Private equity today is less about spreadsheets and more about speed, coordination, and practical do-ables that actually move the needle. Deals close fast, markets shift faster, and the premium buyers pay is increasingly tied to predictable growth and repeatable operational improvements — not just a promise on a slide. That’s why a focused, short-term value creation plan matters: it turns broad strategy into specific actions you can measure and repeat.
What this 90-day playbook does for you
This is a hands-on playbook for the first 90 days after investment. Think of it as three 30-day sprints with a clear monitoring stack, a tight KPI set that connects to MOIC/DPI/IRR, and owner-driven playbooks for commercial, product, and operations teams. The goal is simple: reduce uncertainty, create predictable revenue and margin uplifts, and build the evidence buyers want at exit.
How we approach value creation
- Rapid diagnosis: quickly surface the top 3–5 value levers — retention, deal volume, deal size, or margin — and measure baseline performance.
- Operational cadence: run 13-week sprints with weekly KPIs, 30/60/90 check-ins, and clear accountability across the deal team, operating partners, and management.
- Monitoring and governance: build a lightweight data pipeline and LP-grade reporting so insights become actions, not opinions.
- Tech-enabled lifts: use focused automation, AI co-pilots, and process fixes where they have the highest ROI — retention engines, pricing engines, predictive maintenance, and sales automation.
- Exit thinking from day one: align targets (NRR, CAC payback, margin, security attestations) to make the company “sale-ready” long before the sale process starts.
Over the coming sections we’ll break each sprint down into concrete playbooks, templates, and quick wins you can pull into your first 90 days. No fluff — just the checks, the meetings, and the measurable moves that consistently lift valuation and reduce execution risk. Ready to turn intent into outcomes? Keep going.
What private equity portfolio management means now
Scope: monitoring, value creation, risk, and exits
Modern portfolio management in private equity is broader than tracking financials. It combines continuous monitoring with hands-on value creation: operational improvements, commercial acceleration, technology adoption and governance that together increase optionality for an exit. Risk oversight sits alongside growth initiatives — security, compliance and capital allocation are managed not as separate checklists but as value levers that preserve and amplify enterprise worth.
That means teams must balance near-term liquidity and performance with medium-term strategic moves that lift multiples. Monitoring delivers the signals; value creation converts those signals into predictable improvements in margins, growth and defensibility. All activity should be explicitly framed around how it affects attractiveness to future buyers or public markets.
Cadence: 13-week cash, KPI trees, operator playbooks
Cadence is the muscle that turns strategy into results. A tight operating rhythm — typically rolling short-term cash forecasts, a hierarchical KPI tree and repeatable operator playbooks — keeps the portfolio responsive and focused. Short-cycle cash and performance reviews expose issues early so interventions are surgical rather than reactive.
KPI trees translate high-level investment targets into the day-to-day metrics teams can influence: leading indicators that predict revenue and margin movement, and lagging metrics that validate progress. Operator playbooks capture repeatable, proven interventions so improvements can be scaled across similar businesses in the portfolio.
Accountability across deal team, operating partners, and management
Clear, enforced accountability is the glue of execution. Deal teams own thesis alignment and capital deployment; operating partners drive the blueprint for operational change; company management executes the day-to-day. Successful programs define responsibilities, decision rights and escalation paths up front so that progress is visible and ownership is unambiguous.
Communication routines matter: shared dashboards, weekly cadences, and agreed escalation triggers create a single source of truth and shorten the feedback loop between board, fund and management. When each role has measurable commitments tied to the investment thesis, interventions are faster and outcomes become more predictable.
With scope, cadence and accountability established, the natural next step is to translate those principles into the data, tools and governance that enable repeatable monitoring and rapid, high-conviction interventions across the portfolio.
Build the portfolio monitoring stack and governance
Data pipeline: collect, normalize, analyze, act
Start with a single-source-of-truth data pipeline that ingests finance, CRM, product/usage, support, and ops telemetry. Collect via ELT/streaming connectors, normalize into common schemas, and enrich with master data (customers, products, contracts). The goal is low-friction access for analysts and operators: standardized datasets, data contracts, and a catalog so teams can trust metrics and move quickly from insight to intervention.
