Trust, not model capability, is setting the pace of enterprise AI
The expected disruption has not reached core enterprise processes because adoption is being gated by trust, economics, and organizational readiness.
Growth Protocol founder Miro Dimitrov joined senior business leaders at the Yale CEO Caucus to discuss the widening distance between AI capability and measurable enterprise impact.
Companies are investing heavily in adoption, yet broad P&L impact remains limited. The issue is not simply access to stronger models. AI has struggled to reach core processes where decisions carry material financial, regulatory, and operational consequences.
Technical availability is not economic transformation
McKinsey Senior Partner Asutosh Padhi reinforced the broader point: general-purpose technologies require organizations to redesign workflows before technical progress becomes economic value. McKinsey research cited in Fortune found that only 6% of companies reported significant impact and modest earnings attribution from AI.
Cost is part of the constraint. So is trust. Organizations need systems that can apply business rules, show their reasoning, protect proprietary information, and operate inside the infrastructure enterprises already control.
A different architecture for complex decisions
At the Caucus, Growth Protocol presented how hybrid reasoning combines neural pattern recognition with explicit rules, constraints, and domain knowledge. The architecture is designed to provide explainability and transparency while allowing deployment within client-controlled infrastructure without training on client data.
Fortune subsequently highlighted Growth Protocol's roughly 80-fold reduction in inference costs in live client deployments, achieved by shifting complex workloads away from ultra-expensive GPUs and onto standard enterprise CPUs.
The enterprise race will not be decided by access to the largest model. It will be decided by which systems earn enough trust to reach consequential workflows.