Why you still need your agency

As artificial intelligence fundamentally alters marketing economics, the traditional relationship between brands and media agencies is undergoing a strategic recalibration. 91% of marketing organisations now report using AI. As the AI stack absorbs work that used to sit with the agency, marketing leaders are reconsidering what they buy and from whom.

Almost every major study points to the same three pressures sitting underneath that shift: proving marketing ROI; getting data and AI to work across channels; and scaling AI without losing brand, quality, or compliance. Those pressures do not just reshape internal decisions. They change why and how brands work with agencies.

In the last twelve months, every major agency holding company has either changed its CEO, restructured its legal entity, or announced a multi-hundred-million labour programme. Omnicom and IPG closed the largest agency merger ever. WPP swapped Mark Read for Microsoft’s Cindy Rose, and her Elevate28 strategy puts the holding-company label up for retirement. Publicis signed a $2.2bn agreement for LiveRamp. Dentsu replaced its CEO and announced cutting 8% of international staff. Havas committed close to €1bn to AI.

Meanwhile the AI stack itself is absorbing pieces of the agency role — Salesforce’s Agentforce Marketing, Adobe’s Brand Intelligence in GenStudio, Coca-Cola’s Fuel Light 360 with Bain. To remain viable partners, agencies must adapt to expectations around ROI, transparency, and governance. I can see four shifts already taking shape in the conversations I am having with marketing leaders.

Shift 1. From campaign execution to AI orchestration

The first shift is about what the agency actually does for its retainer. CMOs are making governance central to how they scope work and measure partner performance. The biggest barrier to scaling AI in 2026 is legal, compliance, and brand review — named by 27% of marketers as the top reason they are not scaling AI faster. Brands now expect partners to document verification protocols, brand-safety rules, and escalation paths for autonomous decisions: which prompts, datasets, and channels are off-limits, and who reviews an agent’s output before it goes live. The real opportunity for agencies is to design “content factories” with governance built into the AI pipeline.

Shift 2. From retainers for labour to retainers for efficiency gains

The commercial model is being rewritten alongside the work. Among the small group of European marketing leaders who have reached gen-AI maturity (around 6%), efficiency gains come in above 20%. Meanwhile 55% of marketers worry about budget cuts, and 46% say personnel and headcount are on the cut list, versus just 15% for MarTech and tools. When something has to give, agency labour fees are first on the list. New contracts are being written with pass-through clauses, where efficiency gains from AI flow back to the client as lower fees or higher iteration counts. The old retainer paid for “hours spent”. The new one pays for the gain those hours produce.

Shift 3. From volume KPIs to value KPIs

Measurement is moving away from execution metrics toward business outcomes. Among marketers who can measure AI ROI, 60% report at least 2x returns. Yet only 41% say they can measure the ROI of their AI investments today. New contracts increasingly require agencies to demonstrate AI proficiency as a condition of partnership rather than a pitch-deck talking point.

Shift 4. From done-for-you to done-with-you

The work agencies bring is moving from finished asset delivery toward capability transfer. 97% of marketers say access to AI tools factors into their job decisions, and 75% say it is critical when considering a role. Talent goes where it can keep building. Agencies that want to stay in the room should run playbooks, training, and joint workflows with internal teams.

A four-step playbook to redesign how you work with agencies

• Step 1. Decide what should never be outsourced to AI and agencies. The work that truly differentiates brands stays human. Draw a hard line around three things: brand point of view and taste (61% of marketers say both matter more than ever when humans and AI work together); the value creation agenda — where to grow, which customers to serve, what the brand stands for; and the AI risk appetite itself.
• Step 2. Match agency types to AI-era capabilities. Most leaders still hire against legacy boxes: creative, media, performance, social. The hardest 2026 problems cluster into four other pillars — systemic performance and ROI proof, data and AI readiness, AI-driven content scale, and governance and risk. Make each pillar’s owner explicit in scopes and governance documents.
• Step 3. Give your agency permission to own the AI operating model within their scope. Speed is the gap most often missed: 29% of organisations still take 3 to 5 months to launch a multi-asset campaign, and only 12% can do it in days or hours. Ask your agency to own three concrete things: how AI shows up in your workflows, in your metrics, and in your governance.
• Step 4. Change what you ask your agencies to prove. Most organisations still measure AI in hours saved and vendor cost reductions. Instead, ask: how AI-assisted campaigns changed time-to-market and time-to-learning versus baseline; where AI helped you stop spending; and how you used AI to inform decisions, not just to produce assets.

The next two years

The holding companies that solve the operating-model question will hold the strategic seat. The rest will be reclassified as media resellers. The same question lives on the CMO side: a brand that keeps briefing for production volume in 2027 will be paying for an agency model that no longer makes economic sense.

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