Taboola released research showing that 86% of performance advertisers would allocate up to a quarter of their advertising budget to the open web if agentic AI solutions matched the capabilities currently available only on search and social platforms, according to a study published May 13.

The report, titled “The Agentic Advantage in Performance Marketing: Securing Incremental Growth Beyond Search and Social,” surveyed 200 senior performance marketers at organizations with monthly advertising budgets between $500,000 and $4.9 million. The findings reveal that while three-quarters of advertisers see meaningful performance uplift from AI-powered solutions, those gains remain confined to walled-garden platforms. Eighty percent of respondents stated they would immediately increase ad spend on the open web if comparable autonomous optimization systems existed.

Marketing executive reviewing agentic AI performance dashboard showing real-time budget allocation

Enterprise Advertisers Face Integration Barriers

The study identified workflow integration as the dominant obstacle to agentic AI adoption, with the challenge falling disproportionately on large advertisers. Organizations spending between $1 million and $4.9 million per month reported integration difficulties at significantly higher rates—74% cited it as the primary barrier—compared to just 9% of advertisers in the $300,000 to $499,000 monthly spend range.

The integration challenge stems from agentic AI systems requiring continuous data feeds, real-time decision-making authority, and coordination across creative production, targeting parameters, and bidding strategies. Marketing leaders overseeing media buying services across multiple channels face the task of reconciling autonomous AI adjustments with existing approval workflows, brand guidelines, and attribution models.

“Advertisers of all sizes are leaning into agentic advertising, and the results are following,” Adam Singolda, CEO of Taboola, said in the release. The research indicates demand for autonomous systems that allocate spend in real time and turn impressions into measurable outcomes, capabilities that enterprise brands currently access primarily through Google Ads management and social platform tools.

Budget Reallocation Signal for Open Web Inventory

The 86% of advertisers willing to shift up to 25% of performance budgets represents a material signal for how marketing leaders evaluate channel mix when AI-driven optimization becomes available beyond walled gardens. The research suggests that platform choice increasingly hinges on automation capability rather than reach or inventory scale alone.

Taboola announced Realize+ in April 2026, an agentic solution using a Decision Engine that reallocates spend in real time and an Element Generator that automates creative and targeting. The company reported rolling out the beta to advertisers seeking to drive outcomes at scale. The platform reaches approximately 600 million daily active users across publisher properties including NBC News, Yahoo, and OEM partnerships with Samsung and Xiaomi.

The study methodology focused exclusively on organizations with substantial monthly budgets, indicating that the integration challenges and budget reallocation appetite reflect enterprise-scale operations rather than small-business concerns. Marketing directors at APAC enterprises with Philippine operations face similar workflow integration questions when evaluating whether to add agentic AI tools to their existing paid media programs or wait for platform maturity.

APAC. Implications

The 80% of advertisers stating they would immediately increase open web spend points to budget availability rather than budget expansion—marketing leaders appear ready to reallocate from existing walled-garden channels once AI-driven performance tools reach feature parity. For CMOs at Philippine enterprises and APAC companies with local operations, this signals a shift in how agency briefs should frame programmatic and display buying: not as reach supplements to search and social, but as performance channels capable of autonomous optimization.

The disproportionate integration struggle among large advertisers—those spending $1 million to $4.9 million monthly—maps directly to the operational reality at enterprises where marketing technology stacks involve multiple approval layers, regional coordination, and legacy attribution systems. Marketing directors evaluating agentic AI proposals from agency partners need to allocate implementation time for connecting data sources, establishing decision-making guardrails, and training internal teams on how to oversee rather than manually adjust campaigns.

The research also clarifies that “agentic AI” in advertising refers specifically to systems that make and execute optimization decisions without human intervention—reallocating budget across placements, generating creative variations, and adjusting targeting parameters in real time. When briefing agencies on AI capabilities, this distinction matters: tools that surface recommendations still require human action, while agentic systems operate autonomously within predefined parameters. Marketing leaders should confirm which model an agency’s AI toolset follows before setting performance expectations.