Design the pipeline for action: automated alerts for threshold breaches, onboarded playbooks that map signals to owners, and runbooks that trigger pre-approved remediation or growth experiments. Low-latency dashboards and a lightweight API layer let operating partners and management act without waiting for bespoke reports.
KPI set that predicts MOIC, DPI, and IRR
Translate valuation targets into a hierarchy of KPIs: top-line drivers (NRR, new ARR, average deal size), efficiency levers (gross margin, CAC payback, sales productivity), and liquidity signals (13-week cash, burn vs. plan). Combine leading indicators (pipeline coverage, conversion rates, product usage metrics) with lagging validation (DPI, MOIC, IRR) so the board can see whether current interventions move the needle on exit outcomes.
Operationalize KPI ownership: each KPI must have a named owner, a data source, a cadence, and an associated playbook. Use standardized definitions across the portfolio so benchmarking is apples-to-apples and roll-ups to fund-level metrics are automated.
Cyber and IP protection using ISO 27002, SOC 2, NIST 2.0
“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. Protecting these assets is key to safeguarding the value of an investment.” Portfolio Company Exit Preparation Technologies to Enhance Valuation — D-LAB research
Security and IP controls should be treated as core components of the monitoring stack. Benchmarks to require early include an ISMS mapped to ISO 27002, SOC 2 controls for service and processing integrity, and a NIST-based approach for continuous cyber risk management. Implement practical tooling—asset inventories, identity & access management, endpoint detection, logging and immutable audit trails—so attestations and evidence are available on demand.
“Average cost of a data breach in 2023 was $4.24M (Rebecca Harper).” Portfolio Company Exit Preparation Technologies to Enhance Valuation — D-LAB research
“Europes GDPR regulatory fines can cost businesses up to 4% of their annual revenue.” Portfolio Company Exit Preparation Technologies to Enhance Valuation — D-LAB research
Those outcomes make clear why compliance readiness is not just risk avoidance: certifications and documented controls materially de-risk investments and strengthen buyer trust during exit processes.
LP-grade reporting and benchmarking cadence
Deliver LP-grade outputs by automating fund- and portfolio-level roll-ups: standardized P&L and cash waterfall templates, MOIC/DPI/IRR reconciliations, and regular benchmark packs versus sector peers. Establish a reporting cadence (weekly cash, monthly operating KPIs, quarterly board decks) and publish via a secure portal with versioned diligence rooms and audit trails.
Benchmarking should surface both relative performance and the presence/absence of value-creation capabilities (e.g., repeatable go-to-market playbooks, security attestations, and product-engagement leading indicators) so LPs and buyers can see not just performance but the durability of the value proposition.
With a data pipeline, predictive KPI set, hardened security controls and LP-ready reporting in place, the team can convert signals into 90-day interventions that materially lift valuation — and do so at scale across the portfolio.
90-day value creation sprints that lift valuation
Retention engines: AI customer success and GenAI support
“GenAI-driven retention tools move valuation levers: GenAI call-centre assistants can raise CSAT by ~20–25%, reduce churn by ~30% and boost upsell/cross-sell by ~15%; AI customer-success platforms can increase Net Revenue Retention by ~10%, all of which strengthens predictability and exit multiples.” Portfolio Company Exit Preparation Technologies to Enhance Valuation — D-LAB research
Sprint objective: stop leakage and make recurring revenue predictable. In weeks 0–2, baseline churn, NRR and CSAT and map key touchpoints. Weeks 3–6 build a minimum viable GenAI assistant or CS platform integration on a prioritized segment (high churn or high lifetime value). Weeks 7–10 run A/B tests on proactive outreach, context-aware recommendations and automated renewal workflows. Weeks 11–13 scale the winning workflows, lock in playbooks and hand off monitoring to ops.
