The Role of AI in Modern Technical Due Diligence
On March 20, 2025, leading crypto exchange Kraken announced its $1.5 billion acquisition of trading platform NinjaTrader—marking the largest-ever bridge between traditional finance (TradFi) and crypto markets. Beyond the deal’s headline value, what stood out was Kraken’s pioneering use of artificial intelligence (AI) to complete technical due diligence within hours, automating tasks that once required weeks and a full bench of analysts.
By leveraging AI in the due diligence process, Kraken not only accelerated a single deal but also gained the capacity to pursue multiple transactions simultaneously—without compromising accuracy.
This signals a new M&A playbook, where AI is fast becoming the engine driving modern due diligence. According to Bain & Company, only 16% of deal teams used generative AI in 2023. By 2028, that figure is projected to reach 80% as early adopters demonstrate the transformative impact of AI-powered diligence.
This article explores how AI is reshaping technical due diligence—and why it’s poised to become the new benchmark for M&A efficiency in the years ahead.
Why Traditional Due Diligence Falls Short
Today’s traditional due diligence process is struggling under three compounding challenges:
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Fragmented data spread across organizations, with heavy reliance on manual consolidation, validation, and risk assessment—even for the most basic signals.
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High costs driven by repetitive, low-value workflows requiring large operational teams.
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Evolving buyer expectations, shifting from simply functional software to solutions that are scalable, secure, and AI-ready.
Clearly, traditional methods weren’t designed to evaluate software products in this new context. Companies are now turning to AI-powered technical due diligence to meet modern M&A demands: speed, accuracy, and depth of analysis.
Deal teams are already using AI to automate tasks that once took days—such as document mining, contract scanning, delivery pattern analysis, and code review—enabling them to move faster with greater confidence. According to McKinsey, up to 30% of working hours could be automated by 2030, accelerated by generative AI.
As deal cycles shorten and the pressure to make the right call intensifies, AI gives firms the edge to act decisively without sacrificing rigor. Those unwilling to adapt risk falling behind as deal-making becomes faster, sharper, and more demanding.
How AI Accelerates Technical Due Diligence
In the coming years, AI is expected to help investors make smarter, earlier decisions in M&A transactions. Here are three key ways it enhances efficiency:
Automated Code Analysis
Manual code reviews are both time-consuming and inconsistent. Traditionally, teams reviewed line by line to evaluate quality, security risks, and maintainability.
AI-driven analysis now automates this process, scanning entire codebases in minutes to flag technical debt, inconsistent practices, and vulnerabilities that humans often miss.
👉 Key takeaway: AI enables faster, deeper, and more objective code analysis than human reviewers alone.
Scalability & Performance Testing
M&A is no longer about acquiring software that simply “works”—it’s about investing in platforms that can scale.
AI models simulate high-traffic environments, stress-testing infrastructure to evaluate how applications behave under load. This helps assess whether a product can support more users, geographic expansion, or integration into a larger platform.
Unlike traditional load tests, AI-enhanced systems can dynamically adjust inputs, mimic real-world user behavior, and uncover bottlenecks early.
👉 Key takeaway: AI validates whether a product is truly built for future growth.
Risk Identification
Manual reviews typically focus on known risks.
AI goes further, spotting hidden patterns such as unusually slow build cycles, misconfigured security, or outdated third-party libraries.
As AI continues to learn from new data, it becomes more adept at identifying “silent risks” that lack formal names but carry serious implications.
👉 Key takeaway: AI’s pattern recognition capabilities reveal risks that might derail integration or post-acquisition performance.
Benefits of AI in Technical Due Diligence
AI-powered software due diligence has become a strategic imperative for maintaining margins and boosting efficiency. The key benefits include:
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Speed: Reducing diligence timelines from weeks to days with automated document analysis, code review, and performance testing.
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Accuracy: Machine learning models analyze complex datasets consistently, detecting risks earlier across entire codebases, system logs, and workflows.
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Cost efficiency: Automation reduces reliance on large teams, enabling firms to scale diligence across more deals without inflating costs.
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Strategic insights: Predictive analytics surface post-close risks and opportunities to accelerate value creation, equipping deal teams to make smarter moves.
AI’s value doesn’t stop at execution. According to Eight Advisory, while 71% of M&A transactions are viewed as strategically and financially successful, only 40% actually achieve or exceed expected synergies. That’s why forward-looking firms are extending AI beyond diligence into the post-merger phase—where real value is realized.
The Next Level of AI-Powered Due Diligence
Most current AI solutions stop at Level 1, answering the question: “What has been built?”—delivering faster scans for faster answers.
Enterprise investors, however, play at Level 2, asking: “Why was it built this way, and how is it being built?”
By applying AI to analyze the Software Development Lifecycle (SDLC), firms gain deeper operational insights. Leveraging process mining techniques, next-gen solutions measure performance, delivery timelines, process adherence, and failure points. The result: a clearer view of how to ship faster, at higher quality, and lower cost.
Comparison:
| Criteria | Traditional Technical Due Diligence | Advanced AI-Powered Due Diligence |
|---|---|---|
| Core Question | “What’s been built? Is it stable and secure?” | “How is it being built? Can the team scale and deliver effectively?” |
| Approach | Qualitative—interviews, code scans | Quantitative—timestamps, velocity metrics |
| Focus | Product & company quality | SDLC performance, adherence, and failure points |
| Observability | Risks & opportunities in the product | Deep dive into SDLC to uncover delivery risks |
| Business Impact | Better product & cleaner code | Better process → better outcomes → stronger portfolio |
At KMS Technology, we’ve built this depth into our assessments, turning software delivery into a quantifiable asset. By analyzing SDLC performance, we help buyers identify risks and blockers earlier—and help sellers improve delivery maturity pre-market.
Our goal: bring clarity to investment theses, enable smoother post-close transitions, and drive stronger valuation multiples.
Emerging Technologies Shaping the Future of AI-Driven M&A
As deal teams become more fluent in AI-powered diligence, attention is shifting to how these technologies will evolve—and what they mean for global M&A.
Key trends include:
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AI Agents: Autonomous assistants that act as deal team members—managing workflows, assigning tasks, and interacting with people and systems.
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Advanced NLP: Rapid analysis of technical contracts, architecture docs, and compliance materials with higher precision.
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Predictive Analytics: Modeling future risks and forecasting post-close performance from historical engineering data.
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Cross-Platform Integration: Pulling insights from across ecosystems for a more holistic view.
We’re already seeing examples. AI startup Rogo raised $50 million in Series B funding for its chatbot that replicates an investment banker—analyzing market positioning, competitor activity, and valuation benchmarks in minutes.
With 95% of CEOs planning to pursue M&A in the next 1–2 years, staying ahead of these AI-driven advancements will be critical to success.