Companies and investors are pouring money into robotic process automation (RPA) because it promises faster processes, lower costs, and fewer mistakes. But those benefits aren’t automatic. Poorly vetted automations can stall, create security gaps, or simply never scale — turning a promising program into a maintenance headache and a valuation drag.
RPA due diligence is the simple but disciplined work of verifying three things before you write a check or sign off on a rollout: does the automation create real, measurable value; what risks does it introduce; and can it scale reliably across people, processes, and systems? This article walks that line between opportunity and exposure so you can make smarter, faster decisions.
We use a seven-lens approach that investors and CIOs can apply quickly: strategic fit and process economics; pipeline quality and exception rates; automation maturity and orchestration; financials and bot utilization; compliance and data protection; tech stack and vendor risk; and change velocity (test coverage, release cadence, time-to-repair). For each lens you’ll get the practical checks that reveal whether an automation is an asset or a liability.
Read on for clear, non‑jargon guidance: concise verification questions, the tech and security signals that matter, governance proof points that de‑risk scale, and a short post‑close 100‑day plan you can use to stabilize and accelerate the top automations. If you’re preparing for investment, acquisition, or a large-scale rollout, this introduction will set the compass — the rest of the piece gives you the map and the checklist.
The RPA due diligence lens: seven areas investors and CIOs must verify
Strategic fit and business case by process family
Confirm which process families (e.g., order-to-cash, claims, onboarding) are targeted and why: request the process inventory, ownership map, and a one‑page business case per family. Verify alignment to corporate goals (cost reduction, cycle-time, compliance, customer experience) and that process owners sponsor the work. Check whether the case uses consistent baselines (cost per transaction, throughput, error rates) and that benefits are tied to measurable KPIs with agreed timelines and owners for realization.
Pipeline quality: standardization, volumes, exception rates, rework
Assess candidate-readiness by asking for process-level metrics: transaction volumes, variation (exceptions/branching), exception-handling time, and rework rates. Prioritize high-volume, low-variation processes with predictable inputs. Validate that process standards, canonical inputs, and SLAs exist; where they don’t, flag remediation effort. Request sample datasets, process diagrams, and exception logs to validate the automation pipeline’s throughput assumptions.
Automation maturity: attended vs. unattended, orchestration, citizen dev
Map current automation types and governance: number of attended bots, unattended bots, orchestrator usage, schedulers, and any citizen‑developer activity. Verify whether there’s a Centre of Excellence or equivalent, coding/review standards, and runbooks for handoffs. Look for orchestration patterns (end-to-end flows vs. siloed scripts) and for evidence of lifecycle discipline—release processes, dependency management, and clear escalation paths from citizen-created automations into centrally supported assets.
Financials: TCO, bot utilization, ROI and CAC payback effect
Request a total-cost-of-ownership model covering licensing, infrastructure (infra ops and hosting), development hours, maintenance, and support. Compare that to measured bot utilization (active time vs. idle time), exception-handling cost, and annualized maintenance effort. Check ROI assumptions (benefit realization cadence and sustainability) and how automation affects unit economics such as cost-per-transaction and sales/marketing CAC—especially where automations touch customer acquisition or service operations.
Compliance readiness: data classification and PII/PCI/PHI handling
Verify data flows end-to-end: what data the bots access, where it is stored, masking/encryption practices, and retention policies. Ask for data classification, access control lists, and evidence of least-privilege service accounts. Confirm logging and audit trails exist for data access and decision points, and check exception workflows when sensitive data appears in free‑text fields. If regulated data is in scope, ensure policy owners have approved the automation design and remediation plans exist for gaps.
Tech stack and vendor risk: API-first vs. screen scraping, cloud/on‑prem mix
Inventory integration approaches: percentage of automations using APIs or connectors versus UI/tokens or screen-scraping. API-first designs reduce fragility; UI-scrape approaches increase maintenance and vendor-lock risk. Map infrastructure: vendor SaaS, on‑prem orchestration, hybrid hosting, third‑party connectors, and any bespoke adapters. Review license terms, upgrade cadence impacts, and contingency plans for vendor changes or deprecation.
Change velocity: test coverage, release frequency, time to repair
Evaluate the release discipline: frequency of bot updates, automated test coverage (unit, integration, regression), staging/production separation, and rollback procedures. Measure mean time to detect and mean time to repair for bot failures, and inspect monitoring/alerting dashboards. Prefer teams that use CI/CD practices for automations, have automated smoke tests, and maintain clear SLAs for incident response and recovery.
