As AI Adoption Grows, Nearly Half of Advertisers Plan to Bring Media Planning In-House

As AI reshapes how campaigns are planned, executed, and optimized, a growing number of advertisers are reconsidering one of the industry’s foundational structures: the role of external agencies in media planning. New research suggests that nearly half of brands are preparing to bring more of that function in-house, signaling a broader shift toward control, data ownership, and AI-enabled decisioning.

According to a survey of 120 senior brand and agency leaders conducted by Keen Decision Systems, 44% of advertisers plan to internalize more of their media planning capabilities in 2026. The move comes as marketers look to maximize budget efficiency while leveraging AI tools that increasingly make planning, forecasting, and optimization more accessible internally.

AI Lowers Barriers to In-Housing

The timing of this shift is closely tied to rapid AI adoption across marketing workflows. Nearly half of marketers (47%) are already using AI for brainstorming campaign ideas and media planning, while 44% are applying it to campaign creation. Meanwhile, 26% of advertisers are actively testing or spending on ads within generative AI environments such as ChatGPT and Gemini, with another 23% still researching.

These developments are reducing the operational friction historically associated with in-house media planning. Tasks that once required specialized agency expertise, from channel allocation to forecasting, are increasingly supported by AI-driven tools.

But the more important shift is strategic. As AI makes it easier to model scenarios and simulate outcomes, brands are gaining greater control over how decisions are made, not just how campaigns are executed.

Budgets Are Growing—But Being Reallocated More Aggressively

The push toward in-housing is happening alongside overall budget expansion. The survey found that 62% of advertisers expect their budgets to increase in 2026, with social media leading the way at 63%, followed by retail media networks (56%) and AI chatbot advertising (50%).

However, those increases mask a more dynamic pattern of reallocation. Social media, despite being the top area for increased investment, is also the most common source of budget cuts, with 27% of advertisers planning to pull spend from certain platforms to fund others. AI chatbot advertising and retail media networks show similar duality, with 16% of marketers planning to reduce spend in each.

This fluidity reflects a broader industry dynamic: many digital channels have reached saturation, prompting marketers to continuously rebalance spend in search of incremental performance gains rather than simply scaling existing allocations.

As Bradley Keefer, CRO at Keen Decision Systems, told StreetFight: “Success in a budget-conscious climate requires more than just resourcefulness. Our survey found that the leaders in this space are exploring every avenue at their disposal—including retail media and AI chatbots—while balancing their funnel and measuring real business impact. This is the recipe for sustainable growth in spite of headwinds.”

From Optimization to Marginal ROI

AI adoption is accelerating a deeper shift in how media decisions are made. Rather than optimizing based solely on past performance, marketers are increasingly focused on marginal return—determining where the next dollar will generate the greatest incremental impact.

This aligns with the capabilities of modern marketing mix platforms, which use predictive modeling to simulate budget shifts and forecast outcomes before spend is deployed. As these tools become more accessible, the ability to make those decisions internally becomes more feasible.

For brands, this represents a shift from reactive optimization to proactive allocation—moving from reporting on what worked to predicting what will work next.

Retail Media and AI Highlight Diverging Strategies

Retail media illustrates how these dynamics are playing out unevenly across sectors.

Among retail brands, 26% plan to reduce investment in retail media networks to fund other channels, suggesting a reassessment of performance in a crowded ecosystem. In contrast, 72% of technology brands plan to increase their investment in retail media, with 36% reallocating budget from AI and chatbot advertising to support that growth.

These divergent strategies highlight a key reality: there is no single “correct” media mix. Instead, marketers are tailoring allocations based on where they see the strongest incremental return within their category.

Data Challenges Remain a Constraint

Despite the promise of AI-driven planning, data limitations remain a barrier. Thirty percent of respondents cited data quality and reporting issues as the biggest challenge to reliable forecasting, followed by channel complexity (21%) and shifting stakeholder priorities (18%).

Marketers are also split in their use of data. While 42% rely on a mix of historical and real-time signals, 29% prioritize real-time data, and 28% still depend primarily on historical marketing mix modeling. This reflects an industry in transition, balancing legacy frameworks with emerging capabilities.

Owning the Decisioning Layer

Taken together, the findings point to a structural shift in marketing operations. As AI reduces the complexity of planning and forecasting, more brands are building internal capabilities that were once outsourced. But the move toward in-housing is not simply about efficiency or cost control. It is about owning the decisioning layer to control how budgets are allocated, how performance is measured, and how quickly strategies can adapt.

For agencies, this signals an evolution rather than a disappearance. As media planning functions move in-house, external partners may increasingly focus on strategy, creative, and specialized expertise. For MULO brands the implications are more operational. Success will depend on integrating AI into workflows that enable continuous reallocation of spend based on real-time performance and marginal return.

In that sense, the competitive advantage is shifting.  Not toward those who spend more, but toward those who can most effectively decide where and why to spend next.