Creative intelligence (CI) is moving from an emerging advertising capability toward broader operational adoption, but a new Winterberry Group study indicates that many brands still struggle to connect creative insights with actual campaign decisions.
The report, “Defining Creative Intelligence: Marketing Maturity,” surveyed approximately 200 brand marketers in the U.S. and U.K., supplemented by interviews with marketers, agencies and technology providers. Winterberry Group categorizes respondents across five maturity stages: 9% lagging, 21% emerging, 34% progressing, 26% established and 11% leading.
The findings suggest that adoption of CI technology is advancing faster than the organizational processes needed to operationalize it.
From Creative Measurement to Automated Decisions
Winterberry Group defines creative intelligence as the ability to collect, structure and analyze creative decisions against performance data so assets can be continuously optimized for effectiveness and engagement.
The report identifies several characteristics among organizations at the more advanced stages, including documented testing programs, dedicated experimentation budgets, measurement of creative lift, and workflows shared across creative, media and strategy teams.
One of the central findings is that measurement itself is no longer necessarily the limiting factor. Instead, organizations can struggle to create a return path from measurement outputs into changed creative assets, briefs or campaign decisions.
That distinction is important for AdTech because it moves CI beyond reporting. The emerging model connects creative data with media performance, audience information and optimization systems.
Impact on Advertisers, Agencies and AdTech Platforms
For advertisers, operationalized CI could change how creative is tested and refreshed across campaigns. Rather than evaluating creative primarily after delivery, teams can incorporate performance signals into pre-flight decisions and in-flight optimization.
Agencies face a related workflow challenge. Creative, media and audience teams often operate through different systems and processes, making it difficult to transfer insights between functions. Winterberry Group identifies cross-functional workflow as one of the characteristics associated with more mature CI operations.
For AdTech vendors, the development creates demand for infrastructure capable of connecting creative metadata, audience signals, campaign performance and optimization actions. The technology challenge increasingly becomes interoperability rather than simply generating more creative variations.
AI’s Role in Creative Intelligence
AI is becoming an important layer in this transition. Winterberry Group’s findings arrive as advertising platforms increasingly incorporate AI into planning, activation, measurement and optimization.
The IAB’s 2026 Outlook Study found that 78% of surveyed buyers expected to increase focus on generative AI in media campaigns, while 66% planned greater focus on agentic AI for ad buying and campaign execution. Cross-platform measurement was another major priority at 72%.
These developments provide context for CI’s evolution: AI can potentially connect signals and automate parts of the optimization cycle, but data quality, governance and organizational workflows remain important prerequisites.
Market Landscape
The advertising industry is simultaneously working to modernize measurement and standardize data flows. IAB’s Project Eidos initiative is focused on improving advanced measurement through shared principles, standards and frameworks, reflecting broader concerns around fragmented advertising data.
Winterberry Group’s research suggests a similar challenge exists within creative operations: organizations may possess measurement capabilities without having an effective mechanism for converting those outputs into creative changes.
That makes CI increasingly relevant to the broader programmatic ecosystem, where campaign optimization depends on coordinating media, audience and creative signals.
Strategic Outlook
The next phase of creative intelligence is likely to focus less on simply identifying which creative performs and more on connecting that insight to an executable workflow.
For enterprise advertisers, this means CI maturity may increasingly depend on shared data structures, testing governance, integrated creative and media processes, and systems capable of turning performance signals into actions. AI and technologies such as model context protocol (MCP) could accelerate that connection, although their practical impact will depend on implementation and governance.
Winterberry Group reports that 91% of surveyed brands expect CI investment and operationalization to increase, indicating continued expansion of the category.
Top Insights
- 91% of surveyed brands expect CI investment and operationalization to increase.
- Winterberry Group classifies 34% of respondents as progressing and 11% as leading.
- Organizational workflows remain a major barrier to applying creative intelligence.
- Advanced CI programs connect creative, media and strategy teams through shared processes.
- AI is increasingly being positioned as an execution layer for advertising optimization.
Get in touch with our Adtech experts
