Why people are essential for your scaling
You assign a team within your organization to build an AI tool that automatically creates reports. Five colleagues eagerly get to work and run a demo that works perfectly. They're excited, you're excited. Just a matter of rolling it out to the rest, you think. Three months later, the same five colleagues are still the only ones using the tool. The rest keeps writing reports themselves. Why? Because you never brought the rest of your organization along in the process.
Why your AI pilot gets stuck
Assigning a small group of enthusiasts is a good start. But scaling means dozens of people need to adapt their workflow: provide input, check output, follow the same quality standard. Most organizations underestimate this. If the tooling is already in place and you only then ask people to participate, they get lost in processes you impose on them. They feel no ownership, don't understand the choices, and fall back on old habits.
How to scale up
The 80/20 of edge cases
Many pilots work with 'nice' documents. The standard PDFs, typical requests, and predictable input that's easy to automate. That's 80% of the work. The challenge is in the remaining 20%. A file format that doesn't work well, unusual invoices, and unique requests. If you don't work with these 'edge cases' during your pilot, you'll only encounter them during scaling, and AI won't understand them. Test with difficult cases during your pilot to build quality and trust.
Assign ownership for your scaling
Two people are needed for scaling: the enthusiast and the expert. The enthusiast keeps the larger group engaged by building support, answering questions, and addressing concerns. The expert is technically skilled and determines quality and assesses whether the output is good enough. Both are needed to scale AI successfully.
Share autonomy
Think of AI autonomy as a slider from 0% (full human control) to 100% (full AI autonomy). During a pilot you're around 50%: AI does the work, humans check everything. Want to scale to dozens of users? Then you need to reach 97%. A point where you trust that AI handles the vast majority correctly. That jump from 50% to 97% is where pilots stall. Because that trust must be earned by testing edge cases and proving quality.
