GSB professors warn that professionals who merely use AI as a black box—passing queries and returning outputs—risk minimizing their own role. To remain valuable, leaders must understand the underlying models and assumptions to properly evaluate AI-generated solutions and maintain control of the decision-making process.
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AI is a 'hands-on revolution,' not a technological shift like the cloud that can be delegated to an IT department. To lead effectively, executives (including non-technical ones) must personally use AI tools. This direct experience is essential for understanding AI's potential and guiding teams through transformation.
Simply instructing engineers to "build AI" is ineffective. Leaders must develop hands-on proficiency with no-code tools to understand AI's capabilities and limitations. This direct experience provides the necessary context to guide technical teams, make bolder decisions, and avoid being misled.
With AI agents automating raw code generation, an engineer's role is evolving beyond pure implementation. To stay valuable, engineers must now cultivate a deep understanding of business context and product taste to know *what* to build and *why*, not just *how*.
Treat advanced AI systems not as software with binary outcomes, but as a new employee with a unique persona. They can offer diverse, non-obvious insights and a different "chain of thought," sometimes finding issues even human experts miss and providing complementary perspectives.
To effectively leverage AI, treat it as a new team member. Take its suggestions seriously and give it the best opportunity to contribute. However, just like with a human colleague, you must apply a critical filter, question its output, and ultimately remain accountable for the final result.
Don't view AI tools as just software; treat them like junior team members. Apply management principles: 'hire' the right model for the job (People), define how it should work through structured prompts (Process), and give it a clear, narrow goal (Purpose). This mental model maximizes their effectiveness.
The main barrier to AI's impact is not its technical flaws but the fact that most organizations don't understand what it can actually do. Advanced features like 'deep research' and reasoning models remain unused by over 95% of professionals, leaving immense potential and competitive advantage untapped.
The skills of setting clear goals, understanding resource (model) strengths, and defining processes are the same for managing people and AI agents. Being a great manager makes you a great AI user, as both require clarifying outcomes and marshalling resources to achieve them.
Go beyond using AI for simple efficiency gains. Engage with advanced reasoning models as if they were expert business consultants. Ask them deep, strategic questions to fundamentally innovate and reimagine your business, not just incrementally optimize current operations.
The most significant recent AI advance is models' ability to use chain-of-thought reasoning, not just retrieve data. However, most business users are unaware of this 'deep research' capability and continue using AI as a simple search tool, missing its transformative potential for complex problem-solving.
