Who Runs Your AI After It Ships?
The pilot is the easy part. Most AI projects don’t fail at launch — they fail three months later, when nobody owns keeping it accurate.
A pattern we keep running into with SME clients: someone on the team gets a chatbot, a content generator, or an automated workflow live within a few weeks. Everyone’s pleased with it. Then three to six months later, it’s quietly answering customer questions with outdated pricing, or the automated content has drifted off-brand, and nobody noticed because nobody was assigned to notice.
This isn’t a technology problem. The tools mostly work as advertised. It’s an operating model problem — a system was deployed without anyone owning what happens to it after deployment. Larger enterprises are running into a version of this at much bigger scale, which is why the current wave of advisory work from firms like Deloitte is less about which AI model to use and more about the operating model around it: who monitors it, who retrains it, who has the authority to turn it off when it starts getting things wrong.
For an SME, you don’t need a governance committee. You need one specific, unglamorous decision before launch: who checks this thing weekly, and what do they check for? For a customer-response system, that might mean a human spot-checking 10 conversations a week for tone and accuracy. For automated content, it might mean someone reviewing the calendar every Friday before it posts.
The economics are different too. A website has a fixed build cost. An AI system has an ongoing cost that scales with usage — and an ongoing accuracy cost if nobody's maintaining it. Budget for both, not just the build.
None of this is a reason to slow down on automation. It's a reason to ship it with an owner attached, not just a launch date.