Private equity used to be about spreadsheets, relationships and a good eye for numbers. Today it’s about data pipelines, machine learning and the software you use to run a portfolio. That doesn’t mean PE has become a tech company overnight — it means funds that treat technology as a tool (not a buzzword) can source smarter deals, squeeze more margin out of operations, and shorten the clock to a clean exit.
Here’s a practical signal: in a recent Pictet survey, more than 40% of private equity general partners said they already have an AI strategy for their firm, and around two‑thirds reported that a meaningful share of their portfolio companies are testing or piloting AI. More than 60% even reported some revenue uplift at portfolio companies due to AI work. Source: Pictet Group — AI adoption in private equity: insights and challenges.
Why mention that up front? Because the practical wins are straightforward and measurable: better deal origination from intent and web signals, faster and safer integrations when data and identity are ready, and operational plays — pricing, retention, maintenance — that move EBITDA in months, not years. This article walks through the modern PE playbook: what “private equity technology” actually means today, where value comes from, and a 90‑day path that funds can use to deliver real results.
No hype. No vendor deck language. Just the concrete levers funds use, from securing IP and customer data to deploying AI co‑pilots across sales, support and finance — and how those moves change valuation math at exit. If you want to know how to find better deals, lift margins, and make a portfolio company more saleable by the next fundraise or exit, read on.
What “private equity technology” means now
Technology private equity vs tech‑enabled PE: where value actually comes from
“Private equity technology” today is a dual thesis: on one side are pure technology bets — software and SaaS companies where the product IS the business — and on the other are traditional PE plays that use software, AI and data as a repeatable value‑creation engine across portfolio companies.
The pure‑tech side (software PE, growth buyouts) buys recurring revenue, high NRR/retention and product‑led economics that scale with relatively little incremental SG&A. These businesses trade on multiples tied to ARR growth, retention and unit economics (think Rule of 40, ARR expansion and gross margins).
The tech‑enabled side buys durable businesses in industries such as services, manufacturing, healthcare or logistics and layers in technology — better CRM/RevOps, dynamic pricing, automation, digital supply‑chain — to expand deal size, volume and operating leverage. That approach is less about multiple arbitrage and more about moving EBITDA through operational modernization and repeatable playbooks (roll‑ups, platform + tuck‑ins, and sector plays backed by reusable tech).
For background reading on why software targets attract dedicated PE strategies and how tech‑enabled roll‑ups differ in execution, see Bain’s work on private equity and software and commentary on tech‑enabled vertical roll‑ups (Bain, Tidemark Capital).
Why PE moved hard into tech: recurring revenue, cloud, and AI economics
Three economic realities explain the shift: recurring revenue reduces revenue volatility and raises EV/ARR premiums; cloud delivery turns fixed costs into elastic, scalable spend; and AI compresses marginal costs while improving retention and upsell. Together these forces make growth more predictable and margin‑expanding — exactly what PE underwriters prize.
Practically, SaaS-style income converts uncertain one‑off sales into predictable cashflow, enabling higher leverage capacity and cleaner modeling of exit scenarios. Cloud platforms reduce capital intensity and speed rollouts across geographies. AI and automation multiply the impact of headcount through higher funnel efficiency, personalized retention, dynamic pricing and faster product iteration — all levers that lift EBITDA without linear increases in SG&A.
As one concise valuation driver put it: “IP can be licensed, franchised, or sold separately, providing additional revenue streams that enhance overall enterprise value.” “Deal Preparation Technologies to Enhance Valuation of New Portfolio Companies — D-LAB research”
For further reading on why SaaS and recurring models remain favored by PE, see market summaries from Cherry Bekaert and SaaS‑focused valuation research (Cherry Bekaert, SaaS Capital).
Market context 2024–2025: slower IPOs, private credit tailwinds, valuation reset
The macro backdrop matters: public exits (IPOs) stayed muted through 2023–early‑2024, which pushed more capital and hold‑time into private markets. That longer hold horizon makes operational value creation — not just multiple arbitrage — essential.
At the same time, private credit has grown as an alternative to bank debt, creating more flexible financing for buyouts and partial exits; but multiples and deal volume have reset from 2021 highs, forcing firms to justify higher entry prices with demonstrable tech‑led uplift plans. By 2024 many funds were focusing on carve‑outs and operational plays that can be de‑risked and scaled before the market fully re‑opens to large IPO windows (Bain PE Outlook 2025, PwC mid‑2025 M&A trends).
Put another way: the market now underwrites technology in two ways — premium multiples for software with clean recurring economics, and step‑change EBITDA lifts where technology is applied systematically across a traditional business. The funds that win are those that can rapidly translate tech investments into measurable retention, deal economics and margin expansion while defending value with IP and data controls.
Next, we’ll walk through the actions PE teams take in the first 90 days to lock in those gains — from hardening IP and data to standing up analytics for customer retention — so value sticks through to exit.
Year‑one playbook: protect intellectual property and data to defend valuation
Cybersecurity frameworks that buyers trust: ISO 27002, SOC 2, and NIST 2.0
Buyers increasingly underwrite security posture at the term‑sheet stage. Start with a framework choice that matches the target’s customers and industry: ISO 27002 for enterprise ISMS discipline, SOC 2 for service providers selling to U.S. commercial buyers, and NIST 2.0 where government or defence supply chains matter.
“Capabilities Required: Encryption, access controls, risk assessment tools, security monitoring, backup and recovery systems, secure asset management.” Deal Preparation Technologies to Enhance Valuation of New Portfolio Companies — D-LAB research
“Capabilities Required: Change Management Systems, audit trails and logging, access logging and review, data loss prevention, incident response automation.” Deal Preparation Technologies to Enhance Valuation of New Portfolio Companies — D-LAB research
Adopt the framework that shortens buyer diligence and builds trust quickly — then treat the audit/certification as an operating milestone, not a one‑off checkbox.
