Then “digital transformation” showed up on every slide deck, whether or not the transformation was real. Each wave carried pioneers who built the future — and illusionists who only borrowed the vocabulary.
Now it’s AI’s turn. And the illusionists are louder than ever.
The Seduction of the Label
There’s no denying it: AI dazzles. A good demo can look like magic. A clever prompt can make even the most ordinary system look extraordinary. And in a world where attention is currency, the temptation to label every automation, every wrapper, every API call as “AI” is almost irresistible.
That’s AI-washing. The practice of taking something shallow and branding it as intelligence.
And just like cloud-washing before it, the risk is not in the marketing itself — it’s in the expectations it sets. Boards believe they’re funding transformation, when in reality they’re buying experiments. Executives assume competitive advantage, when in fact the foundation is brittle.
The Cost of Illusion
AI-washing feels harmless in the short term. The demo works, the room applauds, the narrative holds. But the cracks show quickly:
- No learning. Real AI gets smarter with use. A static prompt doesn’t.
- No scale. Enterprise workloads demand resilience; quick hacks collapse under pressure.
- No governance. Regulators and auditors expect explainability, not hand-waving.
- No trust. Business users know when they’re dealing with smoke and mirrors.
Illusions don’t just fail — they erode confidence. And confidence, once lost, is expensive to win back.
The Builders in the Background
Meanwhile, the real builders are quieter. They’re not chasing headlines; they’re chasing hard problems.
They’re cleaning messy enterprise data that nobody wants to touch.
They’re designing feedback loops so the system learns from its users.
They’re embedding governance frameworks to ensure trust.
They’re pushing beyond polished demos to deliver AI that holds up in procurement, finance, supply chain, and audit rooms.
It’s slower. It’s harder. It doesn’t always look spectacular in week one. But it’s the work that endures.
Lessons From the Past
We’ve been here before. When “cloud” first became fashionable, some vendors slapped the label on hosted servers and called it a day. Enterprises that bought into the illusion were burned — migrations that promised agility delivered only complexity.
The companies that thrived were the ones that looked past the label and asked: What is the architecture? How does it scale? Where is the governance?
AI demands the same scrutiny today.
Choosing Integrity Over Illusion
This isn’t an argument against prompting, APIs, or clever automation. Those are powerful tools — the building blocks of the future. The danger lies in pretending they are the future in themselves.
Leaders have a responsibility here. They must ask the harder questions:
- What does this system learn from?
- How is it governed?
- Can it scale under real enterprise conditions?
- Does it build durable capability, or is it a temporary trick?
Because in a world where anyone can stitch together a few prompts and call it AI, the true test isn’t how dazzling the demo is. It’s whether the system can withstand the weight of real business.
The Hard Truth
The story of AI will not be written by illusionists. They will fade as quickly as they rise. The story will be written by builders — those willing to do the invisible work, grounded in integrity, purpose, and resilience.
So the question for enterprises is not whether AI is ready.
It’s this: are we ready to separate the real builders from the illusionists?