Technical Debt Due Diligence: Which Shortcuts Threaten the Investment Thesis?

Technical debt should affect an investment decision when it threatens an underwritten business outcome. Old code, architectural complexity, limited test coverage, and manual deployments may be undesirable, but they are not automatically material. The question is whether those conditions could impair the growth, margin, reliability, integration, security, or delivery assumptions supporting the deal.

That distinction changes the purpose of technical debt due diligence. The objective is not to inventory every engineering imperfection. It is to identify constraints, test them against the ownership plan, compare credible responses, and translate the findings into underwriting and execution decisions.

KPMG’s September 2025 Technology Sector M&A Survey included 135 respondents, comprising 104 from technology or software organizations and 31 from PE or VC firms. Overall, 55% reported that 20% to 40% of engineering staff time had been spent addressing or fixing technical debt during the prior year. In a separate lifecycle question answered by 132 respondents, 27% rated technical debt as highly important during pre-deal evaluation, compared with 68% during integration planning and 67% during post-merger integration. ^[6] Waiting until integration planning does not eliminate the work. It delays discovery until the buyer has less flexibility.

Define technical debt in investment terms

The Software Engineering Institute describes technical debt as a tradeoff in which shortcuts used to accelerate near-term delivery can create unexpected rework costs and degrade the flow of value over time. ^[1] For a deal team, that definition becomes useful when the shortcut is connected to an economic consequence.

A practical investment definition is:

Technical debt is a past technology decision or deferred obligation that could require unplanned cost, displace roadmap capacity, increase operating risk, or constrain an assumption in the investment thesis.

This definition excludes imperfections that engineers may reasonably want to improve but that do not require action during the expected ownership period. It also captures constraints that source-code review alone may miss, including brittle integrations, unsupported infrastructure, fragile data pipelines, slow release processes, undocumented operating dependencies, and systems understood by only a few people.

ISO/IEC 25010:2023 defines a product-quality model for ICT and software products. The model is intended to support requirements specification and quality evaluation by stakeholders including acquirers and independent evaluators. ^[3] Such models can organize diligence, but the buyer must still determine which quality characteristics matter to the transaction.

The relevant unit of analysis is not an isolated finding. It is the chain from technical condition to investment consequence:

Technical condition → operating constraint → thesis assumption at risk → financial or execution consequence → response

Tightly coupled product modules, for example, are not necessarily a deal issue. They become one if the ownership plan requires rapid product expansion, frequent add-on integration, or independent deployment of enterprise features. The architecture matters because of the demands the plan will place on it.

Test debt against the investment thesis

Start with the underwriting case and work backward. For each important assumption, determine what must be true technically, what evidence would validate it, and what happens if it proves false.

This approach avoids two common errors. The first is treating every weakness as a reason to reduce value. The second is accepting management’s assurance that debt is manageable without testing whether the company is already paying for it through slower delivery, incidents, support work, or postponed initiatives.

PwC identifies scalability, performance, security, developer throughput, innovation, and potentially significant unforeseen costs as channels through which technical debt can affect a transaction and its value. ^[7] The buyer must determine which channels are active in the target and relevant to the ownership plan.

Use delivery and reliability evidence to find existing drag

Management presentations often describe technical debt as a future risk. Delivery and reliability evidence can show whether the business is already absorbing the cost.

DORA measures software-delivery throughput using change lead time, deployment frequency, and failed-deployment recovery time. It measures instability using change-failure and deployment-rework rates. ^[2] These measures should not become a universal scorecard or a mechanical comparison across companies. DORA cautions that context matters and that comparisons between substantially different applications can be misleading. ^[2] The measures are more useful for examining trends, differences among comparable products, and the consistency between actual delivery performance and management’s roadmap claims.

Useful questions include:

• How long does a routine product change take from approval to production?
• How frequently can teams release without coordinated downtime or extensive manual work?
• What proportion of deployments require correction, rollback, or unplanned follow-up?
• How long does service restoration take after a failed deployment?
• How much engineering time goes to incidents, defects, support escalations, upgrades, and maintenance?
• Which roadmap items have been postponed because foundational work had to come first?
• Are releases becoming slower or riskier as the product and customer base grow?
• Can teams change one product area without retesting or redeploying unrelated components?

The Software Engineering Institute has examined an architecture-focused, measurement-based approach to technical debt that models rework through dependencies and change propagation rather than relying only on subjective code-quality judgments. ^[1] A practical diligence test is to select several representative roadmap items and ask the technical team to trace the components, data, tests, approvals, and deployments each change would affect.

That exercise is often more useful than asking whether the architecture is modern. It shows whether modest commercial requirements trigger disproportionate technical work.

Request evidence, not a technical-debt narrative

Management may understand its technical debt well, underestimate it, or use the term as a catch-all explanation for delivery problems. The deal team needs evidence that distinguishes among those possibilities.

Architecture and dependencies

• Current architecture and data-flow diagrams
• Component, service, database, vendor, and integration dependencies
• Known scalability limits and completed load tests
• Supported and unsupported languages, frameworks, databases, and infrastructure
• Single points of failure and high-concentration components
• Separation between core product logic and customer-specific customizations

Delivery and quality

• Release-frequency and lead-time trends
• Failed releases, rollbacks, hotfixes, and defect-escape patterns
• Automated and manual testing practices
• Backlog items categorized as maintenance, remediation, or foundational work
• Examples of roadmap changes delayed by technical constraints
• Time allocated to features, incidents, defects, support, and maintenance

Reliability and operations

• Availability and incident history
• Recovery objectives and evidence from recovery tests
• Monitoring, alerting, and escalation processes
• Infrastructure-capacity trends and major cost drivers
• Recurring operational workarounds

People and execution capacity

• Ownership of critical systems
• Areas understood by only one or two people
• Open engineering roles and persistent skill gaps
• Recent turnover in key technical positions
• Use of contractors for core product knowledge
• Management’s remediation plan, estimates, dependencies, and progress

Documents are only the starting point. Interviews should test consistency among executive claims, engineering evidence, delivery history, and the financial plan. A roadmap that assumes simultaneous feature acceleration, platform modernization, add-on integration, and margin improvement deserves particular scrutiny if the same small team must deliver all four.

