Artificial intelligence has left the experiment phase — but most companies have not left the pilot. Between believing in the potential and operating at scale lies a gap, and that is where most AI projects stall.
What the numbers show
Almost everyone is betting: 99% of executives say AI agents will play a central role in their business within three years, and 95% of Brazilian companies plan to adopt agentic AI within two years (Deloitte). But execution lags the rhetoric: 57% still have no dedicated budget, and only 25% of companies worldwide already fully apply 40% or more of their experiments.
Why pilots stall
Pilots die from the lack of three things: recurring budget (a one-off experiment is not enough), governance (only 27% of Brazilian companies have mature models), and integration with real processes. Without these, AI stays a pretty proof of concept that never becomes an operation.
What separates those who scale
Those who scale treat AI as an operating program, not an isolated innovation project: they prioritize use cases by impact, measure before and after, embed governance from the start, and integrate data, processes and people. The reward is concrete — McKinsey points to 20%–30% productivity gains for those who scale across multiple areas.
Entercast's read
The 2026 bottleneck is no longer "which AI to use," but "how to leave the pilot." The companies that will capture value are not the ones that experiment the most — they are the ones that organize data, processes and governance to put AI into production and measure the result. Start small, but start thinking about scale.