How IP protection and data resilience expand multiples and win contracts
IP and data are valuation multipliers. Intellectual property creates optionality (licensing, franchising, or separate monetization) and a defensible revenue stream; customer data and security posture reduce exit risk and contract friction with strategic acquirers and large customers.
“Intellectual Property (IP) represents the innovative edge that differentiates a company from its competitors, and as such, it is one of the biggest factors contributing to a companys valuation.” Deal Preparation Technologies to Enhance Valuation of New Portfolio Companies — D-LAB research
“IP can be licensed, franchised, or sold separately, providing additional revenue streams that enhance overall enterprise value.” Deal Preparation Technologies to Enhance Valuation of New Portfolio Companies — D-LAB research
Quantify the business case: buyers factor in breach cost, regulatory fines and the revenue upside of retained customers. As evidence, the reports note that the average cost of a data breach in 2023 was $4.24M and GDPR fines can reach 4% of revenue — concrete numbers that underwriters use when stress‑testing offers. Deal Preparation Technologies to Enhance Valuation of New Portfolio Companies — D-LAB research
Strong security has demonstrated commercial benefit: controls aligned to NIST helped a vendor win a large DoD contract despite being more expensive on price, showing how compliance and resilience can directly translate into contract wins. “Company By Light won a $59.4M DoD contract even though a competitor was $3M cheaper. This is largely attributed to By Lights implementation of NIST framework (Alison Furneaux).” Portfolio Company Exit Preparation Technologies to Enhance Valuation — D-LAB research
CISO tooling checklist: encryption, identity, logging, incident response, backups
In year one the CISO (or acting security lead) should focus on a compact toolkit that buys the most risk reduction per dollar and converts to buyer confidence:
— Identity & access: MFA, role‑based access controls, single sign‑on and periodic access reviews.
— Encryption: encryption at rest and in transit for customer and IP data; secrets management for keys and credentials.
— Observability & logging: centralized audit trails, SIEM or log‑aggregation, alerting and forensic retention policies.
— Endpoint & network protection: EDR/XDR, secure remote access and patch management.
— Data resilience: regular, tested backups and disaster recovery runbooks; immutable backups for ransomware scenarios.
— Incident response & governance: an IR plan with tabletop exercises, defined escalation to leadership, and a vendor risk management / third‑party security assessment process.
These controls map directly to the framework capabilities buyers expect: monitoring and backups for ISO; audit trails and incident automation for SOC 2; and asset management, continuous monitoring and patching for NIST 2.0. “Capabilities Required: Encryption, access controls, risk assessment tools, security monitoring, backup and recovery systems, secure asset management.” Deal Preparation Technologies to Enhance Valuation of New Portfolio Companies — D-LAB research
Practical first‑90‑day milestones: complete an asset & data inventory; run a light gap assessment against your chosen framework; enable MFA, endpoint protection, and centralized logging; and implement a minimally viable IR runbook plus daily backup verification. These moves reduce near‑term breach risk and create defensible evidence to show prospective buyers and auditors.
Securing IP and customer data is foundational: it defends current valuation and unlocks the ability to deploy AI and revenue‑growth playbooks without creating new risk — the next step is using those capabilities to keep and expand the customers you fought to win.
Keep the customers you fought to win: AI‑driven retention and market share
Customer sentiment analytics and personalization to grow LTV and reduce churn
Retention is the highest‑return lever in private‑equity value creation: small changes in churn compound into outsized EV/EBITDA gains. Start by unifying product usage, support and CRM signals into a single customer view and apply ML to segment customers by predicted lifetime value, churn risk and expansion propensity.
Use cases to deploy in year one: automated churn scoring, root‑cause segmentation (why customers leave), and playbook generation that maps actions to likely outcomes (discount, targeted feature, success outreach). Pair these with A/B tests for personalization at scale (emails, in‑product offers, landing pages) so improvements are measurable and repeatable.
“Up to 25% increase in market share (Vorecol).” Deal Preparation Technologies to Enhance Valuation of New Portfolio Companies — D-LAB research
“20% revenue increase by acting on customer feedback (Vorecol).” Deal Preparation Technologies to Enhance Valuation of New Portfolio Companies — D-LAB research
“71% of brands reported improved customer loyalty by implementing personalization, 5% increase in customer retention leads to 25-95% increase in profits (Deloitte), (Netish Sharma).” Deal Preparation Technologies to Enhance Valuation of New Portfolio Companies — D-LAB research
GenAI call‑center assistants: faster answers, lower churn, higher cross‑sell
Contact centers are where retention meets revenue. GenAI agents augment human reps by surfacing context, scripting tailored responses, recommending next‑best actions and auto‑generating post‑call summaries. The result: faster resolution, better conversion on upsell prompts and fewer escalations.
Operational wins to expect quickly: reduced average handle time, higher CSAT, and automated detection of expansion signals that route warm opportunities to sales.
“20-25% increase in Customer Satisfaction (CSAT) (CHCG).” Deal Preparation Technologies to Enhance Valuation of New Portfolio Companies — D-LAB research
“30% reduction in customer churn (CHCG).” Deal Preparation Technologies to Enhance Valuation of New Portfolio Companies — D-LAB research
“15% boost in upselling & cross-selling (CHCG).” Deal Preparation Technologies to Enhance Valuation of New Portfolio Companies — D-LAB research
Practical tip: run a pilot that blends real‑time agent assist with supervised generative responses; measure escalation rates and revenue per call before scaling. Integrate call outcomes into the customer record so CSMs and account teams can act on signals immediately.
Customer success platforms that raise NRR: signals, playbooks, renewal automation
Customer success platforms are the glue between analytics and action. Feed them product telemetry, usage trends and sentiment scores so they can score health, prioritize outreach and automate renewal workflows.
