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AI is scaling fastest in production, with commercial applications still catching up
The first phase of AI in marketing has been dominated by work that is visible, repeatable and relatively easy to augment: content, creative production and productivity. The harder shift happens when AI moves from producing marketing outputs to influencing the reasoning behind commercial decisions.
Advanced organisations are already further ahead in these areas, but scaling remains much lower in media allocation and attribution than in content and creative work. The current value of AI is therefore clearest in improving how marketing gets done, while its role in improving the outcomes marketing is responsible for is still developing.
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Figure 1. 41% of Advanced organisations have scaled AI in customer insights,
compared with 33% in media allocation and 30% in measurement and attribution.
Cost reduction remains the strongest expected business impact at 69%, while revenue acceleration stands at 38%. This suggests that AI is currently creating clearer value through the economics of existing work than through new sources of growth. The next stage of AI maturity is less about producing more marketing output and more about turning AI into a stronger contributor to the outcomes the business is trying to achieve.
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Figure 2. Cost reduction is the leading expected business impact at 69%,
while revenue acceleration remains at 38%.
The capability gap is becoming an adaptability gap
The report also reveals a difference in how organisations turn exposure to AI into capability. More mature organisations are investing more heavily in active forms of learning, particularly training and peer-based learning. Earlier-stage organisations rely relatively more on industry reports, which help teams understand where the market is moving but do less to change how people work.
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Figure 3. 68% of Advanced organisations use training courses
to stay updated on AI, compared with 44% of Early organisations.
The difference matters, but training is only one part of the capability organisations need. It builds knowledge and helps people develop new skills, but AI keeps changing after the training ends. The more important capability is adaptability: the ability to keep learning and adjust how work gets done as new tools, use cases and expectations emerge. For mature AI organisations, the challenge is therefore not to keep retraining people every time AI moves forward, but to build an organisation that can keep adapting as it does.
Agentic AI makes this particularly visible. As these systems move beyond generating individual outputs to carrying out sequences of tasks with greater autonomy, they change the role of the people working with them. Judgement becomes less about knowing how to use a tool and more about knowing when to intervene, how to assess what it produces and how to respond as its capabilities change. Adaptability becomes a practical capability, embedded in how people work rather than something that can be taught once and applied indefinitely. As AI continues to evolve, this becomes increasingly important to how organisations build capability for the year ahead.
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Figure 4. Even among Advanced organisations, more than half
remain at the awareness or partial-deployment stage.
Scaling AI creates a new strategic choice: absorption
As AI becomes more widely adopted, its next impact is on the organisation around it. Absorption describes how far an organisation incorporates AI into the way it is structured and run. At scale, this becomes a strategic choice: AI can be added across existing functions and workflows, or it can be concentrated in priority areas where the organisation is prepared to change how those areas operate. That choice is likely to become more consequential as AI investment and automation expand into 2027.
Wider use of AI tools and automation is expected to shape the industry far more than greater demand for ROI measurement. Among Advanced adopters, marketing budgets are also more likely to increase than decrease, creating room for further investment. As AI investment scales, the question shifts from adding more applications to deciding what the organisation is prepared to change around them.
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Figure 5. Measurable ROI ranks among the lowest priorities
for organisations planning their AI development in 2026.
This can take different forms. Horizontal absorption spreads AI across existing functions, workflows and teams. Vertical absorption allows AI to reshape the structures within selected areas, including roles, processes and governance. Neither is inherently more advanced. They represent different strategic choices about how much organisational change AI should create.
A new benchmark for AI maturity
This points to a broader definition of AI maturity. Adoption marked the point at which organisations began using AI. Integration showed how deeply it became embedded across marketing. The next benchmark is about what happens as AI becomes part of the organisation: the value it creates, how people adapt to it and how the organisation itself changes around it.
AI maturity is therefore becoming less about how much technology an organisation has introduced and more about what it is able to do with that technology: create meaningful commercial value, adapt as it evolves and make deliberate choices about how AI becomes part of the business.
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