Estimate remediation without false precision

A single technical-debt estimate usually creates more confidence than the diligence evidence supports. Present a range of credible pathways and make the assumptions behind each one visible.

For every material constraint, assess:

1. Required outcome. What capability or risk reduction does the ownership plan require?
2. Response options. Can the buyer retain, stabilize, refactor, replatform, replace, or retire the affected system?
3. Direct cost. What internal labor, external support, software, infrastructure, and migration expense would each option require?
4. Capacity displacement. Which product, customer, integration, or efficiency initiatives would be delayed?
5. Dependencies. Does the work require data cleanup, hiring, vendor changes, customer migration, contract changes, or process redesign?
6. Timing and sequencing. What must happen before the business can realize the underwritten benefit?
7. Execution risk. What could interrupt customers, degrade service, or extend the timeline?

AWS modernization guidance recommends evaluating applications across their business, functional, technical, and financial significance. It identifies expected assessment outputs including a roadmap capturing benefits, risks, and dependencies, a target-state architecture blueprint, and an action plan for closing foundational gaps. ^[4] AWS portfolio guidance also recommends balancing business drivers, migration speed, cost, technical principles, and expected benefits. It reserves more complex approaches such as refactoring or re-architecting primarily for strategic workloads rather than treating every application as a rebuild candidate. ^[5]

That is the right discipline for transaction work. The most technically ambitious option is not necessarily the best investment decision.

A useful estimate separates three cases:

• Minimum stabilization: Work required to reduce immediate reliability, supportability, or security exposure.
• Thesis-enabling remediation: Work required to support the specific growth, product, margin, or integration plan.
• Strategic modernization: Broader work that could improve long-term flexibility but is not essential to the base case.

This separation prevents optional modernization from being presented as unavoidable deal cost. It also prevents the buyer from underwriting superficial stabilization when the thesis requires structural change.

Choose the response based on the system’s role

Not every indebted system should be rebuilt. The response should reflect strategic importance, remaining useful life, change frequency, operating risk, and the ownership plan.

• Retain when the system is stable, sufficiently supported, and not constraining the thesis.
• Stabilize when targeted work can reduce near-term operating risk without major architectural change.
• Refactor when the core product remains valuable but specific structural constraints impede change or scale.
• Replatform when a different technical foundation can address a material constraint without redesigning the entire product.
• Replace when an existing product or commercial system cannot economically meet required capabilities.
• Retire when the system is redundant, low-value, or unnecessary after consolidation.

Migration risk belongs in the comparison. A replacement may look cleaner on an architecture diagram while introducing customer conversion, data reconciliation, training, and business-continuity risks that exceed the cost of retaining the existing system during the ownership period.

For buy-and-build strategies, this decision should connect directly to the integration model. EES’s guide to buy-and-build technology integration explains how to distinguish platform standards from capabilities that can remain local. Technical debt becomes more material when an acquired system prevents the standardization or data integration required by the synergy case.

Convert each material finding into a deal decision

A material finding should end with a decision, not just a severity label. Depending on the evidence, technical debt may affect:

• The base operating case: Add required people, software, infrastructure, external support, or migration expense.
• The downside case: Model slower product delivery, delayed integrations, higher support costs, reliability disruption, or deferred margin expansion.
• The ownership plan: Complete stabilization before launching growth initiatives that depend on the affected platform.
• Purchase price: Account for unavoidable investment or impaired earnings when the issue is sufficiently understood and material.
• Deal structure: Consider conditions, escrows, holdbacks, or other protections where appropriate and advised by transaction counsel.
• Representations and disclosure: Clarify known system limitations, unsupported components, service issues, or remediation commitments with legal advisers.
• Closing requirements: Address a limited number of urgent conditions that cannot reasonably wait until after close.
• The funded post-close plan: Assign owners, budget, milestones, dependencies, and board-level reporting before the work competes with new priorities.

Cybersecurity findings may overlap with technical debt but require their own risk and transaction treatment. The EES framework for cybersecurity due diligence in private equity explains how to translate those findings into valuation, contractual, insurance, closing, and remediation decisions.

Proportionality matters. A finding that requires two engineers for one quarter should not be presented like a platform constraint that could delay the commercial roadmap by a year. Both may be real, but they are not equally material.

Make the diligence report an input to the ownership plan

A useful technical debt diligence report should give the investment committee and operating team five things:

1. The thesis assumptions tested
2. The constraints that could impair those assumptions
3. The evidence supporting each conclusion and the remaining uncertainty
4. The credible remediation pathways, costs, timing, dependencies, and displaced work
5. The recommended underwriting, transaction, and post-close actions

This reflects a broader principle in the Private Capital Technology Decision Playbook: technology decisions should be tied to business importance, evidence, realistic execution requirements, and the cost of delay.

Technical debt rarely produces a clean binary answer. A company can carry substantial debt and remain an attractive investment if the obligations are understood, bounded, and matched with a credible plan. A company can also have a modern-looking technology stack while carrying architectural or operational constraints that undermine the deal.

The decisive question is not whether technical debt exists. It is which obligations matter, what they will consume, and whether the investment still works after accounting for them.