If you are a Chief Operating Officer in a fund management business today, the pressure to adopt AI is everywhere.
Your board wants a clear AI strategy. Investors expect visible signs of innovation. Vendors are embedding AI into every platform. Competitors are increasingly vocal about how they’re “leveraging AI” to drive performance and efficiency.
At some point, the question lands squarely on your desk:
“What are we doing about AI?”
And beneath that lies a more uncomfortable one:
“Are we already behind?”
It is a legitimate concern. AI promises faster insights, more efficient operations, and greater scalable decision-making. In an industry where speed, control and precision matter, that’s hard to ignore.
But as a COO, your role is not to follow momentum. It’s to protect operational integrity, manage risk, and ensure the business can scale reliably.
Which is precisely why rushing into AI may be the wrong move.
This is not an AI problem. It is an operating model problem.
AI is only as good as the data, processes and controls that underpin it.
In many fund management firms, those foundations remain fragmented. Portfolio data, investor records, performance reporting, compliance workflows and operational metrics are often spread across multiple systems, spreadsheets and teams.
As a result, there is rarely a single, consistent version of the truth.
You see the symptoms every day. Different teams produce slightly different numbers. Reconciliations are manual. Reporting requires interpretation rather than inspiring confidence.
Now introduce AI into that environment. It won’t create clarity. You will amplify inconsistency at scale.
AI will not fix your operations. It will expose them.
There is a common assumption that AI will streamline operations and remove inefficiencies.
It will not.
In reality, AI does not redesign processes or resolve data fragmentation. It simply consumes what already exists. If your data is inconsistent, AI outputs will be inconsistent. If your reporting processes are fragile, AI will inherit that fragility and amplify it at scale.
For a COO, that is not simply a technical issue. It is a governance, risk and control issue.
Every output generated by AI must be explainable, auditable and defensible. This is particularly critical in fund management, where operational outputs feed directly into investor reporting, regulatory submissions and critical internal decision-making.
In many ways, AI increases the importance of operational discipline. It does not reduce it.
The real risk is scalable error
AI systems deliver answers with confidence. That is part of their appeal, but it is also part of the risk.
In a fragmented operating environment, this creates a dangerous dynamic. Instead of teams identifying discrepancies, AI can present answers that appear credible and arrive instantly.
The underlying errors do not disappear. They simply spread faster.
As a COO, that should be the central concern. Not whether AI is powerful, but whether your operating model is robust enough to support it.
Because in fund management, errors are never purely t operational. They affect investor trust, capital allocation and regulatory compliance.
The pressure to “Act” is the trap
The push to adopt AI rarely comes from a single direction. It is cumulative and builds from all sides.
Boards want progress. Investors want modernisation. Vendors promise quick wins. Internally, teams see opportunities to automate and improve efficiency.
So, firms take action. Pilot projects. Stand-alone tools. Point solutions.
It creates the appearance of momentum.
But from an operating perspective, this often makes the problem worse. You are layering intelligence onto fragmentation, increasing complexity while reducing visibility and control.
That is the opposite of what a COO is accountable for.
The disciplined decision is to wait
Resisting that pressure to adopt AI requires confidence in your role and clarity in your priorities.
Saying “not yet” to AI is not a sign of lagging behind. It is a sign of operational discipline.
The firms that will benefit most from AI are unlikely to be the earliest adopters. They will be the organisations that ensure their operational foundations are strong enough to support it.
For a COO, that means focusing on the fundamentals:
- Establishing a single, trusted view of core data
- Aligning definitions across investment, finance and operations
- Removing duplication and manual reconciliation
- Ensuring reporting is consistent, accurate and auditable
- Creating end-to-end visibility across the investment lifecycle
This work is not visible externally. It is not typically branded as innovation. Yet it is the work that ultimately determines whether AI creates value or introduces risk.
AI is an accelerator of your operating model
AI will become a core capability in fund management. That is not in question. The real question for a COO is whether it will strengthen your operating model or expose its weaknesses. If your data and processes are aligned, AI becomes a powerful lever for scale and efficiency. If they are not, AI amplifies existing flaws, increases operational risk and undermines trust.
The pressure to adopt AI will continue to grow. But the most effective COOs are not asking how quickly they can implement it. They are asking a more important question: Can our operating model support it? Until the answer is yes, the most strategic move you can make is simple. Wait. The firms that win with AI will not be those that adopt it first. They will be those that are operationally ready when they do.
Thinking about AI but unsure whether your operating model is ready?
We work with fund management firms to assess operational readiness, improve data integrity and create the foundations that enable AI to deliver measurable business value.
