AI Alpha: Turning Middle- and Back-Office Operations Into a Source of Return

At least in principle, the industry agrees that deploying AI can make middle- and back-office work faster and cleaner. Efficiency and cost savings are hard to debate, but we’re seeing a new category of value emerge. Using AI can shift operational work from a cost center to a revenue center. As you take a more mature approach to AI, you discover that it can extend and build upon the concept of operational alpha.

For example, firms tend to look at activities like reconciliation, reporting, data management, and treasury overhead. They budget them accordingly and judge them on how much they cost to deliver. Every dollar saved improves your profitability. While you can use agents to continue along that same cost efficiency path, you can also do more.

Trade execution is an excellent case in point. Agents can handle reconciliation and routing. Having agentic capabilities also allows you to do things like build dashboards and tools that help you optimize. A counterparty revenue dashboard illustrates the point.

If an analyst could easily build analytics on top of the trade data you already hold, you could have a view that lays out the commissions and fees you pay each counterparty across every trade. It’s bespoke to your counterparty arrangements and agreements. You could read it counterparty by counterparty and see what execution is costing you. When a counterparty’s rate is out of line, you could then either renegotiate or route orders elsewhere.

AI comes into the picture because building that view used to require costly custom builds, so nobody built immediate and contextual views of their trade data. Now, an analyst could describe what they want, have the platform build it, and the effort pays for itself the first time you renegotiate. The same logic runs straight through financing and margin. Drop the cost of producing an insight far enough, and the impact shows up in your trade economics.

Bespoke service at scale

The impact we’re describing takes you beyond the choices between customization and ROI. Whether it’s you or your vendor, there’s a fixed budget and a published roadmap. That situation tends to put niche reports and bespoke features to the back of the release queue.

AI changes who does the building. Users can build what they need themselves on top of the same security master, book of record, and reconciliations that already run your shop, with no custom code and no slot to wait for in the release queue. They describe what they want, and the results, such as this hypothetical dashboard, go well past anything your old BI tool could draw.

Aside from the convenience, this flexibility also flips the economics. Tailoring a service to one client, team, or user used to cost more than it could ever recoup. But if you take the cost of that tailoring down near zero, it can fit your operation without paying a premium.

Onboarding and the information gap

It’s reasonable to expect that almost every task with a clearly defined runbook and outcome will be receptive to automation within the next few years. Anything with a defined path and a known set of exceptions can be handed off to an agent without much friction. But that future becomes less likely when processes take copious amounts of human judgment.

A good comparison would be between reconciliation and activities like onboarding, new launches, or complex bespoke instruments such as private loans. With reconciliation, the standard sequence runs end to end. Exceptions are just another documented path the agent can follow.

But other areas make it harder to translate your intentions into what you are actually trying to get the platform to do and then turn that into a configuration. We often see this during client implementations, but it’s similar to something like onboarding a new limited partner or launching a new fund.

When we onboard a client, our implementation team knows the platform cold and has only just started to get to know your business. You understand your business needs but are only broadly familiar with the platform. Most of the work during onboarding is closing that gap quickly and efficiently.

Instead of sending you a 10-page memo on what the platform can and cannot do, we put an interactive dashboard in front of you; you make a configuration choice, and you watch what it does on the spot. Three rounds of working sessions and email collapse into one working session. You would see the same parallel if you were negotiating investor agreements or building a new interval fund.

In these kinds of scenarios, routine work runs itself. For data ingestion, an agent can take in your files and fuzzy-match columns to the data model, the job an analyst used to do by hand for hours. But the reading of the intent and workflows behind the system stays with the person.

Common sources of resistance

This bullishness on what AI can deliver is playing out in real time. But you’ll still encounter various sources of resistance. Volume is the first of these. If you point an agent at a large enough dataset, it will eventually time out before it returns anything usable. Some of the work here involves scoping and factoring AI capabilities so that they work within your business-as-usual volumes and scale to peak activity periods, like month- or quarter-end.

Many of the challenges come from AI’s probabilistic nature. Responses are meant to be dynamic, which isn’t always an advantage.

• Patchy inputs. If you give an agent half the picture, the model underneath it will try to fill in the other half itself. Hallucinations become likely. But if your prompts have to spell out every input and limit, it runs counter to the whole instinct of handing it as little as possible and still getting back what you wanted.
• Inconsistency. A workflow that ran cleanly on Monday might become temperamental on Tuesday and refuse to run a task it did the day before. That becomes a nuisance when the close has to tie out, and the report has to go. Inconsistency can turn into real exposure, and the tools need to nail run-to-run behavior before much else.
• Confirmation bias. With hard questions, LLMs have a built-in tendency to agree with whatever you told them last. If you tell an agent it’s wrong, it might back down to be agreeable and then tell you the opposite a minute later. That’s not the behavior you want when an analyst is legitimately mistaken.

Design your agents to interact in natural language but reason in a structured, coded way, and you avoid much of the friction.

From operational alpha to AI alpha

Demand is playing as much of a part as technology in pushing these AI capabilities forward. They’ve become a component of what asset managers look for in their vendors. They also increasingly show up in the operational due diligence that investors carry out when looking at managers. The more sophisticated they are, the more they want to see AI appear beyond mere efficiency. Some of it comes down to their specific reporting requirements. In the past, such bespoke reports were challenging to implement. Now, much less so.

They want to see AI benefits show up in your performance, too. There’s already a name for return that comes from running operations well rather than picking better securities.