The window for AI to serve as a competitive differentiator in advertising is closing fast. Proprietary, market-level data - the kind that cannot be licensed, averaged, or approximated - is emerging as the real foundation for marketing outcomes. For marketers, the question is no longer which AI tool to pick, but whether their partners have the data to make it work.
"As AI adoption accelerates, proprietary data, not proprietary technology, will become the true source of value. AI is leveling the technology playing field, and it's only going to level it further," said Michael Collins, Chief Executive Officer of Locality, a local TV advertising platform built for the converging world of broadcast and streaming.
Effective TV campaigns depend on a connected end-to-end dataset. AI models trained on that data can continuously improve planning, targeting, and measurement across the full campaign lifecycle.
Why national data falls short in local TV
Many platforms still attempt to retrofit national solutions for local campaigns. National campaigns offer standardized datasets that lack the granularity needed to reflect how individual markets actually behave. "Generic AI tends to give you a statistically correct answer, whereas local intelligence gives you the contextually correct answer for that market," said Locality Chief Technology Officer Kouros Esfahany.
Marketers now have access to a richer picture of local behavior than ever before. This includes identity data from households and devices, cross-platform media exposure data, campaign outcome data such as conversions and sales, and market context covering DMA, local behavior, and historical performance. When unified into a single intelligence layer - rather than fragmented across different tools - this becomes data AI can optimize for real business results, not just impressions or proxy metrics.
AI models trained on national averages rarely fail in obvious ways. They optimize confidently toward the wrong outcome. Each DMA, zip code, and neighborhood shows distinct behavior. Models trained on market-level exposure, performance, and behavioral data can detect differences in engagement across channels at each geographic level and translate those differences into media strategies tailored to that area.
"It's really balancing the scale and precision," Esfahany said. "The DMA level is best for planning and budget allocation, and captures how media is consumed regionally. Zip and neighborhood are best for refining those targets and measuring and capturing the local nuances. The key insight is that geography isn't just location; it's a proxy for behavior, context and intent."
Local insights as a stronger predictor of outcomes
Optimizing for location is not a new idea. Location has long been one of the best indicators of intention and context. What has changed is the shift in mindset. "Taking into account how consumers are engaging with their local environments is the rule, not the exception," Collins said. "These behaviors are strong indicators of intent and provide valuable context. Ignoring them is a missed opportunity for marketers."
AI for Marketing strategies increasingly depend on this type of data foundation. Locality's Audience Engine applies the principle by learning continuously from a long-term history of attribution, viewership, behavioral, and geographic signals. Insights from one campaign inform planning, targeting, and measurement for the next.
"For marketers, this translates into a more precise audience definition, a better media mix decision, and continuous optimization as the campaign runs," Esfahany said. "The most important part is every campaign feeds the system, so it gets smarter over time."
The compounding effect shows up in campaign performance. In a Q2 2026 campaign, a leading home improvement retailer tested a custom Audience Engine segment against a traditional home improvement category shopper segment, measuring foot traffic to retail locations. The Audience Engine segment started stronger - visitor penetration was 15% higher before any ads ran, reflecting the precision of locally-trained behavioral data. Once the campaign launched, ad exposure drove 20% greater visit lift among the Audience Engine segment. The campaign is still in flight, with early results showing strong performance.
Five ways to apply local intelligence in TV campaigns
Collins and Esfahany offered five practical steps for marketers who want stronger outcomes from local TV campaigns.
Treat local as a planning layer, not just activation. Incorporate market-level data into strategy before media investment is allocated, not after the plan is set.
Prioritize data quality over AI claims. Ask where the data comes from. AI models are only as valuable as the data used to train them. Market-level data often reveals patterns that national datasets miss, especially for local broadcast where exposure is not logged the way digital impressions are.
Ask how partners validate outcomes. Optimization without measurement is just automation. If a partner cannot measure the result, you do not know the outcome.
Look for systems where measurement feeds back into planning. Planning, activation, and measurement often live in separate tools, so what a campaign learns in-flight rarely changes the next buy. The strongest partners carry outcome data forward. This matters more in local TV, where every DMA behaves differently. A system that does not carry learnings forward starts from zero in every market, every time.
Prioritize local insights over the national average. Coordinated local execution, backed by market-specific intelligence, typically delivers better results on the outcomes that matter most: conversion, in-market share of voice, and brand recall by DMA.
Why this matters for marketers
As local TV shifts toward audience-based, data-driven buying, marketers who combine AI with meaningful local intelligence will deliver more relevant messaging and make smarter planning decisions across both streaming and broadcast. For AI Learning Path for CMOs and senior marketing leaders, the takeaway is clear: AI is the mechanism, but proprietary local data is the moat. The platforms that own that data - and the marketers who demand it - will have the edge as the technology playing field levels out.
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