We've all experienced it: You see a demo of an AI tool, and it's genuinely impressive. ChatGPT writes a passable email. Claude summarizes a document in seconds. Midjourney creates an image that would have taken hours to produce.
And then you try to use it for actual work. And it's... fine? But somehow the magic doesn't quite translate.
The 80% problem
Here's what we've observed working with teams: AI tools often get you 80% of the way to a useful result with almost no effort. The last 20% — the part that makes it actually usable in your specific context — takes real work.
This isn't a bug; it's the nature of the technology. AI systems are trained on vast amounts of general data, but your organization, your processes, your voice, your specific needs — those are particular. Bridging that gap requires human judgment, iteration, and often some organizational change.
Three things that help
Start with the unglamorous work. The most successful AI adoptions we've seen don't start with the flashy use cases. They start with the tedious, repetitive tasks that nobody loves but everybody does. Meeting summaries. First drafts of routine communications. Data formatting. The wins are smaller but more reliable, and they build confidence.
Build shared language and expectations. When AI use happens in the shadows — individual employees experimenting on their own — organizations miss the chance to learn together. Creating space for people to share what's working (and what isn't) accelerates learning for everyone.
Accept that iteration is part of the process. The first prompt rarely produces the best result. The first workflow design needs refinement. This isn't failure; it's how the technology works. Teams that expect to iterate get better results than those looking for magic.
The human work of AI adoption
The technology is remarkable and getting better rapidly. But the real work of AI adoption isn't technological — it's organizational. It's about building trust, changing habits, and redesigning processes to take advantage of new capabilities.
That's the work we help teams do at Alongside. Not because the technology is complicated, but because the people side always is.