The Role Of AI In Modern Technical Due Diligence

AI is changing technology M&A in two directions at once. Deal teams are increasingly using AI to analyze technical information faster, while investors must also determine whether the companies they are acquiring are genuinely prepared to build, operate, and scale AI-enabled products.

PwC’s 2026 M&A outlook notes that AI is reshaping both what buyers are willing to acquire and how deals themselves are conducted, accelerating activities such as diligence, valuation, and investment committee preparation. The same report also highlights that software investors are becoming more selective as they assess which business models may benefit from AI and which may face disruption.

For technology due diligence, this creates a broader mandate. Investors can no longer focus only on whether the software works today. They also need to understand whether the target has the data, architecture, infrastructure, governance, security, and engineering capabilities required to operate effectively in an AI-driven environment.

This guide examines both sides of AI in technical due diligence and the questions investors should ask before committing to a technology investment.

Why Traditional Due Diligence Falls Short

Today’s due diligence process is buckling under the weight of three compounding challenges:

• Data remains fragmented across organizations, with high dependency on manual work to consolidate, validate, and assess even the most basic risk signals.
• Costs remain high, driven by volume-heavy, low-value workflows that require large operational teams.
• Buyers’ expectations have evolved from functional software to scalable, secure, and AI-ready solutions.

Traditional methods, clearly, weren’t designed to evaluate software products in this new context. Firms are turning to AI-powered technical due diligence as a response to what modern M&A demands: speed, accuracy, and depth of analysis.

Deal teams currently utilize AI to automate tasks that previously required days of work, such as mining documents, scanning contracts, analyzing delivery patterns, reviewing code, etc., enabling them to move faster with greater certainty. According to McKinsey, up to 30% of hours worked could be automated by 2030, accelerated by gen AI.

As deal cycles tighten and pressure mounts to make the right call quickly, AI offers the edge firms need to act decisively without sacrificing rigor. Those who are unwilling to adapt risk falling behind as deal-making gets faster, sharper, and more demanding.

AI Has Changed What Investors Need to Diligence

AI is changing not only how technology due diligence is performed, but also what investors need to evaluate before acquiring a technology company.

For many software businesses, AI is becoming increasingly connected to future product differentiation, operating efficiency, engineering productivity, and competitive positioning. As a result, investors are no longer assessing only whether the target’s existing technology is stable and scalable. They also need to determine whether the company has the technical foundations required to compete as AI becomes more deeply embedded in software products and delivery models.

PwC’s 2026 M&A outlook highlights this shift. AI is influencing both the assets buyers are willing to acquire and the way transactions themselves are evaluated, including diligence, valuation, and investment committee preparation. PwC also notes that software investors have become more selective as they reassess which businesses are positioned to benefit from AI and which may face disruption from it.

For some private equity firms, this question has become significant enough that investment committees are spending a meaningful portion of their time evaluating whether portfolio companies can capture value from AI or are exposed to AI-driven disruption. That changes the diligence agenda.

Those questions remain essential, but AI adds another layer.

These questions are particularly important because the presence of AI features does not necessarily indicate AI readiness.

A company may have successfully integrated a generative AI assistant into its product while still relying on fragmented data, tightly coupled legacy applications, limited API capabilities, weak model governance, or manual AI operations.

From an investment perspective, that distinction matters. An AI feature may take months to build, while creating the technology foundations required to scale AI across an organization can require much larger investments in data engineering, architecture modernization, cloud infrastructure, governance, security, and engineering capabilities.

AI Is Also Changing How Investors Think About Competitive Risk

This creates several possible scenarios. A target may have a defensible advantage because its AI capabilities are supported by proprietary data, deep domain expertise, differentiated workflows, or technology that is difficult for competitors to replicate.

Another company may appear highly AI-enabled while relying largely on commercially available foundation models and features that competitors could reproduce relatively quickly. A third business may not have a significant AI strategy at all, but its existing product could become vulnerable if competitors use AI to deliver similar functionality faster, cheaper, or through a fundamentally different user experience.

Technology due diligence therefore needs to evaluate not only the target’s current AI implementation, but also how defensible that implementation is and how AI may change the competitive position of the underlying product.

AI Economics Are Becoming Part of Technology Risk

AI also introduces a category of technology economics that traditional software diligence may not have examined deeply. Traditional SaaS businesses often benefit from relatively predictable infrastructure economics as they scale.

A technically successful AI capability may therefore still create an unattractive business model if the cost of serving additional users grows faster than expected. Investors should understand whether AI unit economics have been tested under realistic production usage rather than relying only on successful pilots or demonstrations.

AI Is Increasing the Importance of Data Due Diligence

AI also makes data quality and accessibility more strategic. A company may have large volumes of data without necessarily having data that can support AI.

Fragmented data can therefore become more than an operational inconvenience. It can directly constrain the company’s ability to execute an AI-driven product or automation strategy. This is particularly relevant when the investment thesis assumes that proprietary data will become a source of competitive advantage.

AI Is Creating New Governance, Security, and IP Risks

Generative AI also introduces risks that may not exist in conventional software environments.

These issues become especially important in regulated industries such as healthcare and financial services, where AI failures may create compliance, privacy, or customer risk.

Investors Ultimately Need to Validate the AI Investment Thesis

The objective of AI-focused technology due diligence is therefore not to determine whether a target is using the newest AI technology.

It is to determine whether the assumptions about AI embedded in the investment thesis are technically achievable. If management expects AI to drive substantial revenue growth, investors should understand whether the product, data, architecture, and engineering organization can realistically deliver that roadmap.