Critical success steps: instrument health scores in the product, connect signals to CRM and CS tools, define SLA for automated interventions, and set clear KPIs (churn delta, CSAT lift, NRR uplift, upsell conversion). Use a rapid ROI gate: if incremental NRR or churn improvement exceeds the pre-set threshold at day 60, scale; otherwise iterate the model or target cohort.
Deal volume: AI sales agents and buyer-intent data
Short sprints here focus on pipeline velocity and conversion. Start by wiring buyer-intent feeds and an AI sales agent to a pilot segment. Week 1–2: capture intent signals and profile high-opportunity accounts. Week 3–6: deploy AI agents to qualify leads, automate outreach and schedule demos. Week 7–10: measure conversion lift, sales cycle compression and rep time saved. Week 11–13: integrate successful flows into CRM and standardize lead-scoring rules across reps.
Typical outcomes to chase: higher close rates, shorter sales cycles, and increased rep productivity. Guardrails: monitor data quality, ensure human review of qualification thresholds, and track attribution so you can tie pipeline improvement to valuation drivers.
Deal size: recommendation engines and dynamic pricing
90-day pilots for deal size should be surgical: pick a product line or customer cohort with sufficient volume and margin. Weeks 0–2: prepare data (transaction history, product affinities, price sensitivity). Weeks 3–6: run a recommendation engine or dynamic pricing experiment on a controlled traffic slice. Weeks 7–10: measure AOV, conversion rate and margin impact. Weeks 11–13: codify pricing rules, update commerce flows, and roll out to broader segments where ROI is clear.
Monitor A/B uplift on AOV and margin per order, and set conservative rollback rules (e.g., conversion drop or margin erosion triggers automatic halt). Recommendation engines and pricing controls often compound retention improvements by making offers more relevant and margin-accretive.
What good looks like: lift in NRR, AOV, win rates
Define explicit targets before the sprint: example ranges backed by prior pilots include single-digit to mid-double-digit lifts in NRR, double-digit increases in AOV, and measurable improvements in win rates and conversion. Translate those targets to valuation-relevant outcomes: shorter CAC payback, higher recurring revenue, and stronger run-rate predictability.
Operational checklist for every sprint: 1) clear hypothesis and KPI; 2) owner and cross-functional team; 3) instrumentation and data contracts; 4) short experiment runway (4–7 weeks) with defined gates; 5) playbook and handoff if successful. This repeatable cadence lets funds convert tactical wins into durable valuation improvements across multiple companies.
After proving top-line and retention levers in short cycles, the natural next move is to shift attention to operational and margin levers that compress costs and protect uptime—turning revenue gains into sustainable EBITDA expansion and a stronger exit story.
Thank you for reading Diligize’s blog!
Are you looking for strategic advise?
Subscribe to our newsletter!
Scale margins with automation and industrial AI
Predictive maintenance and digital twins for uptime
“Industrial AI drives step-change operational impact: predictive maintenance can improve operational efficiency by ~30%, cut unplanned downtime by ~50% and extend machine lifetime 20–30%; digital twins have been shown to lift profit margins by ~41–54% while reducing factory planning time ~25%.” Portfolio Company Exit Preparation Technologies to Enhance Valuation — D-LAB research
Turn the quote into a program: identify the small set of critical assets that drive the most lost production or cost, instrument them, and run a focused pilot. Collect sensor and maintenance data, integrate with the CMMS, and run a parallel predictive model that proposes specific prescriptive actions (part replacement, adjusted schedules, or process tweaks). Use a digital twin to test interventions before deployment so real-world risk is minimised and ramp time is faster.
Operational metrics to govern pilots: uptime, mean time to repair (MTTR), spare-part availability, maintenance cost per unit of output and the variance between planned and unplanned downtime. Make the pilot owner accountable for a 90-day experiment with defined gates and a scale decision at the end of the window.
Process optimization, additive manufacturing, lights-out operations
Start process optimization with value-stream mapping and quick-win automation: identify repetitive manual steps, bottlenecks, and highest-cost error points. Deploy targeted automation (RPA or embedded controls) where ROI is visible within one quarter, then iterate toward broader system optimizations that remove variability and raise yield.