Collecting the artifacts above—process inventories, exception logs, cost models, runbooks, test suites, and integration inventories—lets you score risk versus value and build a remediation or scale plan. Once you’ve validated these operational and commercial lenses, it’s time to drill into the underlying technology, security posture and intellectual‑property controls to confirm the automation foundation can safely scale and survive a change in ownership.
Tech, security, and IP checks for RPA platforms
Architecture resilience: failover, versioning, disaster recovery RTO/RPO
Request an architecture diagram that shows orchestrator clustering, bot runners, database/storage, and network segmentation. Verify documented RTO/RPO targets and recent DR test results. Check version-control for bot code and artifacts (who can push to prod), backup frequency for configuration and state, and whether there are health-checks and automated failover paths for critical bots. Red flags: single-host orchestrator, manual restore procedures, no version tags for releases.
Integration approach: API priority, event-driven design, legacy adapters
Inventory integrations by type (API/connector, file/queue, UI-scrape). Prefer API- or event-driven flows for stability and observability; flag heavy reliance on screen‑scraping or fragile selectors. Confirm an adapter catalogue (what’s bespoke vs. vendor-provided), documented change-impact analysis for target applications, and contingency plans for upstream API or UI changes. Ask for SLAs or runbook notes where legacy adapters are unavoidable.
Observability: logs, traceability, auditability, SLA dashboards
Require centralized logging and correlation (trace IDs across systems), retention policies for audit logs, and evidence of integration with SIEM or monitoring stacks. Verify per-automation KPIs (success rate, exceptions, run-time, queue length) exposed in dashboards and linked to alerts. Confirm that human approvals and decision points are captured in immutable audit trails to support forensic review and compliance queries.
Security mapped to ISO 27002, SOC 2, and NIST 2.0 controls
“Cybersecurity frameworks materially de-risk automation: the average cost of a data breach in 2023 was $4.24M and GDPR fines can reach up to 4% of annual revenue — implementing ISO 27002, SOC 2 or NIST 2.0 therefore both reduces breach exposure and increases buyer trust. In practice, NIST compliance has been decisive in wins (e.g., By Light secured a $59.4M DoD contract, attributed to its NIST implementation).” Portfolio Company Exit Preparation Technologies to Enhance Valuation — D-LAB research
Ask for certification evidence, SOC 2 reports, or a mapped control matrix showing how platform controls map to ISO/NIST/SOC 2. Confirm schedule and results of external penetration tests and internal vulnerability scans, patch cadence for orchestrator and runner software, identity and access management records (SAML/SSO, MFA enforcement), and third‑party risk assessments for any managed services.
Secrets and data protection: vaulting, encryption, access reviews
Verify use of a secrets manager (no credentials in plain scripts), encryption-at-rest and in-transit, service account separation, and short-lived credentials where possible. Require regular access-certification cycles (who has runtime/control plane rights) and logs of secret access. For sensitive fields processed by bots, confirm masking, tokenization or redaction and that backups do not contain cleartext PII.
IP and licenses: bot/script ownership, vendor terms, open-source use
Review contracts to confirm ownership of bot assets and source (including citizen-developer contributions). Check vendor license terms for the orchestrator and connectors (transferability, escrow, termination impact). Run a software composition analysis for open-source libraries inside bot code and confirm license compatibility. Require a remediation plan for any third‑party license or export-control constraints that could impede a sale or transition.
GenAI-in-the-loop: prompt/data governance, model risk, PII redaction
If GenAI is used in workflows, confirm data-provenance controls (what data is sent to models), prompt templates under access control, evaluation procedures for hallucination and bias, and model-usage logging. Ensure PII is stripped or pseudonymized before external model calls and that prompts are stored for audit. Validate a defined owner for model governance and a rollback plan if model behavior degrades.
These technical, security and IP checks produce a clear scorecard: platform resilience, integration hygiene, observability strength, security-framework coverage, secret controls, clear IP rights, and GenAI governance. Once you’ve closed these gaps, the final step is to validate how the organisation will run, govern and scale automation in practice — the people, processes and policies that make a platform durable and value-accretive.
Operating model and governance proof points that de-risk RPA at scale
CoE structure: roles, RACI, funding, federated vs. centralized
Ask for an org chart and CoE charter that clearly names accountable roles (business owner, automation product manager, platform owner, security lead, ops lead). Confirm a RACI for build/run/change activities and evidence of funding lines (central budget, showback/chargeback, or funded by LOBs). Verify whether governance is centralized, federated, or hybrid and that escalation paths and budget authorities are documented.