Key features to implement: automated health scoring, playbook templates triggered by specific signals, renewal and expansion workflows with staged nudges, and executive dashboards that show NRR, at‑risk ARR and expansion runway.
“10% increase in Net Revenue Retention (NRR) (Gainsight).” Deal Preparation Technologies to Enhance Valuation of New Portfolio Companies — D-LAB research
“8.1% increase in renewal bookings by adopting account prioritizer (Suvendu Jena).” Deal Preparation Technologies to Enhance Valuation of New Portfolio Companies — D-LAB research
Combine CSM automation with revenue ops: route high‑propensity expansion accounts to outbound sellers, schedule executive business reviews for strategic logos, and instrument contract terms so renewals become low friction. Measured improvements in NRR and expansion are among the clearest valuation uplifts an acquirer will underwrite.
Across all three levers — sentiment analytics, GenAI agents and CS platforms — the objective is the same: convert noisy customer signals into reproducible playbooks that increase LTV, shrink churn and create visible, auditable evidence for buyers. With those retention engines humming, you can pivot to widening the top of funnel with automated sourcing and intent‑driven outreach.
Fill the top of funnel with automation, not headcount
AI sales agents: data enrichment, qualification, outreach, and scheduling
Top‑of‑funnel growth in PE portfolio companies is less about hiring dozens of SDRs and more about automating predictable tasks so sellers focus on high‑value conversations. Start by building an automated lead engine that enriches profiles, scores propensity, sequences personalized outreach and books meetings — then measure conversion uplift and time saved.
“Outcome: 40-50% reduction in manual sales tasks. 30% time savings by automating CRM interaction (IJRPR). 50% increase in revenue, 40% reduction in sales cycle time (Letticia Adimoha).” Deal Preparation Technologies to Enhance Valuation of New Portfolio Companies — D-LAB research
How to pilot: (1) instrument and centralize first‑party lead signals; (2) deploy a lightweight enrichment layer + propensity model; (3) run an automated cadence for low‑touch accounts and hand off warm leads to reps; (4) measure revenue per rep and cycle length before scaling.
Buyer intent data: find in‑market accounts before they raise a hand
Intent signals shift marketing from spray‑and‑pray to targeted, timely outreach. Combine third‑party intent (topic consumption, compare/search behaviour) with internal engagement so the outbound engine prioritizes accounts that are actively researching solutions.
“Outcome: 32% increase in close rates (Alexandre Depres). 27% decrease in sales cycle length.” Deal Preparation Technologies to Enhance Valuation of New Portfolio Companies — D-LAB research
Practical steps: integrate an intent provider into your CRM, map intent topics to ICP segments, create templated playbooks for each intent bucket and automate initial outreach. A short A/B test (intent‑led vs. baseline) will prove ROI rapidly.
Benchmark the funnel: conversion rates, CAC, and payback you should hit
Benchmarks keep automation honest. Targets vary by business model, but use these guardrails when sizing the program: aim for an LTV:CAC of ~3:1 and push CAC payback down to 12 months for high‑growth SaaS; broader B2B businesses should track payback against sector norms and capital constraints.
For reference, a widely used rule of thumb for LTV:CAC is ~3:1 (Stripe), while recent SaaS surveys show CAC payback periods drifting longer (median reports around 14–18 months in 2024–2025), so target compression via automation where possible (First Page Sage, Drivetrain).
Key KPIs to track weekly: marketing qualified lead (MQL) velocity, SDR conversion to opportunity, opportunity close rate, CAC (by cohort), CAC payback months, and sales cycle length. Use cohort dashboards so you can see whether automation reduces CAC and shortens payback as intended.
Put simply: automate enrichment, qualification and timing; use intent to hunt active buyers; and measure the economics (LTV:CAC, payback) before you add headcount. Once the funnel is operating at target efficiency, the next priority is extracting more value from each opportunity — increasing average deal size and margin through pricing, packaging and recommendations.
Lift average deal size with dynamic pricing and recommendations
Dynamic pricing engines that balance margin and win‑rate in real time
Dynamic pricing engines use real‑time demand signals, inventory position, customer segment and competitor pricing to recommend the optimal price for each transaction. The core idea is simple: raise price where willingness to pay is high, protect margin where competitiveness is low, and automate the trade‑offs that humans cannot manage at scale.
Implementation checklist: ingest transactions + product signals, estimate price elasticity by cohort, build a constrained optimizer that enforces floor prices and discount policies, run controlled A/B experiments, and instrument P&L attribution so you measure margin vs win‑rate tradeoffs. Start with a narrow product set or channel and ramp as you prove lift.
“Up to 30% increase in average order value (Terry Tolentino).” Deal Preparation Technologies to Enhance Valuation of New Portfolio Companies — D-LAB research
“2-5x profit gains.” Deal Preparation Technologies to Enhance Valuation of New Portfolio Companies — D-LAB research
Recommendation engines that drive upsell/cross‑sell at the point of decision
Recommendation systems marry behavioral signals (what users view, search and buy) with transaction history and product affinity to surface the right add‑ons at the exact moment of decision. Deployed as in‑product suggestions, cart recommendations or sales‑agent prompts, they convert a passive browse into incremental order value.
Best practices: combine collaborative filtering with business rules (margin thresholds, inventory constraints), measure incremental lift with holdouts, and deploy both reactive (cart/pop‑up) and proactive (email/product feed) recommendations. Feed recommendation outcomes back into your models so the engine learns which suggestions actually convert and which dilute margin.
