On March 20, 2025, the top-tier crypto exchange Kraken announced its $1.5 billion acquisition of trading platform NinjaTrader, marking the largest-ever bridge between TradFi and crypto markets. But beyond the headline value, what elevates this deal is Kraken’s pioneering use of artificial intelligence (AI) to complete technical due diligence in just hours, automating tasks that once took weeks and a full bench of analysts.
By leveraging AI in the due diligence process, Kraken not only accelerated one deal but also gained the bandwidth to pursue multiple deals simultaneously without compromising accuracy.
This signals a new M&A playbook where AI is quickly becoming the engine behind modern due diligence. According to Bain & Company, only 16% of deal teams used generative AI in 2023. By 2028, that number is expected to reach 80% as early adopters prove how transformative AI-powered diligence can be.
This blog explores how AI is transforming technical due diligence and why it’s set to become the new benchmark for M&A efficiency in the coming years.
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.
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:
1. 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.
Key takeaway: AI enables a level of code analysis that’s faster, deeper, and more objective than human reviewers alone can achieve.
2. 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.
Key takeaway: AI helps validate whether a product can support future growth, which is critical for informed investment decisions.
3. 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.
Key takeaway: AI’s pattern recognition capabilities help detect silent risks that might derail integration or post-acquisition performance.
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:
- Speed:
AI cuts down diligence timelines from weeks to days by automating document analysis, code review, and performance scans. For deal teams managing multiple targets or racing toward exclusivity, that time savings can make or break a deal.
- Accuracy:
Machine learning models can analyze large, complex datasets with greater consistency than human teams.
AI code analysis also enables comprehensive scans of entire codebases, system logs, and delivery workflows to detect errors earlier, reducing the margin for M&A risks.
- Cost Efficiency:
Automation reduces the need for large operational teams and minimizes manual effort across analysis workflows. With AI handling the heavy lifting, firms can scale diligence across more deals without ballooning costs.
- Strategic Insights:
AI-powered due diligence enables predictive analytics that surface post-close risks and highlight opportunities to accelerate value creation. These insights equip deal teams to make smarter decisions and position for stronger exits.
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.
| Criteria | Traditional Technical Due Diligence | Advanced AI-powered Due Diligence |
| Core Question | “What’s been built, and is it stable and secure?” | “How is it being built, and can this team scale or ship effectively?” |
| Assessment Approach | Qualitative analysis – interviews, code scan | Quantitative analysis – time stamps and velocity metrics to understand true process behavior |
| Focus Area | Measures quality of software product and company | Measures performance, delivery timelines, process adherence, and failure points in the SDLC |
| Observability | Helps customers understand potential opportunities and risks in the software product and company | Provides customers with a deeper dive into SDLC operations to identify risks in delivery |
| Business Impact | A starting point for better product, cleaner code, and smarter orientation | Better process means better outcome, and ultimately stronger business portfolio |
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.
Our ambition is to bring clarity to investment theses, enable smoother post-close transitions, and drive stronger valuation multiples. If you share the same vision, get in touch and explore how KMS can support your next deal.
Emerging Technologies Shaping The Future Of AI-Driven M&A
As dealmakers grow more fluent in leveraging AI across technical due diligence, 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.
FAQs
1. Is AI-driven due diligence just for tech-focused deals?
Not at all. Since nearly every modern business is a software business at its core, AI-driven diligence is critical for uncovering hidden risks and opportunities in any M&A target, regardless of industry.
2. How does AI quantify risks that are typically subjective, like technical debt?
AI transforms subjective assessments into hard, quantifiable metrics by analyzing the entire software development lifecycle (SDLC), not just the final code. It measures factors like code churn, delivery velocity, and bug fix times to calculate the real cost of technical debt in terms of lost productivity and future engineering effort.
3. Will leveraging AI in our diligence process create a “black box” that we can’t explain to our investment committee?
Quite the opposite. AI provides transparency by delivering a clear, data-backed audit trail that justifies every conclusion, moving you away from reliance on qualitative opinions. Moreover, AI and advanced intelligence can present findings in intuitive dashboards that link directly back to the source data, allowing you to confidently articulate the story behind the numbers and de-risk your investment thesis.
4. Does our team need data scientists to use AI-powered due diligence tools?
No, the best AI platforms are designed to empower deal teams, not replace them with data scientists. These AI-powered due diligence tools can translate complex engineering and delivery data into clear, actionable business insights focused on risk, scalability, and team performance for your deal team.
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