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.

While technical challenges exist, an audience poll reveals that for 65% of organizations, "people problems"—such as fear, resistance to change, and lack of buy-in—are the primary obstacles hindering successful AI implementation.

The conventional wisdom that enterprises are blocked by a lack of clean, accessible data is wrong. The true bottleneck is people and change management. Scrappy teams can derive significant value from existing, imperfect internal and public data; the real challenge is organizational inertia and process redesign.

Implementing AI is becoming less of a technical challenge and more of a human one. The key difficulties are in managing change, helping people adapt to new workflows, and overcoming resistance, making skills like design thinking and lean startup crucial for success.

Despite the power of new AI agents, the primary barrier to adoption is human resistance to changing established workflows. People are comfortable with existing processes, even inefficient ones, making it incredibly difficult for even technologically superior systems to gain traction.

Companies fail to generate AI ROI not because the technology is inadequate, but because they neglect the human element. Resistance, fear, and lack of buy-in must be addressed through empathetic change management and education.

Leaders often misjudge their teams' enthusiasm for AI. The reality is that skepticism and resistance are more common than excitement. This requires framing AI adoption as a human-centric change management challenge, focusing on winning over doubters rather than simply deploying new technology.

Despite mature AI technology and strong executive desire for adoption, the primary bottleneck for enterprises is internal change management. The difficulty lies in getting organizations to fundamentally alter their established business processes and workflows, creating a disconnect between stated goals and actual implementation.

The most significant hurdle for businesses adopting revenue-driving AI is often internal resistance from senior leaders. Their fear, lack of understanding, or refusal to experiment can hold the entire organization back from crucial innovation.