What is Technology Due Diligence?
Technology Due Diligence is the systematic evaluation of a company's AI and technology assets, capabilities, architecture, and risks conducted during mergers, acquisitions, investments, or partnerships to assess the true value and viability of its technology stack.
What Is Technology Due Diligence?
Technology Due Diligence is a structured investigation into a company's technology assets, capabilities, and risks conducted before making a significant business decision such as an acquisition, investment, partnership, or major technology commitment. In the context of AI, technology due diligence has become increasingly important because AI capabilities are often central to a company's valuation and competitive position.
Unlike financial or legal due diligence, which have well-established frameworks, technology due diligence requires evaluators who understand both the business implications and technical realities of AI systems. A chatbot that impresses in a demo might rely on hardcoded responses rather than genuine AI. A "proprietary algorithm" might be a minor customization of an open-source framework. Technology due diligence separates substance from hype.
When Technology Due Diligence Is Needed
Common scenarios that trigger AI-focused technology due diligence include:
- Mergers and acquisitions — Evaluating whether a target company's AI capabilities are as valuable as claimed
- Investment decisions — Assessing the technical viability and scalability of a startup's AI technology before investing
- Strategic partnerships — Verifying that a potential partner has the technical capabilities they claim
- Major vendor commitments — Evaluating whether a significant AI platform purchase is sound before signing a long-term contract
- Board-level reviews — Providing independent assessment of an organization's own AI capabilities and technology posture
Key Areas of AI Technology Due Diligence
AI and Machine Learning Assets
Evaluate the company's AI models and algorithms:
- Model inventory — What AI models exist, what do they do, and how are they used?
- Model performance — How accurate and reliable are the models? Are performance claims supported by rigorous testing?
- Proprietary vs. commodity — Are the models genuinely proprietary, or are they based on widely available open-source frameworks with minimal customization?
- Technical debt — How much rework or modernization is needed to maintain and improve existing models?
- Intellectual property — Are models and algorithms properly protected through patents, trade secrets, or other IP mechanisms?
Data Assets
Data is often the most valuable technology asset in AI companies:
- Data quality — Is the data accurate, complete, consistent, and current?
- Data volume and diversity — Is there enough data to support current and future AI applications?
- Data provenance — Where does the data come from? Are there licensing or legal restrictions on its use?
- Data governance — Are there proper processes for managing data quality, access, privacy, and compliance?
- Data moat — Does the company have access to unique data that competitors cannot easily replicate?
Technology Architecture
Assess the underlying infrastructure and systems:
- Scalability — Can the current architecture handle growth in data volume, user load, and model complexity?
- Technical debt — How much accumulated technical debt exists, and what will it cost to address?
- Security — Are systems properly secured against data breaches, model attacks, and other threats?
- Integration — How well do AI systems integrate with other enterprise applications?
- Cloud vs. on-premise — Is the infrastructure modern, flexible, and cost-effective?
Team and Capabilities
Evaluate the human capital behind the technology:
- Team composition — Does the team include the right mix of data scientists, ML engineers, data engineers, and product managers?
- Key person risk — Is critical knowledge concentrated in a small number of individuals?
- Talent retention — What is the turnover rate, and are there factors that might cause key people to leave after a transaction?
- Development practices — Does the team follow modern software engineering practices including version control, testing, code review, and documentation?
Regulatory and Ethical Risks
Identify potential legal and ethical exposures:
- Privacy compliance — Does the company comply with data protection regulations in all operating markets?
- Bias and fairness — Have AI models been tested for bias, and are there processes for ongoing monitoring?
- Regulatory exposure — Are there pending or potential regulatory actions that could affect the company's AI operations?
- Ethical concerns — Does the company's AI usage raise ethical issues that could create reputational risk?
