “How can we come together and make sure that we have a single view and real performance that is measured?”
AI isn’t magic but can make connections between missing data points and disparate information that humans cannot intuitively link, filling gaps through pattern recognition.
First and third party data scrutiny
AI-powered identity and measurement improvement requires examining all available data assets to determine which enable goal achievement and tailored messaging delivery.
“That means that we have to take a real scrutinized look at first party data and make sure that that is a strategic asset and investing in the data hygiene and enrichment,” Clark said. “Third party data to me is someone else’s first party data. So turning to the right trusted third party partners can help deliver scale.”
Connections define good measurement
Cross-platform measurement quality depends on connecting consumer touchpoints rather than managing contradictory methodologies from multiple partners, with marketers typically leveraging 16 tech platforms simultaneously according to TransUnion research with Forrester.
“From a measurement perspective, that can be really, really noisy when you have 16 different partners that you’re working with. You don’t want to have competing methodologies and it gives you contradictory information,” Clark said.
Rebuilding measurement infrastructure requires focusing on ecosystem connections and individual solid methodology rather than fragmented approaches.
Scale and precision balance
Achieving scale and precision balance requires proper foundation, AI-powered modeling that intuitively fills gaps for precision, and ability to connect disparate touchpoints while leveraging appropriate data for scale expansion.
“It is a balancing act — balancing scale and precision is making sure that you have that right foundation, that you have the ability to leverage the right modeling powered by AI,” Clark said.
“Good measurement is about connections,” Clark said. “Tech stacks are sprawling. If we’re rebuilding, it’s rebuilding on the connections that we can make across the ecosystem and making sure that we’re focusing on individual solid methodology.”
You’re watching coverage from Beet Retreat San Juan 2026, presented by Alliant and TransUnion. For more videos from this series, please visit this page.
