Published January 7Edited May 18
As large language models (LLMs) improve, ad leaders across the industry — from holding company giants such as WPP to independents like Monks — are embracing this technology to manage the growing complexity of CTV media planning and buying.
Generative AI is emerging as a way to cut through fragmented inventory and enormous amounts of campaign data, helping teams surface opportunities that are difficult to identify through manual analysis alone. Experts who spoke to The Current said that when used correctly, LLMs don’t replace the media planner or buyer. Instead, they “compress time, surface nonobvious insights and help humans make better decisions faster,” explained Tom Burchill, group director of programmatic at Monks.
Our teams regularly feed large-scale reporting outputs — domain-level delivery, device performance, creative rotation, audience segment exposure, frequency curves — into LLMs to identify statistically meaningful patterns that would be difficult.
Harry Tong, Director of solutions engineering at PubMatic
As the industry looks ahead to 2026, here’s how experts say marketers can apply LLMs across planning, activation and measurement in CTV. The planning process often begins with a natural language brief.
Planning
A planner might select an LLM such as OpenAI’s ChatGPT or Anthropic’s Claude and input a prompt like: “I want to reach sports enthusiasts in the U.S. on premium CTV with a focus on live events,” Tong told The Current. At PubMatic, the LLM translates that request through an “AI connectivity layer” into its agentic system. “Inventory is then searched, matched based on real supply, real constraints and real forecasting,” Tong added.
At PubMatic, the LLM translates that request through an “AI connectivity layer” into its agentic system. “Inventory is then searched, matched and returned based on real supply, real constraints and real forecasting,” Tong added.
PubMatic data shows that LLMs reduced campaign setup time by 87% and sped up issue resolution by 70%. “Things that used to take days now take minutes. Those gains will only compound as this technology matures,” he added.
Measurement and optimization
During activation, the value of LLMs becomes more tactical. This is where Monks sees its largest productivity gains. “Our teams regularly feed large-scale reporting outputs — domain-level delivery, device performance, creative rotation, audience segment exposure, frequency curves — into LLMs to identify statistically meaningful patterns that would be difficult for a human to spot quickly,” Burchill said.
Things that used to take days now take minutes. Those gains will only compound as this technology matures.
Misha Williams, COO at GWI, added: “In our study of marketing and advertising professionals, we found that 4 in 10 are already using AI for audience insights, and a similar number expect AI agents trained on real consumer data to produce higher quality work. The difference is that human insight grounds AI outputs in how people actually behave, not how we assume they behave.”
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PubMatic data shows that LLMs reduced campaign setup time by 87% and sped up issue resolution by 70%. “Things that used to take days now take minutes. Those gains will only compound as this technology matures,” he added.
Increased efficiency
Across all stages, the biggest overall benefit of LLMs in CTV is “workflow efficiency,” Tong said. “Tasks that historically required multiple platforms, different user interfaces and a lot of manual handoffs, such as inventory discovery, deal setup and campaign configuration, can now be handled conversationally in minutes,” he added.
Newer models, such as GPT-5.1, are also improving the quality of insights. “The integration of third-party research sources like GWI, combined with stronger reasoning and conversational memory, makes the tool feel more collaborative and less transactional,” Burchill said.
And a new proposal, the “digital omnibus,” introduced in November, aims to make it easier for AI companies to train their models on Europeans’ data while also simplifying the EU’s notorious cookie banner. Companies may benefit from the clarity around how personal data may be used to train AI models and cookie consent banner requirements.
While it still needs approval by the European Parliament, the message is clear: Regulators, not Big Tech, are shaping advertising in Europe.
What lies ahead
Looking ahead to 2026, Tong believes agentic AI will unlock concrete operational efficiencies across the CTV ad buying ecosystem.
“We are already seeing live agentic campaigns meet or exceed KPIs across premium CTV inventory. Campaigns will increasingly be built around outcomes rather than legacy workflows. Buyers will be able to move directly from a natural language media plan into activation. Optimization and troubleshooting will become much more autonomous, with buyers choosing how much decision-making they want to delegate to agents,” he said.
Do you agree that LLMs will become essential tools for CTV marketers by 2026?
This is not about removing humans from the loop, he emphasized. “It is about removing friction from the system so people can focus on strategy, creativity and outcomes. LLMs become tools that help humans make better and faster decisions.”
CTV content itself may even become more dynamic with agentic AI. We could see elements of longer-form content generated or adapted in near real time, responding to cultural moments, live events, or shifts in user behavior, according to Gina Whelehan, group director of strategy and partnerships at Butler/Till.
“This opens up possibilities for relevance and engagement, while also raising the bar for human oversight. As machines take on more of that real-time adaptation, the role of humans becomes even more critical in setting guardrails, deciding when to intervene, and ensuring cultural context and brand integrity are preserved.”
