Chalice’s Ali Manning: Brands Should Use AI for Growth, Not Just Efficiency

Why It Matters

AI‑driven growth redefines advertising ROI, giving brands and publishers a decisive competitive edge and accelerating the evolution of CTV buying.

Key Takeaways

• Brands should prioritize AI-driven growth over mere cost reduction
• Successful AI adoption requires integrating data, modeling, and outcome prediction
• Custom AI models outperform generic ones for enterprise-specific objectives
• CTV advertising benefits from AI by optimizing pricing and audience targeting
• Early AI testing with available data beats delayed, extensive data projects

Summary

In a recent interview, Chalice CEO Ali Manning argues that the advertising industry must shift its AI focus from cost‑cutting to genuine brand growth, positioning ad dollars as investments rather than expenses.

Manning notes the conversation has moved from speculative future benefits to concrete results. He stresses that true AI value comes from marrying three pillars—high‑quality data, robust modeling, and prediction of outcomes that matter to the C‑suite. Brands like Hershey’s and Bayer are already using AI to drive incremental store sales and identify new‑to‑brand customers, proving the approach works.

He cites Hershey’s AI‑driven Halloween candy sell‑through and Bayer’s Claritin shelf‑reach model as examples of outcome‑focused campaigns, contrasting them with traditional proxy metrics such as clicks. In connected‑TV, AI is beginning to price impressions rationally, matching high‑value, low‑frequency viewers with premium inventory, and Chalice is collaborating with Paramount to access show‑level data.

The implication is clear: brands that integrate data, custom models, and outcome‑based metrics will outpace competitors, while publishers delivering quality, measurable content will capture more spend. Early, iterative testing with the best available data is recommended over lengthy data‑cleaning projects, signaling a rapid shift in ad‑tech strategy.

Original Description

SAN JUAN, Puerto Rico — Companies that focus artificial intelligence implementation with an eye toward business growth, rather than expense reduction, will outpace competitors. The view is that AI’s transformative power should drive real outcomes that matter to brands rather than operational efficiency alone. But getting brands to accept that remains a challenge.

“Who’s going to leap ahead of their competition are brands and publishers who are focused on using AI for growth, not necessarily cost cutting,” Ali Manning, co-founder & COO of software platform Chalice told Beet.TV contributor David Kaplan at the Beet Retreat San Juan. “That’s something that will happen, but the AI focus should really be on how is this going to grow my brand?”

This approach transforms advertising markets into innovation engines where advertiser dollars become investments rather than expenses, rewarding quality publishers while enabling brand growth.

From proxy metrics to business outcomes

Leading brands move beyond platform-driven proxy metrics like clicks and video completion rates toward C-suite relevant outcomes, with companies like Hershey’s using AI to drive incremental store sales and Bayer modeling new-to-brand customers for Claritin at shelf level.

“Since we’ve been beholden to a few companies that have held the power of AI models for themselves, they’ve forced advertisers into buying proxy metrics,” Manning said. “They’re not really about the advertiser, the brand’s business. They’re about the platform’s ability to serve a bunch of advertisers at once.”

Successful implementations focus on outcomes executives discuss rather than marketing manager optimization requirements.

Custom models deliver enterprise value

Most companies handle one or two AI implementation components well, but maximum value requires integrating best available data, optimal models, and relevant outcome prediction, with custom enterprise models outperforming generic solutions.

“Most don’t predict an outcome that really matters to the brand. Most companies give every customer the same AI model, and that’s just not the way enterprises are going to get the most use out of AI,” Manning said. “You don’t want to use the same model if you’re one cell phone company as your two other competitors. You want a model that’s built and deployed directly for you.”

“Messy” data beats perfect delay

Brands should begin testing with available data rather than pursuing lengthy data cleaning projects, as Chalice works with advertisers using messy data and consumer packaged goods companies with minimal datasets.

“You don’t need all of the data in the world. You don’t need it to be clean. We work with advertisers who have messy data. We work with CPGs who have essentially none,” Manning said. “What we do is we find the best available data and start modeling on that. That’s so much better than trying to do a one year data project that turns into a five year data project.”

CTV signals create pricing opportunities

Connected TV presents AI opportunities beyond reach optimization through rational pricing based on consumer access frequency, with high-value consumers requiring different investment levels based on viewing availability.

“If I am a brand and I have a consumer who is very high value to me, that consumer might be someone who watches a lot of non-premium content, or they could be a consumer who you can only get once a week,” Manning said, citing working mothers with limited viewing windows.

Chalice partners with publishers like Paramount to access show-level data for enhanced decisioning capabilities, with select publishers positioned to transform market dynamics through the next upfront cycle and beyond.

“It’s going to be a few publishers who are going to really transform and run ahead of the market in this next upfront cycle,” Manning said.