For parts and tooling, evaluate additive manufacturing for low-volume or complex components that previously required expensive tooling or long lead times. A staged approach—proof of concept, qualification, then production—reduces risk while shortening time-to-benefit.
Lights-out or highly automated operations are a longer-horizon lift but can be staged. Ensure control systems, deterministic scheduling, remote diagnostics and spare-part strategies are matured in phases so uptime and quality gains compound without disrupting current output.
AI co-pilots and agents to cut SG&A and speed workflows
Deploy AI co-pilots in finance, procurement, sales ops and IT to remove predictable, repetitive work and speed decision cycles. Typical first pilots are invoice processing, contract triage, forecasting augmentation, and intelligent work routing. Keep humans in the loop for approvals, exceptions and model feedback—this preserves control while capturing productivity.
Measure success by time-to-complete for key processes, full-time-equivalent (FTE) effort saved, error rates and cycle-time compression. Pair automation pilots with change management so teams adopt new workflows and the run-rate savings become sustainable rather than one-off.
Implementation governance is essential across all these levers: data quality gates, model validation and rollback rules, security and IP controls, and a clear owner who can sign off on scaling. Run 90-day experiments with an agreed metric, a roll/kill decision at day 60, and a documented playbook for scaling winners across similar plants or business units.
When margin expansion programs are repeatable and instrumented, you can translate operational improvements into a cleaner, more defensible EBITDA story for buyers — and then shift focus to packaging those improvements for exit diligence and valuation uplift.
Exit readiness from day one
Targets: NRR, CAC payback, gross margin, security attestations
“Technology-driven value creation is a key exit signal: integrating AI across sales and marketing has produced up to ~50% revenue uplift and ~25% market-share gains in case studies — outcomes that directly support NRR, CAC payback and margin targets prized by buyers.” Portfolio Company Exit Preparation Technologies to Enhance Valuation — D-LAB research
Make exit signals explicit from day one by translating buyer preferences into measurable targets. Typical targets should include Net Revenue Retention (NRR) and its drivers, CAC payback and payback cadence, sustainable gross margin improvements, and evidence of security and compliance programs. Each target needs a baseline, a stretch goal, and a short-term milestone that can be achieved within a 90-day sprint so the board can track progress and validate the investment thesis.
Operationalize targets with a simple scorecard: owner, data source, cadence, current vs. target, and the playbook that will move the metric. This makes every improvement traceable to valuation drivers and creates clear evidence for buyers that growth and margin improvements are repeatable, not one-off.
Diligence room, compliance, and audit trails that convince buyers
Build the diligence narrative continuously, not only at exit. Maintain an organized, versioned virtual data room with financial reconciliations, legal docs, customer contracts, GTM metrics, product roadmaps, IP registers and cybersecurity evidence. Keep audit trails and change logs so any data presented to a buyer can be traced to origin and validated rapidly.
Prioritize compliance and attestations that matter to buyers in your sector (security certifications, contractual SLAs, privacy documentation). Document remediation actions and their impact so diligence turns from a discovery exercise into a confirmation of de-risking work already completed. The easier it is for an acquirer to validate claims, the lower the perceived execution risk and the higher the exit multiple.
Exit paths, buyer mapping, and dry-run process rehearsal
Map likely exit routes early: strategic acquirers, roll-up consolidators, financial sponsors or IPO. For each buyer type, articulate the thesis they will pay for (market share, recurring revenue, cost synergies, proprietary IP) and tailor the evidence pack accordingly. Prioritise buyer lists and run targeted outreach dry-runs to test market receptivity and refine positioning.
Conduct regular dry-run rehearsals of the sales process and diligence Q&A with management and the deal team. Practice responding to the toughest questions on growth cadence, retention, unit economics and security posture; refine the data room and one-pagers based on those rehearsals so the real process is efficient and credible.
When exit signals are measured, documented and rehearsed from day one, they become durable assets in the buyer conversation. With the exit story packaged and validated, the next step is to shift attention to operational levers that expand margins and convert revenue gains into sustainable EBITDA improvements, making the company even more attractive to prospective buyers.