If AI is expected to improve margins, diligence should determine whether the infrastructure and model economics support those assumptions.

And if proprietary AI is presented as a competitive differentiator, investors should understand what actually makes that capability defensible.

This changes the central technology diligence question from: “Is the software technically sound today?” to: “Is the technology organization positioned to remain competitive, scalable, and economically viable as AI changes the market around it?”.

That is why AI readiness is increasingly becoming part of modern technology due diligence, alongside architecture, code quality, cybersecurity, scalability, technical debt, and engineering maturity.

How AI Accelerates Technical Due Diligence

In the next few years, AI is expected to help investors make informed decisions earlier in the M&A transaction. Here are three key ways AI improves the efficiency of technical due diligence process:

Automation of Code Analysis

Manual software code reviews are time-consuming and inconsistent. Traditionally, teams perform manual line-by-line reviews to evaluate code quality, security risks, and maintainability.

AI-driven code analysis now automates this process, scanning entire codebases in minutes to flag technical debt, detect inconsistent coding practices, and surface vulnerabilities that might otherwise be missed.

Beyond volume, this ensures engineers and deal teams spend less time on surface-level assessments and more time interpreting the implications of what’s found.

Scalability And Performance Testing

Modern M&A has long shifted from merely buying working software to investing in platforms that can scale.

AI models can simulate high-traffic environments and stress-test infrastructure to evaluate how applications behave under load. This insight is especially valuable when evaluating whether a product can handle increased user load, geographic expansion, or integration into a larger platform.

Unlike traditional load tests, AI-enhanced systems can dynamically adjust inputs, model real-world user behavior, and uncover bottlenecks that impact scalability or resilience, well before those issues become live problems.

Risk Identification

Manual reviews tend to focus on known risks.

AI goes further, identifying hidden patterns that signal underlying issues, such as unusually slow build cycles, security misconfigurations, or outdated third-party libraries.

By continuously learning from new data, AI-driven code review due diligence becomes better at spotting anomalies that don’t yet have names, but still carry significant implications.

Benefits Of AI In Technical Due Diligence

AI in software due diligence has become a strategic imperative as deal teams look to maintain margins by becoming more efficient. Here are the key benefits that advanced AI capabilities offer:

But AI’s value doesn’t end at due diligence execution. A survey from Eight Advisory shows that while 71% of M&A transactions are viewed as strategically and financially successful, only 40% actually achieve or exceed their “expected synergies”. Hence, the most forward-looking firms are now extending AI beyond due diligence into the post-merger phase, where real value can be created.

The Next Level Of AI-Powered Technical Due Diligence

Most AI-powered due diligence solutions today stop at the surface. The algorithms focus on answering: “what” is going on with the software product. That’s only Level 1: Faster code scan for faster answers, and there’s a much greater opportunity than most people realize

Enterprise-level investors play this game differently. They use AI to understand not only “what” has been built, but also the “why”. This is Level 2: Using AI to analyze the software development lifecycle (SDLC).

Next-gen AI due diligence solutions adopt the process mining methodology – a data-driven technique that delves into the root cause of variations across the SDLC. With the ability to measure performance, delivery timelines, process adherence, and failure points within the SDLC, these solutions provide firms with deeper insights on how to ship more quickly with higher quality at a lower cost.

At KMS Technology, we’ve built that depth into our assessments, powered by A that turns the software delivery process into a quantifiable asset. By analyzing SDLC performance, KMS helps buyers identify risks and integration blockers earlier, and helps sellers improve their delivery maturity pre-market.

Emerging Technologies Shaping The Future Of AI-Driven M&A

As dealmakers grow more fluent in leveraging AI across tech due diligence services, attention is shifting to how these technologies will evolve in the coming years and what that means for the global M&A industry.

At the forefront are AI agents, autonomous assistants powered by machine learning that can operate as active members of the deal team. These agents can independently manage workflows, delegate tasks, and interact with both people and systems to keep deals moving forward.

We’re already seeing signs of what’s possible. Rogo, an AI startup behind a chatbot that replicates an investment banker, has successfully raised $50 million in Series B funding. The chatbot is designed to analyze market positioning, competitor activity, and valuation benchmarks in minutes, setting a new pace for deal sourcing.

The acceleration of these key technologies also pushes AI-powered technical due diligence moving forward:

• Advanced Natural Language Processing (NLP) will make it possible to analyze complex technical contracts, architectural documentation, and compliance materials with far greater speed and precision.
• Predictive analytics will play an even larger role, helping deal teams model future risks and forecast post-close performance based on historical engineering data.
• Cross-platform integration will support deal teams to pull insights from across the software ecosystem and build richer, more holistic data viewpoints.

With 95% of CEOs planning to pursue M&A in the next year or two, dealmakers need to stay ahead of these advancements for the sake of their success.

Final thoughts

AI is reshaping technology due diligence in two important ways. It can help diligence teams analyze larger volumes of technical evidence more efficiently, while also creating new risks and readiness questions that investors need to evaluate before acquiring a technology company.

The key challenge is no longer simply determining whether a target has introduced AI features. Investors need to understand whether the underlying data, architecture, infrastructure, governance, security, engineering capabilities, and economics can support those capabilities at scale.

This makes AI readiness increasingly relevant to the broader technology due diligence process. A compelling AI roadmap may still require significant post-close investment if the technical foundations are weak, fragmented, or difficult to scale.

For investors, the objective is therefore to distinguish between AI adoption and AI readiness, and to determine whether AI strengthens the investment thesis or introduces additional execution risk.