Buy-side firms whose agentic AI deployments are generating measurable returns share one characteristic: they treated data readiness as the strategic investment, not just the AI capabilities dependent on it . The firms still conducting expensive experiments in both liquid and private markets are almost without exception the ones who got that order wrong.
This isn’t an observation about technology. It’s an observation about sequencing. The firms pulling ahead didn’t necessarily have better AI or bigger budgets. They made a different decision about where to start.
“Getting digitally organised isn’t the precondition to AI transformation. For buy-side firms, it is the transformation.”
Agentic AI, systems that execute workflows, make decisions within defined parameters, and interact with live operational data without step-by-step human instruction, is in active deployment across asset management, credit, and private funds. The technology itself is no longer the variable. What’s breaking deployments, in liquid markets and private funds alike, is the data environment the technology has to operate in.
Why the same problem shows up differently across the buy-side
In liquid strategies, the data estate is large, fast-moving, and relatively standardised,but rarely as clean as it looks from the outside. Reference data inconsistencies that were manageable when a human was reviewing them become compounding errors the moment an agent starts acting on them autonomously. Metadata that nobody captured systematically because nobody needed it until now. Governance frameworks written for human decision-making that have no equivalent for a system executing two thousand decisions a day.
In private funds the starting point is harder. Bespoke fund structures, varied LP arrangements, multi-jurisdictional reporting requirements; — the data environment is inherently more heterogeneous, and that complexity has historically made private funds slower to benefit from automation. The firms making real progress in private markets have been ruthlessly specific about where they started: the workflows where the data was already clean enough, the process defined enough, the ROI measurable enough to build the internal case for the next step. LP reporting efficiency, capital call and distribution processing, fee allocation accuracy. Not because those are the most exciting use cases, but because they were the ones where the foundation existed to support them.
The pattern holds across both: AI doesn’t paper over data problems. It exposes them, at speed, and at a cost that scales with how deeply the system is embedded in live operations.
What agent-ready infrastructure actually requires
The firms that have built this well,in both liquid and private markets, didn’t follow a framework. They worked through a sequence. The starting point was always the same: a single, reconciled version of reference data that every downstream system could trust. Not a project to fix everything, but a deliberate decision to resolve the specific data that the first agentic workflow would depend on.
From there, the work expanded. Metadata frameworks that gave agentic workflows the context to understand not just what data they were interacting with , but where it came from and how much to trust it. Process documentation that was honest enough to automate,because agentic systems can only replicate what’s been defined, and firms with undocumented or inconsistent processes discovered that AI didn’t resolve the inconsistency, it amplified it. And governance architecture that existed before the first agent went live: clear accountability for autonomous decisions, audit trails that a compliance function or an LP could actually interrogate, and explainability built into the system rather than retrofitted after the first difficult question.
The firms that tried to build governance in retrospect found it significantly harder than the ones that treated it as part of the initial design. That’s as true in private funds where LP scrutiny of operational decisions is intense as it is in liquid strategies dealing with regulatory oversight.
“Agentic AI doesn’t create operational risk. It reveals the operational risk that was already there.”
Where AI is generating real operating leverage today
The most useful signal right now isn’t which firms have announced AI programmes. It’s which ones have moved a workflow from pilot to production and kept it there. That distinction matters because the gap between a promising pilot and a reliable production deployment is where most programmes stall and the reason they stall is almost always the same.
In liquid strategies, the deployments generating the clearest returns are concentrated in data quality management, AI-driven workflows that monitor, flag, and in some cases resolve reference data anomalies before they reach downstream processes, and in reconciliation, where the combination of high volume and rule-based logic makes agentic automation both tractable and high-value. The firms running these in production aren’t doing so because they solved AI. They’re doing so because they solved the data environment first.
In private funds, the operating leverage is showing up in a different set of workflows but for the same underlying reason. Firms generating real returns from AI in private markets, measurably faster LP reporting cycles, more accurate fee allocation, lower cost per fund operation, got there by picking one workflow where the data quality was high enough to start, proving the model, then expanding deliberately. The ones still running experiments are, in most cases, still looking for that starting point.
