DatAInnovators & Builders features Chief Data Officers and data leaders sharing real strategies for conquering data complexity and building AI solutions that work. Host Saket Saurabh, CEO of Nexla, delivers practical insights on tackling data variety, moving AI from pilot to production, and making transformation actually happen.
Why Your AI Strategy Is Really Just Your Data Strategy
Your AI strategy is really just your data strategy. Christian J. Ward (https://www.linkedin.com/in/wardchristianj/), CDO and EVP at Yext (https://www.yext.com/?utm_source=linkedin&utm_medium=social&utm_campaign=linkedin_companypage_traffictosite), has built his career on that principle, from NLP-driven signal analysis on Wall Street to synchronizing brand data across hundreds of endpoints. Training your own models is not where he would be spending his time.
Christian walks Saket through how opening Yext's data via MCP servers transformed client interactions overnight, why he tracks token usage by department and model, and how correlating weather data with same-store sales identified patterns across 10,000 locations.
Topics discussed:
Your AI strategy is your data strategy
Using knowledge graphs to boost AI visibility and accuracy
Opening enterprise data via MCP servers to clients
Inverse prompting and the tyranny of the blank box
Treating tokens as currency through a token exchange model
Protecting derivative data sets in data partnerships
Tracking token usage by department as a CDO metric
Building organizational consensus to drive AI adoption
8 Sept 2026
Why the real AI moat lives in your data when models are commodities
Terry Miller (https://www.linkedin.com/in/terry-miller-b1333a62), Vice President AI & Machine Learning at Omada Health (http://www.omadahealth.com/), calls frontier models "very expensive commodities." With 14 years of longitudinal data for over a million members, the real competitive advantage lives in proprietary data and the workflows built on top of it.
Terry walks Saket through how reusable templates and tightly bounded applications make agent deployments succeed, why combining traditional machine learning with LLMs unlocks new capabilities, and what "edge healthcare" means for decentralized care from the home.
Topics discussed:
Frontier models as commodities, data as the real moat
Building reusable agent templates for production systems
Tightly bounded applications as the path to agent success
Edge healthcare and decentralizing care to the home
Combining traditional ML with LLMs for new capabilities
Preventing the next wave of high-cost patients
Deep personalization as table stakes for enterprise AI
Policy gaps around autonomous AI in regulated industries
25 Aug 2026
Why grand unified data architecture kills AI projects before they start
For Anusha Dandapani, Chief Data & AI at UNICC, what keeps her up isn't model accuracy. It's the asymmetry of consequences. A wrong commercial AI call costs money, but in a humanitarian context, it can mean aid never reaches the people who need it.
She tells Saket why she'd rather run a less sophisticated model with rigorous decision architecture than drop in a state-of-the-art system, and why nobody wants to talk about data lineage until it's too late.
Topics discussed:
Weighing asymmetry of consequences in humanitarian AI decisions
Avoiding grand unified architecture in favor of minimum interoperability layers
Cost-sharing a shared AI platform across ten UN organizations
Baking responsible AI into design instead of retrofitting later
Treating data lineage as the real foundation for AI trust
Drawing the line between reversible and irreversible AI decisions
Prioritizing decision architecture over model sophistication
Reframing data quality gaps as risk decisions for leadership
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