What is the curse of the POC?
Corporate AI training sessions look strikingly alike these days. The company pays for Claude Code or Cursor across the whole organization, sits everyone down, and sends each person off with a mission. The most common one is: build an AI that can replace you. Support is generous. Tools get provisioned, sessions run long. And that's where it stops.
The POC comes out looking convincing, because people build it on the hope that if the hype is this loud, surely something is possible. The problem is that a POC is just a POC. Turning it into production requires resources, time, and an organization to move in behind it, yet in AI projects that stage is somehow skipped entirely. So the same pattern repeats: something got built, but it never reached real work.
Why doesn't it cross into production?
The cause isn't the technology. It's the structure. Other R&D moves the organization along when investment is made, but AI adoption oddly gets framed as: you all need to find the time and try harder. With no dedicated task force, and your existing job still fully on your plate, AI work gets bolted on as extra work.
That's where individual motivation collapses. Even if you build something and raise your own efficiency, the company doesn't lighten your load; it fills the freed-up time with more work, so there's no reason to bother. Ironically, then, the people who push hardest through this process often end up preparing their own venture at the end rather than automating for the company. The company not offering a real opportunity is the true cause of failed adoption.
How do you tell whether a company can actually use AI?
The test is surprisingly simple: does the company have a team whose job is AI? Without a dedicated group, no matter how many tool licenses it buys or how much company-wide training it runs, the conclusion is nearly set in advance: this will end at the POC.
A prior burn from DX (digital transformation) sharpens the anxiety. Companies laid down full data pipelines, added OCR, and diligently built prediction models, yet the change frontline staff actually felt was vague, and the prediction models had few real uses. The memory of spending a lot while efficiency stayed unclear feeds the fear that AX will repeat it. And AX costs more than DX, with LLM, GPU, and server bills stacked on top. So only companies that put a dedicated team behind the investment and accumulate experiments get past the curse.
Where should you start — not something grand, but your own work
The fix is humble. Drop the idea of building something grand with AI and look closely at what you do every day. Pull apart the value chain and you'll find surprisingly many simple, repetitive stretches that have carried on out of sheer inertia. Swap one small piece like that for an agent and the change is bigger than expected.
Real cases prove it. One full-time trader had relied on an outside vendor to monitor important overnight news from foreign markets and relay it by messenger. After an agent course, he built his own; it responded far faster and could be customized exactly as he wanted, so he dropped the vendor. At a flower-wreath company, one person took phone orders as voice, converted them to text, and organized only the delivery details, stripping the padding out of the supply chain and genuinely making money.
Why domain knowledge, not coding?
What those cases share is not coding skill. It's domain knowledge. We verify AI's output far less easily than we assume. Just as you can't judge whether a lawyer agent's answer is correct if you don't know the law, only someone who knows the field can build something usable. That's exactly why a personalized outfit-recommendation app built by someone with deep fashion understanding actually worked.
So the talent that pulls ahead resembles someone good at an open-book exam. Just as an open book is useless without the ability to apply it, how you operate the open book that is AI decides your skill. Knowledge doesn't become unnecessary; you have to understand the basics to apply anything. Even vibe coding isn't just tossing prompts — you have to know a bit of the underlying layer to build something real.
What the organization must do
No organization becomes flawless from a single training session. A better approach is the case of a financial group that trained over a full year and gave individuals the time to complete an agent project and present it. Wait for your people, lighten their workload enough to let them experiment, and recognize AI as an actual work team. That is the job of leadership.
There's a request for those commissioning the training too. Nailing the answer in advance — insisting everyone learn only Claude Desktop, say — often makes it harder. Better to open the practice accounts, permissions, and billing ahead of time and leave the curriculum to the experts. And training is only the start. Listening alone changes nothing; only those who actually practice turn the tool into their own results.