VideoAmp cut about 20% of its headcount over the summer, and the company says AI will run more of its operations in return. The reduction is not a retreat, according to its executives. It is a deliberate shift toward artificial intelligence in both products and daily workflows.

The cut landed during a turbulent stretch for the TV measurement contender. Last month, the US Joint Industry Committee re-certified VideoAmp alongside Nielsen's other two rivals, Comscore and iSpot. The committee is the industry body standardizing new measurement currencies (the datasets used to price ad inventory). Yet buyers and sellers still have not abandoned Nielsen, the legacy ratings provider they have used for decades. That reality has pushed alternative currency companies toward broader data and measurement businesses.

Who is running the shift?

VideoAmp has reshaped its leadership while repositioning the product. Andrea Zapata, a media veteran, joined in July as SVP of agency partnerships. Josh Hudgins moved from EVP of product to chief product officer in 2024, during a restructuring that included the departure of CEO Ross McCray.

Zapata framed the AI push as operational discipline, not just product strategy. As a company, "we wouldn't be prudent if we didn't take advantage of the promise of AI, which is to make workflows more efficient," she told AdExchanger. She said AI is now embedded in the company's technology and in how it operates day to day.

How does AI connect planning and measurement?

VideoAmp already sells a media planning product alongside its measurement products. Hudgins said a top priority is connecting those two workflows with AI agents (software systems that can recommend and execute actions with limited human input). The agents would flag chances to adjust campaigns while they run, improving the odds of hitting performance targets.

An agent might recommend cutting spend on one inventory source and buying new inventory that generates more unique reach, Hudgins said. The mechanism is simple: continuous comparison of plan against outcome, with machine-suggested corrections in between.

Zapata set the destination plainly. "Ultimately, what we're all trying to get is outcomes," she said. The company's overarching goal, she said, is using AI to help buyers plan smarter campaigns that perform better against their intended outcomes.

Why does the AI still need publisher data?

Hudgins was clear that AI is not an easy button. The agents depend on first-party viewer data, which VideoAmp obtains through data clean room partnerships (protected environments where companies match datasets without exposing raw records) and integrations with media sellers.

To improve video ad performance, "you really need to work directly with publishers' log-level data," Hudgins said. Building those relationships and integrations can take years. He argued the investment pays off because it shows how specific video inventory contributes to an advertiser's goals, and AI infrastructure makes that process "repeatable and scalable."

Where does automatic content recognition fit?

The third ingredient is automatic content recognition, or ACR, technology that identifies what content a device is displaying. VideoAmp uses ACR data for connected TV targeting and measurement because it reveals what viewers stream. Paired with audience data, Zapata said, ACR gives marketers a fuller picture of behavior and preference, such as what people watch when they are in the market for a car or planning a vacation. Marketers can then target advanced audience segments while controlling reach and frequency.

That combination, years of publisher integrations plus AI-driven planning, marks VideoAmp's attempt to compete beyond currency certification, in the daily mechanics of how buyers decide what to purchase and how sellers prove it worked.