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The role of the Chief Marketing Officer (CMO) is at a crossroads as artificial intelligence reshapes marketing, according to Capgemini Research Institute’s latest CMO Playbook. While expectations from CMOs are at their peak, they are struggling with shrinking budgets, declining strategic influence, and limited control over AI and martech investments.
Despite rising optimism around generative and agentic AI, 55% of marketing leaders say AI initiatives are currently funded by IT, with minimal control from marketing teams.
The report finds that nearly 70% of CMOs face rising expectations even as their authority declines. Marketing budgets have tightened over the past two years to an average of just 5% of company revenue, and CMO participation in business-critical decision-making has fallen sharply from 70% to 55% in the same period.
AI adoption is increasing—used for content creation, segmentation, and digital campaigns—but only 15% of marketers say low-value tasks are automated. The report notes that most teams still spend time on manual work, leaving little capacity for brand-building, customer connection, and innovation.
Additionally, martech and data investments are failing to unlock real-time personalization, with only 18% of marketers strongly agreeing that they are effectively personalizing customer interactions.
“CMOs today are expected to drive growth and meet sales targets, whilst also being experts in data and AI – they must now market to both humans and agents. But many lack the resources, control or clarity to manage these growing demands,” said Gagandeep Gadri, Managing Director, frog (Capgemini). “This is a pivotal moment to reposition marketing not just as a support function, but as a driver of customer experience and enterprise growth.”
Nearly 70% of large organizations now use Gen AI in marketing, and investment has surged—AI’s share of martech budgets rose from 64% in 2023 to 79% in 2025. Yet its impact remains limited, with only 7% of marketers strongly agreeing that AI has improved marketing effectiveness.
While optimism around autonomous, agentic AI is high, adoption remains slow due to talent gaps, data privacy issues, security risks, ethical concerns, and low trust in AI-powered autonomous decisions.