Most private equity firms already use AI to accelerate financial diligence. QoE reports turn around faster, ledger anomalies are flagged automatically, and contracts are reviewed at scale. AI-powered financial diligence is efficient and reliable — but it still only validates what’s been reported. Useful, yes. Differentiating? Not anymore.

What’s emerging now is a more powerful AI use case for PE firms: operational intelligence. This new breed of agents can estimate margin growth from automation, revenue lift from optimized sales and marketing funnels, and expected integration friction from disjointed systems and data architecture. For the first time, PE firms can reliably size the risks and opportunities that shape bolt-on speed, organic growth, and exit readiness before making a buying decision.

And once a deal closes, value is realized immediately. Agentic workflows overhaul manual processes. Intelligent sales and marketing engines boost GTM conversion. Legacy systems and data architecture are migrated by agents working around the clock. Together operational AI systems deliver a new playbook for private equity: sharper intelligence before the deal, and faster value capture after.

Automating Margin Expansion

Every investor talks about “back-office efficiency.” The difference with operational AI is that it can show exactly where the waste is during diligence, and how much EBITDA can be freed up after deal close.

Process mining agents can scan across ERP, CRM, HR, procurement, billing, and collaboration tools where employees spend most of their time — email, chat, calls, docs, decks, and tasks. They trace the digital footprints of how work actually happens and surface bottlenecks like invoices rekeyed across systems, AP and supplier approvals stuck for weeks, manual payroll adjustments and inventory backfills, poor support resolution times, and so much more.

Each workflow is costed in time, headcount, and resulting dollars from the ERP, giving PE deal teams a precise estimate of the margin that can be unlocked through automation. Then reusable AI agents plug into existing workflows post-close without requiring a full-scale tech rebuild. Manual processes are accelerated within weeks, and headcount is redirected to higher-value work or exited from the company. By the time the first board deck is presented, margin expansion is no longer a projection — it’s already underway.

Deploying Intelligent Growth Engines

Organic topline growth has always been harder to pin down than cost savings. AI changes that by quantifying exactly where organic lift is available.

Algorithmic attribution during diligence shows which marketing touchpoints actually convert, which campaigns are fatigued, and where budgets are wasted. Drip nurture programs are measured against re-engagement and churn reduction, while CRM data exposes cycle times, stage drop-offs, win rates, and discounting patterns. Sales and marketing performance is then benchmarked against industry standards, with dollars assigned to the revenue growth available from improvements at each stage of the funnel.

Post-close, AI engines generate personas from historical conversion data, build tailored omnichannel campaigns for each persona, and measure performance post-launch to self-improve over time. Dynamic lead scoring, nurture flows, and next-best-action prompts sharpen sales execution. Marketing spend is reallocated to highest ROI channels, and low value activities are sunset to control GTM focus. Within months customer acquisition improves, churn drops, and revenue lifts in line with diligence projections.

Streamlining Infrastructure & Systems

The hidden drag on deal close often isn’t in the numbers — it’s in the systems. A deal with strong growing EBITDA can easily fall apart if its data and infrastructure are a patchwork of bolt-ons, manual workarounds, and incompatible tools. Upstream buyers don’t want to inherit a platform where integration will take years and reporting can’t be trusted.

Operational AI makes those risks visible early and fixes them fast. Infrastructure agents inventory every tool in play and map how they connect. Schema-matching models normalize data between bolt-ons, resolving duplication and mismatches that compromise reporting. Process mining tools compare workflows across business units, surfacing where standardization is required to scale. Even permission structures and data flows can be stress-tested to flag security gaps or compliance risks that would stall diligence.

The result is proactive rationalization. Redundant systems are consolidated, vendors retired, and reporting schemas standardized — often in months instead of years. Because agents run continuously, portfolios don’t just get cleaned up before a sale; they stay clean as new bolt-ons are added. The payoff is twofold: faster integration of acquisitions, and platforms that present not just strong financials but operational readiness at exit — commanding higher multiples and reducing the risk of deals falling through.

The New Era of Value Creation

Private equity is on the edge of a structural shift. Operational AI will compress holding periods, transform bolt-on integration from a multi-year exercise into a matter of months, and make value creation far more predictable. This changes the industry’s center of gravity: instead of competing on access to deals or financial structuring, firms will compete on their ability to re-engineer portco operations at scale — using AI as the lever to build better businesses, faster.

Active helps investors capture that shift. We partner with PE firms to implement operational AI directly into portfolio companies, delivering systems that automate core processes, sharpen organic growth, and streamline integrations. The result isn’t just bigger exits — it’s a repeatable system for creating enterprise value that compounds across every deal.