Despite proven cost efficiencies from deploying fine-tuned AI models, companies report the primary barrier to adoption is human, not technical. The core challenge is overcoming employee inertia and successfully integrating new tools into existing workflows—a classic change management problem.

Companies struggle with AI not because of the models, but because their data is siloed. Adopting an 'integration-first' mindset is crucial for creating the unified data foundation AI requires.

The biggest resistance to adopting AI coding tools in large companies isn't security or technical limitations, but the challenge of teaching teams new workflows. Success requires not just providing the tool, but actively training people to change their daily habits to leverage it effectively.

While enterprises slowly adopt AI for workflow automation within existing structures, the frontier has moved to a new paradigm of on-demand capability creation via code generation. This isn't a difference in speed but in direction. The gap is no longer linear but compounding, as the two models of operation are fundamentally decoupling.

Vendors selling "one-click" AI agents that promise immediate gains are likely just marketing. Due to messy enterprise data and legacy infrastructure, any meaningful AI deployment that provides significant ROI will take at least four to six months of work to build a flywheel that learns and improves over time.

While AI models improved 40-60% and consumer use is high, only 5% of enterprise GenAI deployments are working. The bottleneck isn't the model's capability but the surrounding challenges of data infrastructure, workflow integration, and establishing trust and validation, a process that could take a decade.

The slow adoption of AI isn't due to a natural 'diffusion lag' but is evidence that models still lack core competencies for broad economic value. If AI were as capable as skilled humans, it would integrate into businesses almost instantly.

The excitement around AI capabilities often masks the real hurdle to enterprise adoption: infrastructure. Success is not determined by the model's sophistication, but by first solving foundational problems of security, cost control, and data integration. This requires a shift from an application-centric to an infrastructure-first mindset.

Just as electricity's impact was muted until factory floors were redesigned, AI's productivity gains will be modest if we only use it to replace old tools (e.g., as a better Google). Significant economic impact will only occur when companies fundamentally restructure their operations and workflows to leverage AI's unique capabilities.