Conducting AI Technology Due Diligence
Assemble the Right Team
Technology due diligence requires a cross-functional team:
- AI/ML specialists who can evaluate model quality, data practices, and technical architecture
- Enterprise architects who can assess infrastructure scalability and integration
- Security experts who can evaluate vulnerability to data breaches and cyberattacks
- Legal advisors who understand IP protection and regulatory compliance
- Business analysts who can connect technical findings to business value
Follow a Structured Process
- Document request — Request access to technical documentation, source code, data samples, model performance reports, and architecture diagrams
- Technical interviews — Conduct detailed interviews with the CTO, engineering leads, data science team, and infrastructure team
- Hands-on evaluation — Review source code, run model performance tests, assess data quality, and probe security measures
- Gap analysis — Compare findings against best practices and the claims made by the company
- Risk assessment — Identify and quantify technical risks, including remediation costs and timelines
- Report and recommendations — Summarize findings, quantify risks, and provide recommendations for the business decision
Technology Due Diligence in Southeast Asia
Regional factors that affect technology due diligence in ASEAN include:
- Varying IP protection — Intellectual property enforcement varies across ASEAN countries, affecting the value and protectability of AI assets
- Data sovereignty — Cross-border data regulations may limit the portability of data assets after an acquisition
- Talent market dynamics — Key person risk may be higher in markets where AI talent is especially scarce and mobile
- Regulatory evolution — Rapidly changing AI and data regulations across ASEAN create compliance risks that must be carefully evaluated
Key Takeaways for Decision-Makers
- Technology due diligence is essential for any significant business decision involving AI technology
- Focus on data assets, model quality, technical architecture, team capabilities, and regulatory risk
- Engage evaluators who understand both the technical realities and business implications of AI
- Use findings to negotiate fair valuations and plan for post-transaction integration or remediation
In an era where AI capabilities drive significant portions of company valuations, technology due diligence has become as important as financial and legal due diligence. Acquiring a company based on inflated AI claims can lead to overpayment, post-acquisition disappointment, and costly remediation. Conversely, thorough due diligence can uncover hidden value in data assets or technical capabilities that justify a premium.
For CEOs evaluating acquisition targets or strategic partnerships, technology due diligence provides the evidence needed to make informed decisions. It separates genuine AI capability from marketing claims and identifies risks that could destroy value after the transaction closes.
For CTOs, leading or participating in technology due diligence is a critical responsibility. Your technical judgment directly affects whether the organization makes sound technology investments and whether post-transaction integration succeeds.
In Southeast Asia's dynamic market, where AI startups and technology companies are frequently involved in investment rounds, partnerships, and acquisitions, building internal technology due diligence capability is a strategic advantage.
- Engage AI specialists, not just general IT auditors, for technology due diligence involving AI assets
- Evaluate data assets with the same rigor as financial assets — data quality, provenance, and exclusivity drive AI value
- Assess key person risk carefully, especially in markets with limited AI talent pools
- Test AI model performance independently rather than relying solely on the target company's reported metrics
- Identify and quantify technical debt as it directly affects post-transaction integration costs
- Review regulatory compliance across all operating markets, with particular attention to evolving AI regulations
- Include due diligence findings in valuation negotiations and post-transaction integration planning
Frequently Asked Questions
How long does AI technology due diligence typically take?
A thorough technology due diligence process typically takes four to eight weeks, depending on the complexity of the target company's technology stack. Simple assessments of a single AI product might be completed in two to three weeks. Complex evaluations involving multiple AI systems, large data assets, and multi-country operations can take eight to twelve weeks. Time constraints are common in competitive deal processes, so having a structured approach and experienced team helps complete assessments efficiently.
What are the biggest red flags in AI technology due diligence?
Key red flags include AI capabilities that cannot be demonstrated on live data, heavy reliance on one or two individuals for critical knowledge, lack of version control and documentation for models and code, training data with unclear provenance or licensing restrictions, and significant gaps between reported and independently verified model performance. Any of these should prompt deeper investigation and may affect deal terms or the decision to proceed.
More Questions
Yes. Periodic internal technology due diligence, sometimes called a technology audit or health check, helps identify risks and improvement opportunities before they become critical. It is particularly valuable before fundraising rounds where investors will conduct their own due diligence, before major strategic planning exercises, and when preparing for potential acquisition or partnership discussions. An internal assessment lets you address weaknesses proactively.
Need help implementing Technology Due Diligence?
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