What Enterprise Technology Cycles Teach Us About AI Hype
Every enterprise technology wave arrives with the same promise: this one finally replaces the old, slow way of doing things. Enterprise teams have lived through several of these cycles — service-oriented architecture, business process management suites, the first RPA wave, the “cloud-first” mandate. Each one delivered real value. None of them delivered on the version of the pitch that got budget approved.
AI is not exempt from integration reality
The organizations that got value from every prior wave were the ones that treated the new technology as something that had to work inside existing systems, not around them. The organizations that struggled tried to bolt the new thing onto processes nobody had actually mapped. AI is repeating this exactly. A chatbot demo is easy. A chatbot that can see a customer's real account status, inside a real CRM, governed by real permissions, is integration work — the unglamorous kind.
What's actually different this time
The speed of capability improvement is genuinely new, and the accessibility — a business analyst can now prototype something in an afternoon that would have needed a development team five years ago — is real and significant. But speed of prototyping is not the same as speed of safe, governed, production deployment inside a regulated organization. That gap is where most of the current AI consulting market is either overselling or underdelivering.
The discipline that works hasn't changed much across three technology cycles: understand the systems and the data first, prove value on a narrow, well-chosen use case, and only then scale. AI makes the narrow use case faster to build. It doesn't make the discipline optional.