Don't treat AI as an omniscient expert. Instead, view it as an intern: provide clear, detailed instructions, show examples of the desired output, and always review the results critically. You wouldn't let an intern's work go straight to the board, and you shouldn't with AI either.

AI agents, like human employees, require clear roles, ongoing coaching, and defined success metrics. Neglecting this leads to 'zombie agents' or performance 'drift,' where the AI's output becomes misaligned and useless over time.

Treat AI agents like new hires. Start with simple, supervised tasks, provide corrective feedback, and codify successful workflows into reusable skills. This gradual process builds the trust necessary to grant full autonomy for complex, long-running tasks.

Frame your relationship with AI agents as an employer-employee dynamic. This involves proper onboarding, creating documentation for processes, and defining clear roles and communication protocols to ensure they operate effectively and align with your goals.

Since every AI agent needs human oversight, companies are creating a new specialization. These engineers don't just write code; they manage the company's central "super-agent," ensuring it works correctly, fixing its mistakes, and integrating it into workflows, often by "talking" to it in Slack.

To successfully implement AI, approach it like onboarding a new team member, not just plugging in software. It requires initial setup, training on your specific processes, and ongoing feedback to improve its performance. This 'labor mindset' demystifies the technology and sets realistic expectations for achieving high efficacy.

General-purpose AI assistants produce inconsistent output. Instead, define AI agents with specific roles, boundaries, and quality gates, much like onboarding a new engineer with a clear job description. This disciplined approach leverages how LLMs are trained, leading to more reliable and predictable results within the SDLC.

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.

Pega's CTO warns leaders not to confuse managing AI with managing people. AI is software that is configured, coded, and tested. People require inspiration, development, and leadership. Treating AI like a human team member is a fundamental error that leads to poor management of both technology and people.