Artificial intelligence is reshaping how we work. But the real challenge isn't technical. It's human. How do we use AI in a way that’s purposeful, responsible, and aligned with what people actually need?
That’s what this manifesto is about.
We don’t begin with technology. We begin with the job that needs to be done. This is the Jobs-to-be-Done to AI approach. It shifts the focus from what AI can do to what should get done and how AI can help.
These principles are for anyone committed to using AI purposefully: leaders, teams, consultants, implementers. It’s not a toolkit. It’s a mindset.
The Eight Principles
Start with the Outcome
Successful AI starts with the outcome, not the tech. The key question isn’t “What can this AI do?” but “What's the outcome?” Define the outcome first, then choose the right AI solution. This prevents tech for tech’s sake and keeps the focus where it belongs: on meaningful improvement.
Stay in Control
People remain in charge of every strategic AI decision. Final responsibility always lies with humans, regardless of AI accuracy. Humans set the standards and make the calls on what decisions require human judgment. Where is human oversight essential? What decisions must AI never make alone? Responsible AI means drawing those lines clearly.
Design Decisions, Don’t Just Make Them
Every AI decision touches three levels: functional, emotional, and social. Functional: what task will AI perform and what value will it add? Emotional: do people feel safe, supported, and capable working with AI? Social: how does it affect team dynamics and company culture? Acceptance and impact require that all three levels align.
Let Employees Lead the Change
Employees aren’t passive users of AI, they shape their own professional future. They recognize where AI can help and manage the transition: What can AI take on today? How will that change tomorrow? People remain the architects of their roles and their workplaces.
Rethink Collaboration
We shift from using AI as a tool to working with it as a teammate with complementary skills. As teams shift with AI, roles and tasks adapt. This requires coordination, communication, and shared ways of working. Responsibilities are redefined for both humans and AI. Skills are reassessed. Knowledge gaps are filled. Making the most of this collaboration and leading that change becomes a key skill.
Teach the Capabilities, Not the Tech
AI only delivers value when its capabilities are clearly understood. Forget the technical specs. What matters is what AI can actually do in context. What tasks can this AI handle? Where does it shine? Where are its limits? Only when employees grasp these answers can AI be used purposefully. Without that clarity, potential is wasted or misapplied.
Think Beyond Your Own Team
AI affects more than just one task or team. Success requires seeing beyond the immediate use case. How does it change collaboration with other departments? What dependencies does this create? This systemic view avoids siloed solutions and builds enterprise-wide value.
Build in Quality from the Start
AI systems must be explainable, reliable, fair, and legally compliant. Quality standards must guide AI development, not fix problems later. Can the AI explain its decisions? Does it operate consistently, without bias? Regular checks and improvements maintain these standards.
Final Thoughts
These eight principles are not about using the newest tech. They’re about putting AI to work for people – with clarity, purpose, and respect.
They ask us to think strategically and act precisely. To build systems that support people, not sideline them. To see AI not as a replacement for human ability, but as a partner that enhances it.