“30% increase in cross-sell conversion rates for B2C, and 25% for B2B (Affine), (Steve Eveleigh).” Deal Preparation Technologies to Enhance Valuation of New Portfolio Companies — D-LAB research
“10-15% revenue increase through improved upselling, cross-selling and customer loyalty.” Deal Preparation Technologies to Enhance Valuation of New Portfolio Companies — D-LAB research
Packaging and bundling tests: fast experiments, measurable AOV gains
Packing, bundling and anchoring tests are low‑risk experiments that typically pay back quickly. Run controlled tests for bundle types (feature bundles, product + service, multi‑unit discounts), price anchors and decoy offers. Track average order value (AOV), attachment rate and margin per bundle to avoid dilutive discounts.
Operational approach: design 3–5 hypothesized bundles, implement as temporally limited experiments (or region/channel split), use conversion and margin dashboards to pick winners, then operationalize through pricing engines and the recommendation layer so bundles are suggested at the right moment.
“Up to 30% increase in average order value (Terry Tolentino).” Deal Preparation Technologies to Enhance Valuation of New Portfolio Companies — D-LAB research
Across all three levers, the technical lift is only half the story — governance and measurement matter. Lock in safe pricing floors, maintain seller playbooks for exceptions, and require every experiment to report incremental revenue, margin and impact on conversion. When done well these interventions increase AOV, improve profitability and create repeatable pricing playbooks that acquirers can underwrite at exit.
With pricing and recommendation engines improving deal economics, the next set of opportunities is operational — using AI to cut downtime, optimize inventory and make factories more profitable so margin gains compound across the business.
Make the factory a profit center: predictive maintenance and lights‑out ops
Automated asset maintenance and digital twins to cut downtime and costs
Predictive maintenance turns repairs from reactive cost centers into scheduled, optimized interventions that preserve throughput and margin. Start by instrumenting critical assets (vibration, temperature, runtime), establish a centralized telemetry pipeline, then deploy anomaly detection and prescriptive models that recommend when to service, not just what failed.
“Technology: AI performs predictive maintenance, prescriptive maintenance, condition monitoring, and automated root cause analysis. Digital twin of assets may also be implemented to test maintenance strategies before deploying them.” Portfolio Company Exit Preparation Technologies to Enhance Valuation — D-LAB research
“30% improvement in operational efficiency, 40% reduction in maintenance costs (Mahesh Lalwani). 50% reduction in unplanned machine downtime, 20-30% increase in machine lifetime.” Portfolio Company Exit Preparation Technologies to Enhance Valuation — D-LAB research
Practical pilot: run a 90‑day program on one line—deploy sensors, feed data to a cloud model, set up alerts and a small prescriptive team. Measure avoided downtime, mean time between failures (MTBF) and maintenance spend. Successful pilots typically scale horizontally across lines, multiplying EBITDA impact while deferring capital spend.
Inventory and supply‑chain optimization to reduce disruptions and working capital
AI‑driven supply‑chain planning replaces rigid reorder points with probabilistic forecasts that account for lead‑time variability, demand seasonality and supplier risk. The result: fewer stockouts, lower safety stock, and improved cash conversion cycles.
“Outcome: 40% reduction in supply chain disruptions, 25% reduction in supply chain costs (Fredrik Filipsson). 20% reduction in inventory costs, 30% reduction in product obsolesce (Carl Torrence).” Portfolio Company Exit Preparation Technologies to Enhance Valuation — D-LAB research
Quick wins include shortening forecast windows for fast movers, implementing multi‑ echelon inventory optimization for complex SKUs, and automating replenishment triggers. Link these models to procurement workflows so savings flow straight to working capital and gross margin improvements.
Lights‑out factories: where robotics + AI deliver throughput and quality
Lights‑out (or lights‑low) factories combine advanced robotics, closed‑loop process controls and scheduling optimization to run 24/7 with minimal human intervention. They are capital‑intensive to build but can deliver exceptional quality and utilization once tuned.
“Technology: Fully automated production facilities that operate without human intervention. Factories leverage robotics, sensors, AI and other Industry 4.0 technologies to manage production 24/7.” Portfolio Company Exit Preparation Technologies to Enhance Valuation — D-LAB research
“Outcome: 99.99% quality rate (Nucleus AI). 30% increase in productivity output (Emmet Cole).” Portfolio Company Exit Preparation Technologies to Enhance Valuation — D-LAB research
Deploy incrementally: automate repeatable cells first, instrument OEE dashboards, then integrate predictive maintenance and digital twin simulations to optimize throughput. Measure yield, scrap reduction and labour redeployment—those line‑item margin gains feed straight into EBITDA.
Across all three levers, two governance rules matter: (1) instrument outcomes tightly so every model recommendation has an ROI tag (downtime minutes saved, spare parts avoided, margin impact), and (2) build integration back into operations—alerts must drive work orders, procurement changes and scheduling decisions, not just dashboards. When factories start producing predictable, margin‑rich output, product teams can iterate faster and competitive intelligence becomes actionable—feeding the next phase of growth.
Product that sells itself: customer‑centric R&D and competitive intelligence
Design optimization tools: fix issues in CAD, not on the line
Shift R&D upstream: use simulation, topology optimisation and generative design to find mechanical, thermal and manufacturability issues inside CAD before a single prototype is built. That reduces rework, cut tooling costs and shortens time‑to‑market — all direct drivers of margin and exit multiple.
Practical playbook: embed automated DFM checks into the CI pipeline for new designs; run batch simulations on constrained parameter sets; generate and score variant designs by cost, cycle time and defect risk; and push winners into pilot production with automated test plans. Start with the handful of SKUs that drive >70% of margin impact and scale from there.
“Skilful improvements at the design stage are 10x more effective than at the manufacturing stage- David Anderson (LMC Industries).” Portfolio Company Exit Preparation Technologies to Enhance Valuation — D-LAB research
AI competitive intelligence to place the right roadmap bets
Replace gut instinct with data: ingest product releases, patents, pricing pages, job postings, review sites and social signals to map competitor trajectories and feature gaps. Use NLP to surface product themes that are gaining momentum and to estimate commercial impact of adjacent features.
