The primary barrier to enterprise AI adoption isn't the technology, but the workforce's inability to use it. The tech has far outpaced user capability. Leaders should spend 90% of their AI budget on educating employees on core skills, like prompting, to unlock its full potential.

The fastest way for smaller tech companies to leverage AI is not by building complex proprietary models, but by training employees to master existing consumer-grade tools like Claude and ChatGPT. This treats AI adoption as a skill to be developed through practice and experimentation, yielding immediate productivity gains.

Companies mistakenly treat AI training as a project with a completion date. In reality, AI capability is a depreciating asset with a measurable rate of decay. Budgets must shift from funding one-off "ignition" events to funding continuous maintenance to prevent inevitable skill loss and wasted investment.

Despite people being the single largest barrier to converting AI adoption into value, organizations are drastically underinvesting in them. A Deloitte study found 93% of AI spend goes to infrastructure, with a mere 7% for people-related initiatives like training, creating a significant adoption bottleneck.

Most companies buy AI tools but fail to see returns. Priyanka Vergadia's 10-20-70 rule advises spending 10% on tools, 20% on execution, and 70% on training. This builds the "habit" of AI, which is essential for achieving long-term productivity and ROI.

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.

Companies should reframe AI spending not as a traditional IT cost but as a direct investment in amplifying human capital. This model views AI agents as 'digital workers' that provide leverage to every employee, justifying spend based on the ROI of the augmented workforce.

For large, traditional companies, the most critical first step in AI adoption isn't building tools, but fostering deep understanding. Provide teams sandboxed access to AI models and company data, allowing them to build intuition about capabilities before crafting strategy.

Successful AI transformation doesn't require everyone to be a data scientist. Instead, organizations should aim for a "30% rule"—a minimum baseline understanding of AI concepts for the entire workforce, similar to mastering a portion of a new language for business. This empowers broader contribution and demystifies the technology.