Intake and scoring: value/risk scoring, compliance gates, sign-offs
Require the intake form and scoring rubric used to approve automations. The rubric should combine value (volume, cycle-time, cost) and risk (data sensitivity, exceptions, upstream volatility) and produce a prioritization score. Check for mandatory compliance and security gates, documented sign-off owners, and a backlog with clear status for approved, in-scope, and deferred candidates.
SDLC: design standards, reusable components, peer review, automated testing
Review the SDLC artifacts: coding standards, naming conventions, reusable component libraries, and UI/connector abstraction patterns. Confirm a peer‑review policy for bot code and design documents, and that code is stored in version control with branching rules. Ask for automated test artifacts (unit/functional/regression), defect metrics, and a definition of “ready for production” that includes test pass criteria.
Deployment and operations: orchestration, scheduling, blue-green releases
Inspect deployment pipelines and runbooks: is there a CI/CD pipeline for bots, staging environment, and an approval workflow for production releases? Look for orchestration and scheduler configurations, support for rolling or blue/green deployments, and feature-flag or canary mechanisms to limit blast radius. Confirm handover checklists between build and ops teams.
Exception/incident handling: thresholds, playbooks, root-cause cycles
Request incident playbooks and SLA definitions for detection, escalation and resolution. Verify alerting thresholds, on-call rosters, and the cadence of post-incident reviews with documented root‑cause analysis and action tracking. Ensure that exception classification maps to remediation routes (fix, retrain, human-in-loop) and that lessons feed back into design standards.
Performance and utilization: definition, measurement, and targets
Confirm documented metric definitions (e.g., bot utilization = productive run time / availability window, exception rate = failed transactions / total runs). Review dashboards and report samples that show utilization, success rate, mean time to repair, and business KPIs tied to automations. Check target-setting processes and governance for rebalancing bots or retiring low-value automations.
Collecting these proof points — charters, intake rubrics, SDLC artifacts, deployment pipelines, incident records and metric dashboards — lets investors or CIOs move from anecdote to evidence. With governance validated, you can then model how automation and intelligence will translate into durable value across revenue, cost and customer metrics.
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Valuation upside with RPA + AI: retention, deal volume, and deal size
Retention plays: AI sentiment, success platforms, call‑center assistants
Start with the customer journey: use sentiment analytics to surface at‑risk accounts, deploy AI‑driven customer success platforms to prioritize interventions, and add GenAI call‑center assistants to shorten handle times and surface cross‑sell opportunities. Typical outcomes to validate in diligence: improved CSAT (often +20–25%), material churn reductions (benchmarks show ~30% reductions in customer churn in strong pilots) and incremental upsell performance from assisted agents (mid‑teens percentage uplift).
Pipeline growth: AI sales agents, buyer intent signals, hyper‑personalized content
AI sales agents that qualify, enrich and sequence outreach can expand pipeline quality and conversion. Combine first‑party CRM + intent signals and hyper‑personalized content to increase qualified lead volume and conversion. Evidence to request: increases in SQLs, conversion rate lifts, and sales cycle compression — strong cases show both higher pipeline throughput and shorter cycles where AI reduces manual qualification and follow‑up burden.
Deal size expansion: recommendation engines and dynamic pricing
Recommendation engines and dynamic pricing directly lift average order value (AOV) and deal profitability. Evaluate uplift by channel and product: on‑site/product recommendations drive higher basket sizes and conversion, while dynamic pricing captures value by segment and demand. Look for measured outcomes by cohort (A/B tests) and margin impact: recommendation engines commonly add low‑double‑digit revenue lifts and dynamic pricing can materially increase AOV and profit margins when tuned to elasticity.
Margin lift in ops: predictive maintenance and lights‑out flows
Operational AI and automation reduce variable costs and increase throughput. Predictive maintenance reduces unplanned downtime and maintenance spend, while end‑to‑end lights‑out flows reduce labour cost and defect rates. For valuation, translate operational improvements into sustained margin expansion (higher EBITDA) via reduced COGS, fewer outages, and lower headcount scaling per unit of output.
