The short version, for anyone still imagining that AI lives only in research papers and Twitter threads, is this, the bubble has burst. People who don’t code, don’t train models, and don’t spend their weekends arguing about tokenization are using chatbots at dinner, on beaches, and in meetings. I heard the same thing from a colleague who said, “I spent a month last winter on vacation in Fuerteventura playing beach volleyball,” and came back with strangers telling him about ChatGPT. That accidental exposure matters more than we think.
What matters even more is how you make this technology useful for 45,000 people who all have different jobs. You can’t shoehorn every problem into a generic demo. You have to translate the tech into someone’s actual day, in their language, with their documents, in their workflow.
When a business asks, “How can we use generative AI?” the honest answer is often, “Tell me what you actually do.” That’s the problem most technologists skip. If you show someone a general demo it’s abstract and distant. If you show an end user a colleague in the same department using a chatbot to solve a task they actually face, it’s suddenly plausible.
So here’s a simple blueprint that works, and yes it’s delightfully low tech:
As my guest put it, “show them what generative AI can do and then open the door for them to think for themselves.” That’s the difference between inspiration and intimidation.
There are two levers, top down and bottom up. You need both. Get buy-in from the board so people know using these tools isn’t forbidden, and cultivate a grassroots community so curious people can get hands on. One trick that actually moves minds quickly, is getting a high-level leader to use the tool publicly. As he said, “if the boss of the boss of the boss is using this technology, then maybe it’s okay if I use it too.” That’s legitimacy delivered through example, not policy memos.
The alternative is worse, a kind of corporate black market where people secretly use free public tools and drop sensitive information into them. That’s a cultural problem, a security problem, and a morale problem all at once. Fixing it means offering safe, sanctioned tools and clear guidelines, so people don’t have to hide.
There’s a useful humility here. Generative AI is not the answer for every problem. Two years ago the wrong move was dumping Excel data into an LLM and expecting a robust financial report. If Excel or a simple function will do the job, use Excel. “Don’t use it to solve problems which you can already solve with existing technologies,” was the blunt, correct point.
But the tech evolved fast, and now LLMs can act as orchestrators, calling deterministic tools when needed. The best pattern I’m seeing is treating the LLM as the brains at decision nodes, asking, “Should I call the Python function, query the database, or hand this to a human?” That turns the model into an orchestrator, not a blind sledgehammer.
Trust matters more than capability. Many folks tried early chatbots, got poor results, and declared the tech useless. Others are hyped and loud. That creates FOMO and cynicism in equal measure. The key question is, “How do you build trust in a system?” That’s fundamentally different from convincing people it’s inevitable.
One approach is to spark curiosity rather than pressure. Show practical, low-risk wins. Teach people how to get good outputs. Offer sandboxed environments where they can play without exposing sensitive data. That sounds basic, because it is, but it works.
Forget magic. The immediate wins come from integration. When document search, chat, and code assistance live inside the tools people already use, adoption becomes frictionless. We already have the tech to let an assistant book travel, order a ride, and check into a hotel, without a dozen manual clicks. The missing piece is integration and the right guardrails.
Also, for AI to be a real assistant, it needs delegated rights. It should be able to act on your behalf with the level of access you choose, otherwise it’s just an expensive consultant that types slowly. “The assistant needs to be able to act on your behalf,” was the point made plainly, and it’s where UX, security, and trust collide.
Stop asking if you should use AI and instead ask how you should use it. Pull together a technical translator, someone who can take your noisy, headline-fueled ideas and translate them into practical experiments. Invest in showcases so people can see plausible, relevant uses. Make it safe to experiment, and reward people who go build small, valuable prototypes.
And be patient. Integration will happen, workflows will shift, and a lot of the heavy lifting is organizational, not algorithmic. Meanwhile, people will still value human craft, whether that’s artisanal furniture or lovingly written code. There will be new markets for human-made things, because humans like other humans.
If you want to make AI useful at scale, focus on the small, human stuff, not on wowing people with hottest model. Translate, showcase, train, and then scale with care. Do that, and you’ll move from theory to practice, from a tech bubble to tangible value.
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