Agentic AI in Private Equity Value Creation

An AI agent is a piece of AI with reasoning capability, designed for a specific task. Unlike standard automation — which can only handle predefined cases — agents can read unstructured inputs, make decisions within a set confidence level, and execute actions inside your existing systems without a human involved at every step. In a PE context, this means automating the back-office operations inside portfolio companies that eat into margins and slow down value creation during the holding period.

It depends on process complexity, but a focused first deployment — a single process like AR collections or order processing — typically runs over several months from scoping to live system. The AR collections agent we deployed across two markets was subsequently taken and rolled out by the client across nine additional markets independently.

Order processing, accounts receivable collections and dunning, invoice matching, working capital monitoring, sales pipeline and CRM administration, and KPI reporting consolidation across portcos. Broadly: any high-volume, repetitive process that doesn't require strategic judgement but currently requires people to do it.

We start with a diagnostic — mapping where time, cash, and headcount are leaking across the portco's operations. From there we identify the highest-value starting point, scope a pilot tightly, and build it. The goal of a first engagement is a working deployment with measurable results — not a strategy document.

We look for three things: high volume, repetitive execution, and clear inputs and outputs. Processes that currently rely on people reading documents, entering data, chasing responses, or reconciling records are strong candidates. Processes that require relationship management or complex strategic judgment are not — at least not yet.

We measure in the metrics that matter to funds: EBITDA impact, cash released from working capital, FTE hours redirected, and revenue uplift. For example, our AR collections agent reduced late invoices from 25% to 7% in one market, releasing €2.7M in cash. Every deployment is set up with a dashboard that tracks performance from day one.

Yes — and this is by design. Every implementation is built to be documented and reused. The AR collections agent we deployed in two markets was taken by the client and rolled out across eleven markets without rebuilding. At the fund level, what works at one portco becomes a ready playbook for the next.

We use a multi-layer setup: an orchestration system hands work to the AI component, which acts as the reasoning brain — reading inputs, structuring data, making decisions. Where needed, an RPA bot handles system navigation. Everything connects to the portco's existing ERP and workflow infrastructure. A confidence scoring threshold determines what gets processed automatically and what gets flagged for human review.

All deployments run on enterprise-tier AI infrastructure — not consumer subscriptions. That means your data is logically separated, never used for model training, and governed by data processing agreements, SOC 2, and ISO standards. For portcos with stricter compliance requirements, self-hosted deployment is also an option.

Every agent is set up with a confidence threshold — typically 95%. If the agent is 95% or more confident in its output, it processes automatically. Below that, it flags the case for a human to review and push through. This means your team isn't removed from the process — they're freed from the routine volume and focused on the exceptions that actually need judgement