The AI conversation in fund administration has moved quickly from whether to when. Vendor decks promise transformed operations, and the underlying technology is genuinely capable. Yet the evidence from early adopters is sobering: most enterprise AI projects fail, and they rarely fail because the model was not good enough.

MIT's 2025 State of AI in Business research found that around 95 per cent of generative AI pilots deliver no measurable impact on profit and loss. The researchers attributed the failures not to model capability but to poor workflow integration, weak data readiness, and the absence of a defined outcome. UK financial services tell a similar story from the other direction. The Bank of England and FCA's most recent survey found that 75 per cent of firms already use AI, and that the constraints they report are dominated by data quality and governance rather than the technology itself.

For a regulated fund administrator, the lesson is direct. Readiness is organisational, not technological. In regulated firms, most AI project failures are control failures and data failures wearing a technology costume. So before you evaluate any platform, evaluate your firm. The checklist below covers six dimensions that determine whether an AI deployment lands as a controlled operational improvement or becomes an expensive pilot that never leaves the sandbox.

95% of generative AI pilots deliver no measurable P&L impact (MIT, 2025)
75% of UK financial services firms already use AI (Bank of England / FCA, 2024)
4 of 5 top perceived AI risks reported by firms are data-related

1. Data quality: do you have a golden source?

AI amplifies whatever data it is given. Feed it a clean, authoritative record of your entities, clients, and vendors and it will prepare work quickly and accurately. Feed it three conflicting spellings of the same client across three systems and it will industrialise the confusion.

Ask yourselves:

If the honest answers are uncomfortable, that is not a reason to abandon AI. It is your first work package, and it pays for itself whether or not you ever buy a platform.

2. Process definition: can you write it down?

An AI platform automates the process you actually have, not the one described in the procedures manual. In most firms those are different processes, and the gap between them is where deployments quietly fail.

"A process that cannot be written down cannot be safely automated. The distance between the documented procedure and daily practice is where AI projects go to die."

3. The control model: what must never be automated?

Automation should strengthen a control environment, not thin it out. Before any vendor conversation, map your controls and decide, deliberately, which of them are load-bearing.

A useful discipline: treat every control as non-waivable by default, and require a documented decision to relax one. Firms that discover their controls only exist on paper tend to discover it at the worst possible moment.

4. Decision governance: who is accountable for each output?

This is the dimension regulated firms most often skip, and the one supervisors increasingly ask about. The emerging expectation, visible in the Bank of England and FCA's survey work and in board-level guidance across jurisdictions, is that firms can show how their use of AI is governed: who is accountable for each AI-assisted output, and how the human decision is evidenced.

Regulatory note: under Article 38 of the Data Protection (Jersey) Law 2018, individuals have the right not to be subject to decisions with legal or similarly significant effect based solely on automated processing. In practice this means a person, not a machine, makes the decision, and your records must be able to prove that the human involvement was real rather than a rubber stamp. Equivalent provisions exist under the UK and EU GDPR.

This is the design principle behind CoreAdmin: the AI prepares the work, a named officer makes the decision, and the platform records both sides of that exchange automatically. Whatever platform you choose, insist on the same shape.

5. People: champions, training, and honest framing

The MIT research is blunt on this point: the pilots that succeed are pulled by the people who do the work, not pushed by an innovation function. Technology adoption in a fund administration team is a human problem long before it is a technical one.

6. Vendor due diligence: six questions for any AI platform

Once the internal dimensions are in reasonable shape, the vendor conversation becomes far more productive, because you know what to demand. Put these questions to every platform, and treat vague answers as answers.

1
Where is our data hosted and processed? Which jurisdictions, which sub-processors, which model providers. Is client data ever used to train models shared with other customers?
2
Is any output ever auto-approved? Map every path where AI output takes effect without human sign-off. If the honest answer is "yes, above a confidence threshold", you need to know the threshold and who set it.
3
Is there an AI-interaction log? Every prompt, output, acceptance, and correction recorded and exportable, so you can show an auditor or the JFSC exactly what the AI did and what a human decided.
4
What happens when the model is wrong? How is low confidence surfaced, where do exceptions queue, who is notified, and do corrections feed back into behaviour? A vendor who cannot describe failure handling has not thought about it.
5
How are our controls enforced? Segregation of duties and four-eyes approval should be hard constraints in the platform, impossible to bypass, not a policy the platform politely assumes.
6
What is the exit? Data export formats, notice periods, and what the vendor retains after termination. Ask on day one, while you still have leverage.

Using the checklist

Score each dimension honestly with the people who do the work, not just the management team. Gaps in the first three dimensions (data, process, controls) are sequencing questions: fix the worst of them first, then start with one narrow, well-controlled workflow rather than a firm-wide programme. Gaps in decision governance need closing regardless of your AI plans, because the accountability expectations behind them are arriving either way. Gaps in the last two shape your rollout plan and your procurement questions.

Firms that pass this checklist do not avoid AI risk entirely, but they take it with their eyes open, with controls that hold, and with evidence that stands up to inspection. That is the difference between the 5 per cent of projects that compound and the 95 per cent that stall. If you want to see what a governed deployment looks like against your own workflows, with the AI preparing, named officers deciding, and every interaction recorded, a 30-minute CoreAdmin demo is the quickest way to test the standard this checklist sets.

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