How funds should use it: run monthly scoring that ranks roadmap candidates by market demand signal, implementation complexity and margin upside; prioritize features with high conversion or retention lift and low cannibalization risk. Integrate CI outputs with product OKRs so investments target measurable KPIs (adoption rate, NPS lift, incremental ARR).
“50% reduction in time-to-market by adopting AI into R&D (PWC).” Portfolio Company Exit Preparation Technologies to Enhance Valuation — D-LAB research
Digital Product Passports (DPPs) to boost trust, compliance, and pricing power
DPPs attach provenance, compliance and sustainability metadata to each SKU (often backed by immutable records). For B2B buyers and ESG‑sensitive end markets, DPPs reduce procurement friction, enable premium pricing and lower regulatory risk at exit.
Rollout strategy: pilot DPPs on high‑value products or those in regulated channels; expose machine‑readable proofs in the commerce and after‑sales flows; and package DPP data into sales collateral to shorten enterprise procurement cycles. Monitor win rate uplift and any reduction in contract negotiation time as primary KPIs.
“71% of consumers believe DPPs will lead to more trust in the brand (FasionUnited).” Portfolio Company Exit Preparation Technologies to Enhance Valuation — D-LAB research
Measurement and governance tie the three levers together: mandate experiments with holdouts for every major roadmap decision, instrument adoption and retention impact, and capture R&D ROI into the monthly operating review. When product roadmap decisions consistently show measurable revenue, margin or retention uplift, buyers will pay a premium for the repeatable process — which is exactly the profile private equity firms want to present at exit.
With product and engineering now driving measurable commercial lift, the next step for operating teams is to quantify where portfolio companies still leave value on the table so those gaps can be closed systematically.
See where your portfolio is leaving value on the table
Before you double down on add‑ons or new hires, run a portfolio‑level leakage diagnostic. The goal is to turn anecdote into action: identify the highest‑impact gaps (pricing leaks, churned ARR, production inefficiency, warranty costs, under‑monetised IP), prioritise fixes that move EBITDA fast, and prove lift with short pilots.
Start with three simple steps: (1) assemble an evidence layer — product usage, CRM activity, contract terms, financial cohorts and operations telemetry in one place; (2) run a value‑leak scorecard that maps lost margin by cause (discounting, missed upsell, churn, downtime, excess inventory, service costs); (3) execute 30–90 day experiments (pricing changes, intent‑led outreach, predictive maintenance) and measure incremental margin and payback.
Which metrics expose the most rot? Track NRR and GRR, logo churn and expansion ARR; CAC, CAC payback and close rates; AOV and discounting frequency; EBITDA margin, revenue per FTE and cost per unit; plus operational KPIs — unplanned downtime, OEE, inventory days and obsolete SKUs. Cohort and product‑level views turn team anecdotes into objective priorities.
There’s low‑hanging fruit everywhere: pricing ops and recommendation engines lift AOV, customer success automation reduces churn, and digital twins plus predictive maintenance cut downtime and warranty spend. As Diligize summarised, “Revenue growth: 50% revenue increase from AI Sales Agents, 10-15% increase in revenue from product recommendation engine, 20% revenue increase from acting on customer feedback, 30% reduction in customer churn, 25-30% boos in upselling & cross‑selling, 32% improvement in close rates, 25% market share increase, 30% increase in average order value, up to 25% increase in revenue from dynamic pricing.” Deal Preparation Technologies to Enhance Valuation of New Portfolio Companies — D-LAB research
Practical governance: require every proposed intervention to include a baseline, a control cohort and three KPIs (revenue lift, margin impact, payback months). Use a central dashboard for the fund to compare experiments across portfolio companies and to redeploy capital to the highest‑return plays.
Finally, capture the process as a reusable playbook: what data sources mattered, how propensity models were trained, which playbooks moved NRR fastest, and the checklist to scale winners. When you can show repeatable, measurable uplifts across companies, you change the conversation with LPs and buyers — and make exits faster and richer.
With the biggest leaks identified and a pipeline of proven pilots, the next step is to automate execution at scale — deploying AI agents, co‑pilots and task automation so teams can sustain improvements without proportional headcount growth.
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Automate the work: AI agents, co‑pilots, and assistants across the org
Where to deploy first for fast ROI: sales, support, finance, IT
Deploy where repetitive tasks create drag on growth and where outcomes are measurable. Priorities that reliably pay back quickly:
– Sales: automate CRM updates, prospect enrichment, meeting scheduling and low‑touch outreach so reps spend more time closing. – Support: conversational assistants and summarizers reduce handle time, increase CSAT and free senior agents for complex cases. – Finance: invoice processing, reconciliations and monthly close workflows are high‑volume, low‑risk wins for RPA + LLM co‑pilots. – IT & engineering: co‑pilots that surface code suggestions, automate routine admin and triage incidents accelerate delivery and reduce backlog.
“52% reduction time to solve the most complex customer support tickets (John Kell). 40-50% reduction in manual sales tasks. 30% time savings by automating CRM interaction (IJRPR). 70% reduction in fraud (Bob Mashouf).” Deal Preparation Technologies to Enhance Valuation of New Portfolio Companies — D-LAB research
Data readiness and change management that keep projects on track
AI tools amplify existing data problems. Before scaling agents and co‑pilots, secure three foundations: (1) data plumbing — reliable pipelines from CRM, support, ERP and product telemetry; (2) canonical models — unified customer, product and sku dimensions so assistants speak the same language as users; (3) guardrails — access controls, audit trails and human‑in‑the‑loop escalation for high‑risk decisions.