What this means for how buy-side firms should think about AI investment
The most durable AI operating models being built on the buy-side right now don’t treat AI as a product sitting on top of existing infrastructure. They treat it as the foundational infrastructure: embedded beneath every workflow, every data layer, every decision point where speed and accuracy compound into competitive advantage. That means the investment thesis for AI isn’t ‘which tools should we deploy.’ It’s ‘what does our operational foundation need to look like for those tools to actually work.’
At IVP, this is the logic that runs through every solution we build. AI isn’t a standalone capability we’ve added, it’s the foundational intelligence layer embedded across the platforms and solutions. In master data management, AI agents surface and resolve data quality issues before they propagate. In private funds administration, they accelerate reporting cycles and reduce manual intervention in fee and expense processing. In reconciliation, they handle exception management at a volume and speed that manual processes can’t match. The result isn’t a set of AI point solutions. It’s an operating model where data accuracy, process efficiency, and decision speed compound across the investment lifecycle.
The firms that adopted this model early, treating AI as substrate rather than feature, are running a different race to the ones still evaluating tools. The distance between those two groups is growing, and it’s growing faster than most firms have accounted for in their planning.
MindMeld London 2026 — 11 June, etc.venues County Hall.
We’re examining both sides of this with buy-side practitioners on 11 June.
Invitation only. Request An Invite: https://www.ivp.in/request-an-invite-mm/
Frequently Asked Questions
Q: What is agentic AI and why does it matter for buy-side firms?
Agentic AI refers to systems that act autonomously within defined parameters, executing workflows, making decisions, and interacting with live data without step-by-step human instruction. For buy-side firms, this includes AI that manages reconciliation exceptions, generates LP reports, monitors data quality, and drives fund operations with far less manual intervention . Unlike co-pilot tools, agentic systems require robust data foundations because they act on data rather than flagging it for human review.
Q: Why do most buy-side AI deployments stall before they scale?
Almost always the same reason: the data infrastructure underneath the AI wasn’t built for autonomous systems. Fragmented reference data, metadata gaps, undocumented processes, and governance frameworks designed for human decision-making all become critical-path blockers the moment agentic AI tries to operate at scale. The pilot looked fine because it ran on a curated data set. Production fails because it has to work on the real one.
Q: Is the data readiness challenge different in private funds versus liquid strategies?
Yes — private funds starts from a harder position. The data environment is more heterogeneous: bespoke fund structures, varied LP arrangements, multi-jurisdictional reporting requirements. Standardisation that liquid strategies can often take for granted has to be built from scratch in private markets. That’s why the firms generating AI operating leverage in private funds have been more disciplined about sequencing, starting where data quality was highest and expanding from there, rather than attempting broad deployment across a fragmented data estate.
Q: What does an agent-ready data foundation require?
Four things need to be in place: reconciled reference data that every system agrees on, metadata frameworks that give agents context about the data they’re working with, process documentation honest enough to automate, and governance architecture that existed before the first agent went live. Firms that retrofitted governance after deployment found it significantly more expensive than firms that built it in from the start.
Q: Where is AI generating measurable value in private funds operations right now?
The clearest returns are in LP reporting efficiency, capital call and distribution processing, fee allocation accuracy, and cost reduction per fund operation. These aren’t the most ambitious use cases, they’re the ones where the data foundation was already solid enough to support automation. Firms that started here, proved the model, and expanded deliberately are now running meaningfully different operations to firms still in pilot mode.
Q: How does IVP embed AI across the investment lifecycle?
IVP treats AI as the foundational layer across its platform rather than a feature within individual products. In master data management, AI agents surface and resolve data quality issues before they reach downstream processes. In private funds administration, they accelerate reporting and reduce manual intervention in fee and expense workflows. In reconciliation, they manage exceptions at scale. The result is operational improvement across the full investment lifecycle, front office, middle office, back office, and investor relations , rather than isolated gains in a single function.
