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 that experiment endlessly with AI but fail to operationalize it face the biggest risk of falling behind. The danger lies not in ignoring AI, but in lacking the change management and workflow redesign needed to move from small-scale tests to full integration.

Providing AI licenses isn't enough. Companies must actively manage the transition of employees from basic users (asking simple questions) to advanced users who treat AI as a collaborator for complex, high-value tasks, which is where real ROI is found.

For AI tools that fundamentally alter workflows, a simple software deployment is insufficient. Success requires a dedicated team of 'forward deployed' experts (e.g., ex-lawyers for legal tech) to manage the enormous change management undertaking, ensuring adoption and proficiency across the client organization.

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.

The biggest mistake in corporate AI investment is buying platform licenses for everyone without first investing in the necessary training and change management. This over-investment in tech and under-investment in people leads to wasted resources, as employees lack the skills or motivation to adopt the tools.

Many companies struggle with AI not just because of data challenges, but because they lack the internal expertise, governance, and organizational 'muscle' to use it effectively. Building this human-centric readiness is a critical and often overlooked hurdle for successful AI implementation.

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.

Many AI projects become expensive experiments because companies treat AI as a trendy add-on to existing systems rather than fundamentally re-evaluating the underlying business processes and organizational readiness. This leads to issues like hallucinations and incomplete tasks, turning potential assets into costly failures.