Design the rollout as a change program: pick one high‑value use case, run a 30–60 day pilot, embed the bot into the user’s workflow (not as a separate tool), collect qualitative feedback and measure quantitative KPIs. Create a lightweight Centre of Excellence to capture playbooks, prompt templates and escalation rules so wins are repeatable across portfolio companies.
“Workflow Automation: AI agents, co-pilots, and assistants reduce manual tasks (4050%), deliver 112457% ROI, scale data processing (300x), reduce research screening time (-10x), and improve employee efficiency (+55%).” Portfolio Company Exit Preparation Technologies to Enhance Valuation — D-LAB research
KPIs to track: time saved, error rate, employee satisfaction, SLA adherence
Use a small set of leading and lagging KPIs for each pilot so impact is visible and comparable across companies: time saved per user, tasks automated per month, error or rollback rate, SLA adherence, customer satisfaction (CSAT/NPS), and employee satisfaction. Financial KPIs should map back to margin impact: labour cost saved, reduction in churn or increased revenue attributable to faster responses or better data, and CAC or RTO improvements where relevant.
Operationalise measurement with control cohorts and A/B tests — for example, route 10% of tickets to baseline agents and 90% to the assistant to estimate incremental resolution speed and CSAT. Require every experiment to publish payback months and a scaling threshold before broader rollout.
Start small, secure a few measurable wins and then convert those into templates — shared connectors, tested prompts, escalation matrices and governance. Once repeatable modules exist, you can automate execution at scale, freeing headcount for growth work rather than routine administration.
With the organisation running on reliable automation and co‑pilots, the natural next move is to lock in the upstream data flows that feed sourcing and diligence so insights are available earlier and more reliably across the fund.
Data‑led deal origination and diligence
Sourcing signals: web/news processing, third‑party intent, and outbound orchestration
Move from reactive to proactive sourcing by instrumenting signals across public and proprietary channels. Key sources: company mentions and exec moves in news and filings, product and pricing changes on web pages, job postings, review sites, and third‑party intent providers. Combine these with first‑party telemetry (customer activity, usage spikes) and firmographics to build a rolling universe of in‑market targets.
Practical recipe: centralise ingestion (news APIs, web crawlers, intent feeds), normalise entities (company name, domain, sector), score signals with short‑term (intent, funding, hiring) and long‑term (market fit, defensibility) models, and surface high‑propensity targets into an outbound cadence that ties to SDR/BDR playbooks. Prioritise channels that can be operationalised into measurable outreach within 7–14 days.
Tech and AI diligence checklist: code, data assets, model risk, and security posture
Technical diligence should be checklist‑driven and risk‑scored so decisions are objective and repeatable. Cover four pillars: code & engineering, data & models, security & compliance, and third‑party dependencies.
Core checks to include: repository health (tests, branch strategy, CI/CD), architecture diagrams and scalability limits, data inventory and lineage (PII, retention policies), model governance (training data provenance, performance baselines, monitoring plan), dependency and license review, incident history, existing certifications (SOC 2, ISO), and an initial threat surface assessment (exposed endpoints, authentication, secrets management).
Use risk buckets (business‑critical, high, medium, low) and map remediation actions to purchase terms (escrows, holdbacks, integration milestones). Require sellers to deliver runnable sandboxes, wireframe observability (metrics/logs), and a data export package so valuation assumptions on revenue, churn and unit economics can be stress‑tested.
Day‑0 integration planning: identity, data pipelines, observability, and controls
Due diligence should produce a Day‑0 integration plan not a wish list. Identify the minimum technical prerequisites to begin operational improvement in month one: SSO and identity mapping, canonical customer/product schemas, ingest pipelines for telemetry and finance, and baseline observability (error rates, latency, business KPIs).
Checklist for Day‑0 readiness: mapped identities and access policies, prioritized data feeds and owners, ETL/ELT patterns and schema contracts, a monitoring playbook (dashboards + alert thresholds), backup/restore proof points, and an agreed escalation path for critical incidents. Lock in quick wins (customer analytics, a billing reconciliation job, or a simple predictive churn model) as the first deliverables so the integration demonstrates ROI inside 60–90 days.
Measure success with conversion metrics (signal→meeting→LOI), diligence velocity (hours per deal stage), and integration speed (time-to-first-data and time-to-first‑impact). With signal pipelines, repeatable diligence templates and Day‑0 playbooks in place, funds can scale originations while reducing execution risk — and then codify those repeatable operating plays so portfolio teams convert insights into cashflow improvements at speed.
What top technology private equity firms do differently
Sector focus and reusable playbooks (pricing, cybersecurity, RevOps)
Top tech PE firms double down on a narrow set of sectors and build reusable operational playbooks that compress learning across deals. That means a single pricing engine, SOC 2/NIST remediation checklist or RevOps stack can be template‑deployed across 6–12 portfolio companies rather than rebuilt each time — driving both speed and margin improvement.
“High-ROI AI Areas:Automated asset maintenance, factory process optimization, AI agents for sales and customer service, and customer sentiment analytics.” Deal Preparation Technologies to Enhance Valuation of New Portfolio Companies — D-LAB research
Practically, firms codify the target state for 3–5 cross‑cutting capabilities (pricing ops, customer success automation, security baseline) and create implementation bundles: tested vendors, wiring diagrams, KPIs and a one‑page ROI model. That turns operating plans from bespoke projects into repeatable rollouts that buyers can underwrite at exit.
Operating partner models that turn plans into EBITDA
Rather than hand off playbooks and hope for the best, leading funds deploy operating partners — ex‑CROs, CTOs, RevOps chiefs — who embed for 3–9 months to guarantee execution. These partners translate playbooks into sprint plans, unblock data or sales frictions, and coach management on adoption.
Execution metrics are simple and finance‑driven: time to incremental ARR, margin uplift, and payback months on implementation costs. This accountable model converts strategic intent into measurable EBITDA before the next board review.
