Brands Generate AI Content Faster Than They Can Govern It

The governance gap

Artificial intelligence has removed the traditional bottlenecks in content creation, enabling marketing teams to produce personalized campaigns at unprecedented speed and scale. But this capability has created an unexpected problem: brands now generate content faster than they can track, approve, or ensure it aligns with brand standards.

According to Bynder's 2026 State of DAM research, three-quarters of enterprise content is now AI-touched, with near-universal adoption expected within 12 months. Meanwhile, 93% of businesses face content challenges that traditional rule-based automation cannot solve, including detecting unauthorized or off-brand content at scale.

Why it matters

When content volume outpaces visibility, operational risks multiply. Teams recreate assets they cannot find, outdated files get distributed, and campaigns launch with inconsistencies that are difficult to trace. As AI becomes embedded across marketing functions, the competitive advantage will belong to organizations that can connect creation to control—not simply those that produce the most content.

Consumer adoption drives enterprise pressure

The pressure to scale content production reflects widespread AI adoption among end users. Prosper Insights & Analytics survey data shows 41% of U.S. adults already use generative AI, with adoption reaching 47% among Millennials and 44% among Gen-Z. Among those users, 17% use AI for content creation, 19% for creative writing, and 30% for writing assistance.

On the enterprise side, McKinsey's 2025 global AI survey found 88% of organizations regularly use AI in at least one business function, up from 78% the previous year. However, only about one-third have begun scaling AI programs, indicating that operational foundations lag behind adoption.

Traditional automation falls short

The core challenge is that AI-driven content introduces more variation than traditional automation can handle. A simple trigger-and-action workflow works when paths are predictable, but AI-generated assets require decisions based on market context, channel requirements, usage rights, accessibility standards, and specific brand rules.

Bob Hickey, CEO of Bynder, explained that brands risk scaling output without scaling context and control. "A system of record is foundational to these brands' AI strategies," he noted, emphasizing the need for trusted, approved content that provides speed, scale, and brand authenticity.

The solution: context-aware systems

Organizations need what industry observers call a "trusted system of record"—typically a Digital Asset Management (DAM) platform that provides a governed environment for approved content, metadata, permissions, and brand rules. This foundation allows both people and AI to access the same approved assets and business rules, enabling content to be adapted and distributed with greater confidence.

McKinsey research supports this approach, finding that high-performing organizations redesign workflows, establish human validation processes, and embed AI into business operations rather than treating it as a separate experiment.

The brands that benefit most from AI will be those that treat content governance as core business infrastructure rather than administrative housekeeping. Creating more content is now easy; knowing what to trust and making that trusted content work across the enterprise is where the competitive divide will emerge.

These findings were first reported by Gary Drenik in Forbes, drawing on research from Bynder, Prosper Insights & Analytics, and McKinsey.