Employees achieving massive AI-driven productivity gains ('secret cyborgs') often hide their methods, fearing punishment or layoffs. To scale these innovations, leadership must create explicit incentive structures that reward them for sharing their methods, turning individual hacks into organizational advantages.
Effective AI adoption requires a three-part structure. 'Leadership' sets the vision and incentives. The 'Crowd' (all employees) experiments with AI tools in their own workflows. The 'Lab' (a dedicated internal team, not just IT) refines and scales the best ideas that emerge from the crowd.
Mollick warns against the common first AI project: a Retrieval-Augmented Generation (RAG) chatbot for internal documents. These custom projects are expensive, and their functionality is often quickly surpassed by cheaper, more powerful off-the-shelf models, resulting in a poor return on investment.
When buying AI solutions, demand transparency from vendors about the specific models and prompts they use. Mollick argues that 'we use a prompt' is not a defensible 'secret sauce' and that this transparency is crucial for auditing results and ensuring you aren't paying for outdated or flawed technology.
Ethan Mollick argues that the 20-40% competitive advantage historic American firms held came from management experimentation. He posits that today's leaders must similarly experiment with AI-driven processes internally, rather than outsourcing strategy to consultants or vendors, to win.