Pattern wins: security‑led trust uplift, pricing ops, and NRR expansion
Top funds bet on patterns that repeatedly move multiples. Three examples recur in successful exits: security posture as a commercial differentiator (winning large contracts), pricing operations that lift AOV and margins, and targeted NRR programs that turn churn into predictable expansion revenue.
“Exit Potential:Up to 50% increased revenue and 25% increase in market share by integrating AI in sales and marketing practices (Letticia Adimoha), (Vorecol).” Deal Preparation Technologies to Enhance Valuation of New Portfolio Companies — D-LAB research
They measure pattern wins with the valuation levers acquirers underwrite: NRR/GRR, CAC payback, revenue per FTE and EBITDA margin. When the same playbook (e.g., dynamic pricing + recommendation engine + CSM automation) yields consistent improvements across companies, it moves a fund’s entire portfolio multiple.
Operational discipline — focused sector plays, living playbooks, embedded operating partners and a small set of repeatable pattern wins — separates top tech PE firms. Once these elements are in place, funds can confidently project which interventions will compound value and plan capital allocation accordingly, setting the stage for a forward‑looking view of risks and opportunity that informs the market outlook to follow.
2025 outlook for private equity technology
AI adoption and monetization: where returns compound, where they stall
2025 will be the year many funds move from pilot to scale. Expect two paths: companies that treat AI as a productivity multiplier (internal co‑pilots, workflow automation) will compound returns quickly; companies that treat AI as a bolt‑on feature without data, monitoring and customer‑facing hooks will see limited upside.
As a blunt data point from value‑creation work, “Workflow Automation: AI agents, co-pilots, and assistants reduce manual tasks (4050%), deliver 112457% ROI, scale data processing (300x), reduce research screening time (-10x), and improve employee efficiency (+55%).” Deal Preparation Technologies to Enhance Valuation of New Portfolio Companies — D-LAB research
Where monetization accelerates: (1) AI embedded into revenue workflows (pricing engines, recommendation systems, intent‑driven outreach) that lift AOV/close rates; (2) product features that unlock new paid tiers or usage monetization; (3) clear provenance of model performance and monitoring so buyers underwrite future revenue. Independent research supports large upside from enterprise copilots and generative tools (see Forrester/Microsoft TEI studies and McKinsey on GenAI economic potential) (Forrester TEI on Copilot; McKinsey, 2023).
Cyber risk, regulation, and insurance: costs, coverage, and board questions
Regulation and insurance will shape where capital flows. The EU AI Act and related standards are introducing compliance steps that fund teams must budget for (registration, documentation, impact assessments) — see the EU Commission timeline and guidance (EU AI Act overview).
On insurance: cyber underwriting is stabilising but underwriting scrutiny is higher; insurers expect demonstrable controls, incident history, and remediation plans before offering meaningful coverage (Marsh cyber market updates; Munich Re outlook). Expect higher diligence on frameworks — ISO 27002, SOC 2 and NIST remain the practical checklist for buyers and insurers.
As Diligize puts it, “IP & Data Protection: ISO 27002, SOC 2, and NIST frameworks defend against value-eroding breaches, derisking investments; compliance readiness boosts buyer trust.” Deal Preparation Technologies to Enhance Valuation of New Portfolio Companies — D-LAB research
Operationally, funds should treat compliance and cyber as value‑creation levers: remediation often unlocks large contracts, reduces potential deal escrows and speeds exits — but it also requires early, budgeted investment and a clear evidence trail to satisfy insurers and strategic bidders.
Deal size and volume trends: where multiples are likely to expand
Macro headwinds in 2024 shifted PE activity, but 2025 shows selective recovery: deal counts are rebounding in several regions and sectors that exhibit recurring revenue and clear digital moats (see Bain & McKinsey 2025 PE outlooks). Buyers are paying premiums for predictable, tech‑enabled revenue streams and demonstrable retention metrics.
Which portfolio interventions correlate with multiple expansion? Pattern wins include security‑led trust uplift (winning larger enterprise contracts), sophisticated pricing ops (dynamic pricing + recommendation engines lift AOV and margins), and targeted NRR programmes that convert churn into expansion revenue. Those playbooks repeatedly show measurable uplifts that acquirers can underwrite.
Repeatable evidence matters more than a headline technology: capture baseline cohorts, show impact on NRR/GRR, CAC payback and revenue per FTE, and present those metrics in exit materials. As a reminder of the upside, Diligize highlights that “Exit Potential:Up to 50% increased revenue and 25% increase in market share by integrating AI in sales and marketing practices (Letticia Adimoha), (Vorecol).” Deal Preparation Technologies to Enhance Valuation of New Portfolio Companies — D-LAB research
Bottom line for 2025: allocate capital to projects with measurable commercial levers (pricing, retention, security) and the governance to scale them. With those levers proven, funds can both increase deal size at exit and raise conversion rates for originations — which brings us to the metrics buyers actually underwrite next.
The valuation scorecard buyers actually underwrite
Growth and retention: NRR, GRR, logo churn, expansion revenue
Buyers start with recurring revenue health. Net Revenue Retention (NRR) and Gross Revenue Retention (GRR) tell the story of predictability and expansion: high NRR signals that the installed base will compound revenue without proportional sales investment, and low logo churn reduces execution risk in an exit process.
When you present a company to a buyer, show cohort‑level NRR/GRR, the drivers of expansion (upsell, cross‑sell, pricing), and the pipeline of at‑risk accounts with remediation plans. Use product usage, ARR cohorts and churn root‑cause analysis to prove your read.
