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.
To win over skeptical team members, high-level mandates are ineffective. Instead, demonstrate AI's value by building a tool that solves a personal, tedious part of their job, such as automating a weekly report they despise. This tangible, personal benefit is the fastest path to adoption.
Formal AI competency frameworks are still emerging. In their place, innovative companies are assessing employee AI skills with concrete, activity-based targets like "build three custom GPTs for your role" or completing specific certifications, directly linking these achievements to performance reviews.
Despite marketing hype, current AI agents are not fully autonomous and cannot replace an entire human job. They excel at executing a sequence of defined tasks to achieve a specific goal, like research, but lack the complex reasoning for broader job functions. True job replacement is likely still years away.
When leadership demands ROI proof before an AI pilot has run, create a simple but compelling business case. Benchmark the exact time and money spent on a current workflow, then present a projected model of the savings after integrating specific AI tools. This tangible forecast makes it easier to secure approval.
When creating AI governance, differentiate based on risk. High-risk actions, like uploading sensitive company data into a public model, require rigid, enforceable "policies." Lower-risk, judgment-based areas, like when to disclose AI use in an email, are better suited for flexible "guidelines" that allow for autonomy.
As users increasingly get answers from AI assistants, marketing strategy must evolve from Search Engine Optimization (SEO) to Generative Engine Optimization (GEO). This means creating diverse, authoritative content across multiple platforms (podcasts, PR, articles) with the goal of being cited as a trusted source by AI models themselves.
Many leaders mistakenly halt AI adoption while waiting for perfect data governance. This is a strategic error. Organizations should immediately identify and implement the hundreds of high-value generative AI use cases that require no access to proprietary data, creating immediate wins while larger data initiatives continue.