Recently I sat down with someone who lives at the messy intersection of astrophysics, data science and corporate AI adoption, and a few things became painfully clear. Big companies do not need more theory, they need translation, they need tangible examples, and they need a path from inspiration to safe, scalable deployment. If your org is asking, “what can we do with generative AI?” the first honest answer might be, “what are your tasks? I don’t know what your tasks are.”
The single most practical move is simple, but rarely executed well, because it requires curiosity and patience. Find real people in the business who have tried something with AI, document what they did, and show it to others. Make short videos, do demos, publish internal showcases. When a colleague sees another employee in a similar role saying, “I built a chatbot on our requirements documentation and it helps me,” the leap from abstract hype to practical adoption becomes plausible.
That transition is crucial. As the phrase went in the conversation, “we’re in this bubble” versus the rest of the world. Most people do not care about model architectures, they care if a tool solves their problem. Show them someone in their business unit using it, and suddenly the gap closes.
If you want engineers and domain experts energized, give them problems to gnaw on. ZEISS ran internal data science hackathons that began as pure experimentation, then shifted into generative AI thons. The format is neat and practical.
This is not a toy exercise. It creates a self-reinforcing wheel of traction. It also builds an internal culture where experimentation is normal, not suspicious.
Changing workflows at scale means acting at two levels at once. You need board level sponsorship to make it safe and legitimate, and grassroots adoption to make it useful. Showcasing a senior leader using the tool in public can be a catalyst, because people then feel permitted to try it themselves. Conversely, leaving access unchecked leads to messy situations, the kind that were called “almost like a black market kind of,” where everyone uses tools privately and nobody knows the rules.
That is dangerous. It leaks data, creates inequality in productivity, and destroys trust. Large organizations usually have the compliance teams needed to build safe, internal versions of these tools, so the real work is aligning policy, training and product.
Past breaches and misuse of early models left many understandably skeptical. Trust once broken is not just about fixing the tech, it is about demonstrating consistent, safe behavior over time. One helpful approach is to give people clear, practical guidelines about “what you can and cannot put into AI tools,” combined with training that actually teaches them how to get good results.
There’s also a subtle human element, which is curiosity. People who tried early systems and were disappointed can be coaxed back not by hype, but by experience. Let them play with a well-crafted, relevant demo. Spark curiosity, don’t shove fear in their face.
One misconception I see is the temptation to throw every problem at generative AI. Two years ago that was definitely a bad idea. If Excel can sum columns reliably, don’t paste the spreadsheet into a public model and hope it behaves. Use the right tool for the job.
Today the landscape has shifted. Models can call functions, execute Python snippets, and orchestrate other tools. The smarter play is to use a large language model as the brains that decides which deterministic tool to call, and when. Think of it as orchestration, not replacement. In practice, that means embedding AI into existing workflows, not replacing them.
Yes, the infrastructure side matters, and yes, you should make sure models can scale. But the harder part is adoption. Scaling usage internally requires:
If your users still need to copy, paste and jump between five apps to get a task done, adoption will stall. Seamless integration into everyday workflows is where the real ROI happens.
Generative AI will keep getting better, and integration will follow. The most important job is not to predict the exact shape of models five years from now, it is to prepare people to use them responsibly and imaginatively today. If you do that, you’ll free up talent from drudgery, enable more creative work, and maybe get to a future where people spend less time glued to screens, and more time doing things that actually matter to them.
That future is not automatic, it is curated. If your company wants to surf the wave, stop asking whether to adopt AI and start asking how to make it part of the way you solve real problems.
Commenting Rules: Being critical is fine, if you are being rude, we’ll delete your stuff. Please do not put your URL in the comment text and please use your PERSONAL name or initials and not your business name, as the latter comes off like spam. Have fun and thanks for your input.
Join a growing community. Every Friday I share the most recent insights from what I have been up to, directly to your inbox.
