The news: AI adoption is surging—75% of businesses now use generative AI for at least one function, up from 55% in 2023, per Qualtrics—yet decision-making remains slow. 

Companies have deployed sophisticated tools, but some executives still struggle to translate insights into trusted, actionable business moves. The disconnect is less about the technology itself than the processes surrounding it.

Brands that level up are the ones that move beyond pilots. Enthusiasm is high—the majority of organizations now have AI agents in production—but the path from pilot to scaled impact remains fraught.

“The key is consistent data, governed access, and a single platform that scales with the organization rather than against it,” Lee James, director of partnerships and customer adoption at data analytics platform Domo, told EMARKETER.

Zooming in: James said pilots benefit from controlled conditions. “Pilots succeed because someone gives a small motivated team clean data, clear access, and a defined problem to solve,” he said. “Scaling breaks that illusion fast. Suddenly you are dealing with fragmented data across business units, inconsistent metric definitions, and access permissions that nobody documented.”

• Dashboards and models generate recommendations, but approval chains, weekly review cycles, and fragmented data governance prevent decisive action. 
• Success in isolated conditions can stall when exposed to inconsistent metrics, lack of employee training, and ungoverned data.

Implications for brands: Closing the gap requires redesigning how organizations act on insights, not just how they generate them. This requires a shift from passive dashboards to decision-ready AI assistants that show their work, surface recommendations with transparency, and invite executive judgment. 

Brands that can run AI pilots while managing tight decision cycles have higher chances of success.

• Redesign decision workflows, not just dashboards: Replace fragmented approval chains and weekly review cycles with streamlined processes that embed AI insights directly into executive decision-making.
• Unify data governance before scaling: Establish consistent metrics, governed access, and enterprise-wide data standards before moving pilots into production—ensuring that what works in isolation can scale without breaking.

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