Nobody reads 81 use cases, and I wouldn't either. So let me explain why we wrote them anyway.
A few weeks ago I wrote that we were 21 banking use cases in, and counting. The counting part turned out to be an understatement. We sat down, went through every floor of the bank, added what regulators, customers and our own delivery teams kept bringing up, and published the result as a public guide: 81 jobs AI can take over inside a bank. Each job gets three things spelled out, namely where the AI fits, what the evidence says, and what a strict bank has to govern.
The number itself matters less than what writing it all down taught me. I didn't expect two things. Banks have stopped shopping for individual use cases and are slowly turning into a different kind of bank. And what decides which banks get there is governance, more than the model, the budget or even the talent.
Banking has done this before. In the branch era, people did every task and software just recorded it. Then came the neo banks, with a great app and slick onboarding, and behind the glass largely the same manual work. If you've ever waited four days for a "digital" bank to review one document, you know how thin that glass is.
The next move is different, because it changes the work itself. In the AI bank the jobs run on governed AI, and the screens are almost a side effect. Servicing, fraud triage, KYC review, reconciliation and audit prep all run on the same platform, with a human on every decision that matters. Think of a bank where people supervise, decide and handle the edges, rather than a bank with a chatbot bolted on.
The neo bank changed how a bank looks. The AI bank changes what a bank is.
And this is happening now, at the leaders. In May 2026 CommBank resolved about 84.6 percent of its self service messaging interactions end to end, according to Microsoft. DBS has gen AI virtual assistants serving more than 10 million customers, plus a real time assistant for its customer service officers. At Morgan Stanley, advisors search the firm's own research with an AI assistant, and access to documents jumped from 20 percent to 80 percent according to OpenAI's case study. McKinsey's estimate of $200 to 340 billion a year sounds abstract until you spread it over 81 concrete jobs.
When we sorted all 81 jobs by floor, the result surprised me a bit.
The front office, where every AI conversation starts, holds 14 jobs. The biggest floor is the one nobody demos, the middle office, with 24: fraud scoring, AML triage, mule network detection, sanctions, perpetual KYC, SAR drafting. Add the specialized businesses (corporate, trade, wealth) and the bank behind the bank (IT, HR, audit, the model inventory itself), and about four out of five jobs sit outside the front office. Most of them are in places your customers will never see.
That makes sense once you think about it. An interface upgrade stays in the front, while a work upgrade spreads through the whole building.
Take SAR drafting. An alert survives triage, and the AI builds the case file, writes the narrative and attaches the evidence. The investigator reads it, edits it and owns the filing. The AI does the hours, the human does the judgment, and the audit trail records both. Do that 24 times and you have a floor. Do it 81 times and you have a bank.
This is the part I care about most, and it's the reason I wrote this post.
Every job in the catalog has a "what to govern" section. Write 81 of those in a row and you start to notice that it's the same four rules every time, wearing different clothes.
The first is guardrails, so the AI answers only from approved sources, checks identity before anything sensitive and refuses what it's not licensed to do. Then a model inventory, where every model has an owner, a purpose, data lineage and a risk tier, and shadow AI gets found instead of tolerated. Third, a human in the loop. The AI drafts, scores and recommends, and a named person owns the decision on the onboarding approval, the sanctions hit, the credit limit or the account freeze. And finally an audit trail, with every automated action logged, explainable and replayable when a model changes.
We didn't invent any of this, because supervisors are writing it down. APRA's CPS 230 has been in force since July 2025 and covers critical operations and material service providers, AI or not. MAS consulted on AI risk management guidelines that expect up to date AI inventories and human oversight. OJK published AI governance guidance for Indonesian banks in April 2025. The EU AI Act requires deployers of high risk systems to assign human oversight, keep the logs and cooperate with the authorities, and Paul wrote a readiness checklist for that one. In short, the rules are on paper now, and they will only get more specific.
After five years of working for banks, this is what I see. More and more use cases are moving toward AI, and the appetite is real, adoption is high. Getting something into production still takes a long time, though. A big part of that sits with central banks and regulators. They need to understand what is happening before anything can move, and to be fair, they want to. They just turn like an oil tanker.
Regulators want AI in banking. But a regulator turns like an oil tanker.
You can't push a tanker. You can make it very easy to see where you're going, and that's what good governance does. The clearer you can show a supervisor what the AI does, who decides and what gets logged, the sooner the tanker starts turning.
Every bank has to meet that bar, so the question becomes how many times you want to build it. Fifty jobs as fifty vendor projects means fifty security reviews, fifty ways of logging, fifty guardrail sets drifting apart, and an AI estate nobody can explain to a supervisor. Good luck with that meeting.
On one governed platform you set up the four rules once and every job inherits them, from the voice assistant to the fraud triage. In our experience that separates a risk review that takes a quarter from one that takes a meeting, because the review only has to look at what's actually new. It's also why we carry ISO 42001 next to ISO 27001, SOC 2 Type II and PCI DSS. The AI management system is certified once, and every use case lands inside it. And it's why we built a control tower for agentic operations, since oversight has to be built into the product once agents do the work.
My favorite use case in the whole catalog is the last one, the model inventory itself, run as an AI job. Agents find the models in use, read their cards and approvals, flag what's unregistered and build the evidence pack when the supervisor calls. The platform ends up governing itself. When we demo that one, the compliance team leans in further than the innovation team, and after enough of these projects I've learned that's the most reliable sign a project will make it to production.
Nobody switches on 81 jobs at once (please don't try). The advice from the 21 use case post still stands, now spread over five floors. Pick one job from the front office, because visible wins buy patience. Add one from the middle office, because that's where you want to learn governance early, and one from the back office, because it pays for the other two. Measure, share the numbers internally, and let the second wave ride on what the first wave already paid for.
A pile of pilots and an AI bank both start with three use cases. The difference shows up at use case four. On a governed foundation it inherits the guardrails, the inventory entry, the oversight model and the audit trail from the first three, so every new job lands faster and safer than the last. Fifty use cases stop being fifty projects and become one capability. That's how the AI bank gets built, quarter by quarter, and it's why the banks that start with governance pull away from the ones still trying to get pilot number seven past risk.
The whole guide is public: 81 jobs, five floors, every "what to govern" spelled out. If you want to go through your own longlist, I'm happy to map it on a whiteboard together. We'll look at which jobs fit your bank, in which order, and what governance looks like when it comes first.
Updated on 12 October 2026.