Model the upside: NRR, AOV, cycle time, error rate, and market share
“Quantify upside with concrete outcomes observed in AI+automation projects: AI sales agents have driven ~50% revenue uplifts and 40% shorter sales cycles; recommendation engines and dynamic pricing can add 10–30% to revenue/AOV; customer-focused AI has reduced churn by ~30% and improved close rates by ~32% — use NRR, AOV, cycle time and error-rate levers to model value accretion precisely.” Portfolio Company Exit Preparation Technologies to Enhance Valuation — D-LAB research
Turn those outcomes into a model by: 1) establishing clean baselines (NRR, AOV, conversion and cycle time by cohort); 2) creating conservative/mid/aggressive uplift scenarios tied to specific initiatives (retention, pipeline, pricing, ops); 3) converting KPI deltas into revenue and margin impacts over a 12–36 month horizon; and 4) running sensitivity on CAC payback and churn to test valuation resilience. Include capex and run‑rate opex for AI/RPA investments and account for one‑off integration costs and ongoing maintenance.
When the model shows credible, measurable upside, pair it with execution proof points (A/B tests, production dashboards, and runbooks) and then stress‑test assumptions against worst‑case exception rates and technology fragility. With both numbers and execution in hand you can confidently translate automation investments into value‑creation plans — next, you’ll want to inspect the risks that can undermine those gains and prepare a focused stabilization roadmap to protect and scale the highest‑impact automations.
RPA due diligence red flags and a 100-day plan postclose
Red flags that depress valuation
Look for concentration and fragility: a handful of fragile UI‑scrape bots carrying most volume; no version control or backups for bot code; lack of secrets management (credentials in cleartext); no SLAs or monitoring; missing audit trails for sensitive data; orphaned citizen‑dev automations with no ownership; undocumented exceptions and high rework rates; unclear license or IP ownership for bot assets; and absence of a prioritised backlog or measurable ROI evidence. Any combination of these increases technical and operational debt and compresses valuation.
15 diligence questions to ask the automation lead
1) What are the top 10 automations by business value and who owns each?
2) Where is bot source stored, who can push to production, and are releases versioned?
3) How are credentials and secrets managed and rotated?
4) What percentage of integrations use APIs vs. UI scraping and what’s the change‑impact plan?
5) What monitoring and alerting exist for failures and SLA breaches?
6) How do you classify and protect PII/regulated data in automations?
7) What is your mean time to detect and mean time to repair for bot incidents?
8) Who signs off on compliance/security and how are gates enforced in intake?
9) Are there automated tests (unit/regression) and a CI/CD pipeline for bots?
10) How do you measure bot utilization, exception rate, and business outcome realization?
11) Which automations are maintained by citizen developers vs. the CoE and what are handover rules?
12) What third‑party components or open‑source libraries are in scope and what are the license risks?
13) Have you run penetration tests or architecture reviews and what were the remediation items?
14) What is the disaster recovery plan for orchestrator and bot runner infrastructure?
15) What are the top three single points of failure and the mitigations in place?
A pragmatic 100-day plan: stabilize, secure, and scale the top 10 automations
Days 0–30 — Stabilize: run an intake audit to confirm the top 10 automations, owners, and dependencies. Execute smoke tests, verify backups and runbooks, rotate any exposed credentials, and patch critical platform vulnerabilities. Put temporary run‑time guardrails (e.g., throttles, feature flags) on high‑risk bots.
Days 31–60 — Secure & standardize: onboard top automations into version control and CI pipelines, integrate secrets into a vault, implement basic observability (central logs, alerts, dashboards), and run a tabletop incident exercise. Close high‑priority compliance gaps and update data‑handling policies for sensitive fields.
Days 61–100 — Scale & optimize: introduce automated regression tests, formalize deployment (staging → production) and release cadence, and apply value/risk scoring to the wider pipeline. Begin replatforming fragile UI scrapes to APIs where feasible and document SLAs for ongoing operations. Deliver a one‑page playbook for each top 10 automation covering ownership, runbooks, KPIs and rollback steps.
Targets to track weekly: utilization, exceptions, releases, wins
Track a compact weekly dashboard that includes: bot utilization (productive runtime vs. availability), exception rate and root‑cause categories, number of releases and rollback events, MTTR for incidents, number of automations promoted to production, realized cost/time savings against targets, and a wins log showing business outcomes (reduced cycle time, decreased FTE effort, or increased throughput). Use these metrics to prioritize remediation and to validate that scale plans are delivering predictable value.
Capturing red flags quickly and executing a disciplined 100‑day program turns risky automation portfolios into investable, scalable assets. Once stabilized, use the documentation, tests and weekly targets above as the foundation for ongoing value capture and a longer‑term roadmap.