Small, provable uplifts matter. As one D‑Lab finding notes, “10% increase in Net Revenue Retention (NRR) (Gainsight).” Deal Preparation Technologies to Enhance Valuation of New Portfolio Companies — D-LAB research
Go‑to‑market efficiency: CAC, CAC payback, close rates, AOV
Buyers underwrite unit economics. They want to see disciplined acquisition: CAC that scales down with channel mix, CAC payback measured in months, durable close rates and rising average order value (AOV). These metrics convert growth narratives into cashflow expectations.
Present a clear funnel model: marketing spend → qualified pipeline → conversion → average deal. Show historical CAC payback, LTV:CAC, and experiments that materially moved these levers (intent data pilots, AI enrichment, recommendation engines, or dynamic pricing tests).
Benchmarks from value‑creation work show meaningful uplifts from targeted interventions — for example, a “32% increase in close rates (Alexandre Depres).” Deal Preparation Technologies to Enhance Valuation of New Portfolio Companies — D-LAB research
Operating leverage: EBITDA margin, cash conversion, revenue per FTE
Valuation is ultimately a multiple on cashflow. Buyers look at operating leverage: can revenue scale without linear SG&A increases? Useful metrics are EBITDA margin trend, cash conversion cycle, revenue per FTE and unit contribution margins. They also want to see which operational levers are repeatable (automation, pricing, product‑led upsell, manufacturing efficiencies).
Include a simple waterfall in diligence materials that reconciles revenue growth to EBITDA expansion — show where headcount, gross margin and working capital move as revenue scales. Provide sensitivity tables (best/likely/worst) anchored to KPIs buyers trust: NRR, CAC payback, and revenue per FTE.
Quantify expected payoff from operational plays. D‑Lab summarises the upside of tech‑led interventions: “Exit Potential:Up to 50% increased revenue and 25% increase in market share by integrating AI in sales and marketing practices (Letticia Adimoha), (Vorecol).” Deal Preparation Technologies to Enhance Valuation of New Portfolio Companies — D-LAB research
How to package the scorecard for buyers: (1) a one‑page summary of the three valuation levers with current vs. target numbers; (2) cohort and sensitivity schedules that prove assumptions; (3) an evidence folder — dashboards, pilot results, and the remediation plan with owners and timelines. That converts promise into underwritable signals and lets buyers map multiples to achievable outcomes.
With a clear, metric‑driven scorecard in hand you can move rapidly from analysis to action: baseline the KPIs, run focused 30–90 day pilots against the highest‑impact levers, and prepare the playbook you will present to potential acquirers.
A 90‑day implementation path that moves the needle
Week 1–4: secure IP/data and stand up customer analytics
Kick off with the existential checks: IP inventory, access controls, backups, and a SOC‑2/NIST gap map. While the security team locks down identity and logging, the analytics team should stand up a lightweight customer data stack (cloud warehouse, ETL, canonical customer/product schemas) and a first‑page dashboard that answers: ARR by cohort, churn by cohort, top 20 customers by revenue, and usage signals that predict churn.
Deliverables: asset/IP register, prioritized remediation backlog (with owners), a populated analytics schema, and a green dashboard with the 5 baseline KPIs for revenue & retention.
Keep the implementation pragmatic and measurable — start with the smallest instrumentation that produces reliable cohorts and move from there. As D‑Lab recommends, apply analytics early: “Apply customer analytics to increase revenue and market share of portfolio companies.” Deal Preparation Technologies to Enhance Valuation of New Portfolio Companies — D-LAB research
Week 5–8: pilot one revenue workflow (pricing or intent‑led outbound)
Pick one revenue leaver with clear measurement and short feedback loops. Two high‑probability pilots:
– Dynamic pricing pilot: activate price recommendations on a narrow set of SKUs/accounts, run controlled A/B pricing tests, track AOV, win rate and margin impact. – Intent‑led outbound: connect buyer intent feed → SDR cadence → CRM automation and measure signal→meeting→opportunity conversion.
Structure the pilot as an experiment: hypothesis, control cohort, test cohort, success metric (e.g., +AOV, -sales cycle, +close rate), and a clear stop/go decision at day 28. Instrument everything so attribution is clean: which signal produced the meeting; which price lift produced the margin.
Use conservative, high‑impact automation to free sellers: D‑Lab notes the productivity returns from sales automation, for example “40-50% reduction in manual sales tasks. 30% time savings by automating CRM interaction (IJRPR).” Deal Preparation Technologies to Enhance Valuation of New Portfolio Companies — D-LAB research
Week 9–12: scale wins, set quarterly targets, and report to IC
If the pilot hits its target, move from experiment to scale: document the playbook (data flows, prompts, vendor connectors, runbooks), replicate across adjacent products/regions, and automate roll‑out tasks (onboarding scripts, dashboards, training modules). Establish quarterly targets tied to valuation levers: NRR lift, CAC payback improvement, AOV or margin uplift, and time‑to‑value for each scaled use case.
Packaging for the investment committee: a one‑page scorecard (baseline vs target KPIs), cohort evidence, sensitivity tables (best/likely/worst), and an owners/timeline matrix. That packet converts operational wins into an underwritable narrative for buyers and sets the roadmap for the next 6–18 months.
Reminder and motivation: automation compounds when pipelines, pilots and secure data are in place — D‑Lab calls this out as a core source of value in exits: “Workflow Automation: AI agents, co‑pilots, and assistants reduce manual tasks (4050%), deliver 112457% ROI, scale data processing (300x), reduce research screening time (-10x), and improve employee efficiency (+55%).” Deal Preparation Technologies to Enhance Valuation of New Portfolio Companies — D-LAB research
When the 90 days finish, you should have secured IP and data, a working customer analytics engine, a validated revenue play with measured uplift, and a repeatable playbook that an operating partner or portfolio team can deploy at scale — the ideal setup for turning those improvements into durable EBITDA gains and a clear story for prospective buyers.