AI Is Reshaping Omnichannel Advertising — and the Agency Business Model

How AI Is Redrawing the Map of Omnichannel Advertising

The economics of advertising are being rewritten by artificial intelligence, not through a single flashy tool but through a million tiny, continuous adjustments that no human media buyer could ever keep up with. For years, creative, media buying, and attribution lived in separate platforms, forcing brands and agencies to stitch together fragmented workflows. Campaign optimization was reactive — based on delayed reporting and siloed channel views — even as consumers bounced across search, social, display, video, streaming and connected TV in a single journey.

AI is now stepping in to manage the thousands of micro-decisions that happen after a campaign goes live: adjusting bids for a 0.1% lift, reallocating budget across channels in real time, tailoring creative to a viewer’s location or the weather, and recognizing patterns that traditional attribution models miss. Individually, each tweak is negligible; compounded across a campaign, they add up to measurable performance gains that were previously impossible to achieve manually. The shift is especially powerful in omnichannel advertising, where a consumer might first see a brand on CTV, search for it days later, engage on Instagram, and convert through an entirely different channel. AI-powered systems can finally evaluate that ripple effect holistically, understanding how creative, placement, audience behavior, and timing work together, rather than optimizing each channel in isolation.

This isn’t just automation — it’s a fundamental rebalancing of where human judgment and machine execution sit. The traditional agency model, built on labor-intensive optimization and reporting, is being challenged. As AI handles more of the operational load, agencies have an opening to reposition themselves around strategy, creative intelligence, and business outcomes, rather than manual execution. But this transformation only works with transparency. Marketers under pressure to justify every ad dollar are increasingly demanding visibility into what the AI is doing and why performance is shifting. Black-box optimization is no longer enough.

From Siloed Teams to Connected Intelligence

The Agency Model Under Pressure — and a New Opportunity

The traditional agency’s value proposition has long been tied to manual, headcount-heavy services: trafficking ads, resizing creative, reconciling reports across platforms, and making pacing adjustments. AI is compressing the economics of that work. Instead of adding people to scale, agencies can adopt AI systems that reduce operational overhead while allowing teams to manage more accounts. Several are already exploring this path — not to cut headcount indiscriminately, but to free talent for higher-value strategic and creative work. The opportunity lies in becoming a trusted advisor that interprets AI outputs, sets the overarching strategy, and ensures brand safety and message consistency across channels, rather than executing routine optimizations.

The Transparency Imperative

As AI takes a larger role, the call for explainability is getting louder. Advertisers want to know not just that a campaign performed well, but why it did — which signals the AI acted on, how budget moved between channels, and where the true incrementality came from. Without that visibility, trust erodes and marketing dollars face internal skepticism. The next wave of advertising technology will have to combine continuous automation with real-time, measurable attribution that marketers can audit. Those who provide black-box solutions risk regulatory attention and client pushback as the industry matures.

What the Liberty Biberty Example Tells Us About Brand Positioning

The recent introduction of Liberty Mutual’s ‘Liberty Biberty’ character — a concept that had actually been incubating since 2019 — underscores a parallel truth: in a fragmented addressability landscape, brands must be very clear about why consumers should choose them. The advertising technology revolution is happening alongside a strategic reckoning. Brands either need to be undeniably best on price, or they must make their premium worth it through either performance or experience. AI can help deliver that differentiation by making personalization and real-time relevance far more efficient, but it can’t answer the core brand question. The past several years saw a wave of innovation aimed at preserving addressability as signals became harder to access; now, the challenge moves from finding the consumer to being compelling enough to win them.

Next Moves for Brands and Agencies

• For agencies: Audit your current workflow for tasks that are openly automatable — bid management, creative resizing, multi-platform reporting — and pilot AI-driven alternatives that provide transparent, auditable decision logs. Use the efficiency gain to shift headcount into strategic planning, creative intelligence, and client consulting, rather than simply cutting costs.
• For brands: Demand attribution that shows cross-channel influence, not just last-click metrics. Ask your agency or in-house team to explain how AI is allocating budget in-flight, and require real-time dashboards that connect creative changes to performance outcomes.
• For both: Revisit your brand positioning through the price-versus-premium lens. If you’re competing on price, ensure your advertising technology can squeeze every fraction of a percent in efficiency. If you’re competing on experience or performance, use AI’s dynamic creative and targeting capabilities to prove that premium value at the moment of consideration — no amount of micro-optimization can save a weak value proposition.
• Vendor selection: When evaluating mar-tech partners, prioritize platforms that combine automation with clear, real-time reporting on where budget is having an impact. Avoid black-box solutions that can’t justify decisions, especially as financial scrutiny on ad spend intensifies.