For CIOs, CMOs and agency leaders, the core issue is not whether AI improves throughput; it is who decides when speed is worth the loss of distinctiveness. If AI is governed only as a productivity tool, teams will optimise for output volume, lower unit cost and shorter cycle times. That can be attractive in the short term, but it also shifts the operating model toward standardisation โ a poor fit when brand value depends on originality, judgment and differentiation.
This creates a management trade-off that should be explicit in portfolio and funding decisions. AI use cases should not all be measured against the same efficiency scorecard. Leaders need separate criteria for automation, augmentation and creative work, because the benefits differ: cost reduction, capacity release, quality improvement or growth impact. The practical question is whether AI investment is being approved to remove work, or to improve the quality of decisions and ideas that drive revenue.
That means governance needs to move beyond policy and risk control into operating discipline. Useful next questions include:
- Which activities must remain human-led because originality is part of the business model?
- What metrics will show whether AI is improving brand outcomes, not just productivity?
- Who owns the decision to trade speed for experimentation, and how is that risk recorded?
- How will agencies or internal teams be rewarded for effectiveness if remuneration still favours efficiency?
The leadership test is simple: if AI is only making the organisation cheaper to run, it may also be making it harder to grow. Decision-makers should treat creativity, customer experience and brand strength as portfolio outcomes, not soft extras.
Like it or not, AI accountability and constraints loom large. While generative and agentic AI reshape the way marketing agencies deliver value โ by accelerating speed to market, producing at scale, and removing costs/fees โ the emphasis of efficiency over effectiveness threatens the originality fueling distinctiveness and commercial growth. Marketers and agencies compromise creativity when they focus on productivity and cost efficiency. Itโs a steep price to pay.
AIโs ubiquity within marketing agencies continues to grow. Nearly nine out of 10 marketing agencies use generative or agentic AI as part of marketing creation and delivery. From brainstorming and creative ideation to research and competitive intelligence to summarizing marketing performance, AI is the rule and no longer the exception. Agenciesโ challenges to scaling AI evolved from legal and privacy concerns, dominating in 2024 and 2025, to reliability and accuracy concerns, marking advancements in maturity and sophistication of production use cases.
Agencies And Marketers Sacrifice Creativity And Growth For Efficiency
AIโs impact on growth and creative outputs stalls, however, when it comes to brand and agency objectives. Most agencies prioritize โenhancing the productivity of internal staffโ for generative (81%) and agentic AI (63%). At the same time, agencies place less value on โimproving the quality of creative ideationโ using genAI (65%) and agentic AI (35%). Marketers appear equally shortsighted with their AI ambitions. Forrester research shows that the AI benefit corporate marketers request most frequently from their agency is cost efficiency (71%), while marketing performance and revenue growth lag significantly behind, at 49% and 27%, respectively. The accountability and demand for AI returns coming from the C-suite and financial markets is poised to dismantle decades and billions in marketing investments, eroding companiesโ most distinctive asset: their brands.
AI marketing capabilities in brand-agency remuneration remain absent. For the third consecutive year, AI remains a cost of business for the majority of agencies, with generative and agentic AI funded directly by the agency without passing the costs to their clients. Fewer than 10% of marketing agencies monetize genAI investments as a line of business. The silver lining? The advanced systems that agencies such as Code and Theory, DEPT, Havas, Horizon Media, Omnicom, PMG, and Publicis build with agentic AI show promise. AI agents are less likely to be an agency cost center and more likely to be monetized.
CMOs: Reset Your AI Expectations From Efficiency To Effectiveness
Now that artificial intelligence reprices advertising industry valuations, brands and agencies should reconsider the short-term benefit of productivity and cost reduction versus the long-term gain of effectiveness and creativity. Marketers recognize the need beyond efficiency โ a majority believe that corporate profits will come from organic business growth, as opposed to cost-cutting measures. Marketers need brand differentiation to realize that growth. But itโsย sorely missing. More than three-quarters of brands in Forresterโs Brand Experience Index had โOKโ or โpoorโ scores. Forresterโs Customer Experience Index reached its lowest average in 2025, with a mild uptick in 2026.
To make their companies competitive, CMOs must help lead an AI business and strategy transformation, with marketing playing an important role. Forresterโs research shows that CMOs are as likely as other C-suite executives to lead AI change. Agencies must guide strategy, innovation, and execution using AI to help their clients reach these ambitions. Otherwise, both risk forfeiting the transformation remit to another member of the C-suite and an automated, hyperscaler tech partner.
Forresterโs latest report, The State Of AI Inside US Marketing Agencies, 2026, details the growth of generative and agentic AI (agents) within agencies, including objectives, use cases, benefits, barriers, partnerships, and remuneration. The report reveals opportunities for how brands can leverage the emerging capabilities of marketing partners for their own growth.
Forrester clients interested in learning more about what the current state of play means for the future of the client-agency relationship can set up a guidance session with Jay Pattisall.
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