Written by Zudu CEO, Paul Duffy.
Every sector update this year is reading the same way. Healthcare is on the edge of an AI-driven overhaul. Financial services are racing to fight AI-generated fraud with AI-powered defence. Retailers are rebuilding how they forecast demand and talk to customers. The commentary treats each of these as the start of something new, a lot of which we are supporting at Zudu.
However, at Zudu, and at businesses like ours, we read that coverage from a different vantage point. Software delivery and AI consultancies have been living inside this exact pace of change for years, not months. Every new model, framework, and integration pattern lands on our desks first, is tested against a real client problem, sense-checked against their business, their strategy and their industry and either earns its place in a production system or is discarded. Long before a sector “adopts AI”, someone (us) has been building, breaking and rebuilding the tools that make that adoption possible.
Take three sectors already living through it. The NHS is rolling out validated AI diagnostic tools and AI scribes under its 10-Year Health Plan, backed by a £21 million Diagnostic Fund. Financial services firms are deploying systems like Experian’s Transaction Forensics, running dozens of AI models against every transaction to catch fraud that would have slipped past a human reviewer. Retailers are using AI so hard that McKinsey now puts adoption at 88% across the sector, with personalisation engines lifting revenue by 10 to 15% and demand forecasting models cutting stockouts and excess stock that used to be written off as the cost of doing business. Each of these stories gets told as a healthcare story, a banking story, a retail story. Rarely does anyone mention the delivery teams who had to understand the technology, prove it against real data, and ship something a regulator, a clinician or a store operator would actually trust.
That work is harder than it looks from the outside. It means learning a new AI capability well enough to explain its limits to a client who has never had to think about model drift or hallucination risk. It means building the guardrails, the testing, the change management that turns a clever demo into something a hospital or a bank will stake its reputation on. Software delivery teams are doing this simultaneously across half a dozen industries, which means we see the pattern before any single sector does.
I’m not writing this for tech companies to be applauded. It’s just exciting and often absolutely bonkers to watch it unfold in real time. I’ve said before it’s a privilege to be leading a team that is uncovering problems that have never been solved before or implementing new tech that didn’t exist a year or two ago. The organisations translating that raw capability into something a business can actually run on, safely and repeatably, are the ones absorbing the pace of change first. We are learning new tools so we can teach our clients to use them. We are implementing change so our clients can implement their own.
If your organisation is a few years into an AI journey and starting to feel the pace, it is worth remembering that pace has been the day job for delivery teams like ours long before it reached your sector. That experience is exactly why we would rather be your partner through it than watch from the sidelines.
