Whether you're acquiring a company, investing in AI capabilities, or selecting a major technology vendor, the stakes are high. AI has become central to competitive advantage, but not all AI is created equal. Some organizations have built genuine capabilities that will compound in value. Others have assembled fragile systems held together by a few key employees and undocumented processes.
The difference between these scenarios can mean millions of dollars—in either direction. That's why AI due diligence has become essential for any significant technology investment.
This framework applies whether you're a private equity firm evaluating an acquisition target, a corporate development team assessing a potential partner, or a business leader choosing between AI vendors. The questions may be scaled up or down, but the categories remain the same.
The Four Pillars of AI Due Diligence
Effective AI due diligence examines four interconnected areas. Weakness in any one area can undermine the others, so a comprehensive evaluation covers all four.
1. Data Assets and Quality
AI systems are only as valuable as the data that powers them. During due diligence, you need to understand not just what data exists, but whether it's truly an asset or a liability.
Key questions to investigate:
What data does the organization actually have?
Look beyond marketing claims. Many organizations overstate their data assets. What matters is what data is consistently captured, properly stored, and actually used. Request data dictionaries, schema documentation, and evidence of data governance practices.
Is the data proprietary and defensible?
Data that anyone can purchase or scrape isn't a competitive advantage. Evaluate whether the data is truly unique—gathered through proprietary processes, customer relationships, or operational activities that competitors can't easily replicate.
What's the quality and completeness?
Sample the data directly when possible. Look for consistency issues, missing fields, duplicate records, and data entry errors. Ask about data validation processes and how quality is monitored over time.
Are there legal or compliance risks?
Understand how data was collected and whether appropriate consents were obtained. This is especially critical for customer data, healthcare information, or any data subject to regulations like GDPR, HIPAA, or CCPA. Undisclosed data compliance issues can become expensive surprises post-acquisition.
What's the data's historical depth?
Many AI applications require substantial historical data to train effective models. If the organization only has 12 months of clean data, certain AI applications may not be feasible regardless of other capabilities.
2. AI and ML Model Maturity
Not all AI is sophisticated AI. Some organizations have production-grade machine learning systems. Others have proof-of-concept models that have never been properly validated. Understanding where on this spectrum an organization sits is crucial.
Key questions to investigate:
What AI/ML is actually in production?
There's a significant difference between AI that's running in production, affecting business outcomes, and AI that exists as a promising prototype. Request a clear inventory of what's deployed, what's in development, and what's conceptual.
How were models validated?
Properly validated models have been tested on holdout data, evaluated against relevant metrics, and monitored for performance drift over time. Ask for documentation of validation processes and ongoing monitoring. If this documentation doesn't exist, that's a significant red flag.
What's the technical architecture?
Understand whether AI systems are built on modern, maintainable architectures or on legacy approaches that will be difficult to scale or update. Look for evidence of MLOps practices—version control, automated testing, deployment pipelines, and monitoring.
Is there vendor lock-in?
Some AI capabilities are deeply dependent on specific vendors or platforms. This isn't necessarily bad, but you need to understand the implications. What happens if the vendor raises prices? What's the cost and effort of migrating?
What's the model refresh cycle?
AI models degrade over time as the world changes. Organizations with mature AI practices regularly retrain and update models. Those without this discipline often have models that performed well historically but are declining in effectiveness.
3. Talent and Organizational Capability
AI doesn't run itself. Behind every successful AI implementation is a team that builds, maintains, and improves it. Understanding the human capital is as important as understanding the technology.
Key questions to investigate:
Who are the key people, and what happens if they leave?
In many organizations, AI capabilities are concentrated in one or two individuals. If those people leave, the AI capability may leave with them. Understand the key person risk and what knowledge management practices exist.
Is there documentation?
Well-run AI teams document their work—data pipelines, model architectures, training procedures, and operational runbooks. If documentation is sparse or nonexistent, knowledge transfer will be difficult and costly.
What's the team structure?
Effective AI requires collaboration between data engineers, data scientists, ML engineers, and business stakeholders. Evaluate whether the organization has appropriate roles or whether individuals are stretched across too many responsibilities.
Is there business integration?
The best AI teams work closely with business stakeholders to identify opportunities and translate technical capabilities into business value. Teams isolated from the business often build impressive technology that doesn't get adopted.
4. Risks and Technical Debt
Every technology organization accumulates technical debt—shortcuts and compromises that made sense at the time but create ongoing costs. AI systems are particularly prone to hidden debt that can be expensive to address.
Key questions to investigate:
What's the security posture?
AI systems often have access to sensitive data and can become attack vectors. Evaluate security practices, access controls, and incident response capabilities.
Are there compliance exposures?
AI applications in regulated industries face specific requirements around explainability, fairness, and documentation. Determine whether the organization has addressed these requirements or is operating with unaddressed compliance risk.
What's the infrastructure cost trajectory?
AI can be expensive to run at scale. Understand current infrastructure costs and how they're projected to grow. Some organizations have AI that works but isn't economically viable at scale.
What technical debt exists?
Look for warning signs: manual processes that should be automated, models that can't be retrained without heroic effort, data pipelines that break frequently, and systems that no one fully understands. This debt represents future costs that should be factored into any valuation.
Beyond Risk: Finding Value Creation Opportunities
Due diligence isn't just about identifying problems. It's also about understanding where value can be created post-investment.
Underutilized data assets — Sometimes organizations have valuable data they're not fully exploiting. Identifying these opportunities can significantly increase the value of an investment.
Scalable capabilities — AI capabilities that work in one area of a business often can be extended to others. Look for models and approaches that could be applied more broadly.
Quick wins for improvement — Minor investments in data quality, infrastructure, or talent can sometimes unlock major improvements in AI performance.
Competitive moats — Understand which AI capabilities, if strengthened, could create lasting competitive advantages versus those that competitors could easily replicate.
Making Better Decisions
AI due diligence produces better investment decisions—not by eliminating risk, but by ensuring that risks are understood, priced appropriately, and addressed in post-investment plans.
Organizations with strong AI fundamentals command premium valuations for good reason: they have genuine capabilities that will compound in value. Organizations with weak fundamentals may still be attractive investments, but the price should reflect the work required to build proper foundations.
The worst outcome is paying a premium for AI capabilities that don't actually exist—or that will evaporate when key employees leave. Proper due diligence prevents these expensive surprises.