How Should Technical Due Diligence Evolve for AI-Native Businesses?

Over the last five years, AI has evolved from a feature embedded in products to a core determinant of enterprise value. Capital markets have responded accordingly. Across venture, growth equity, and M&A transactions, technical due diligence has moved from a confirmatory exercise to a valuation-critical discipline. Assets, which were previously assessed primarily on software scalability and engineering hygiene, are now evaluated based on the defensibility, governability, and operational resilience of machine learning systems deployed at scale. 

This evolution has exposed a structural gap. Legacy diligence frameworks were built for deterministic software. They are poorly equipped to evaluate probabilistic systems whose behaviour is shaped by data quality, model architecture, and continuous retraining. As a result, acquirers and investors increasingly encounter situations where headline AI capabilities mask fragile foundations, commoditised implementations, or unquantified downside risk. Closing this gap requires new checklists, deeper technical scrutiny, and a significantly higher standard for evidence. 

Code and System Architecture: From Readability to Resilience

 

At the foundation of AI, due diligence remains code quality, but the lens through which it is viewed has undergone a fundamental change. For AI-native companies, the question is no longer whether the code works today, but whether the system can evolve safely under continuous model iteration, data drift, and expanding workloads. 

A rigorous review begins with architectural coherence. Mature AI companies demonstrate a clear separation between data ingestion, feature engineering, model training, inference, and downstream application layers. Tight coupling across these components signals fragility, particularly when models must be retrained or customised for enterprise clients. Code modularity, test coverage across ML pipelines, dependency management, and reproducibility controls are now baseline expectations rather than best practices. 

Scale AI, Inc., headquartered in San Francisco and widely used by autonomous vehicle developers and foundation model builders, provides a strong reference point. Its internal tooling emphasises versioned data pipelines, auditable labelling workflows, and model evaluation infrastructure that allows clients to benchmark performance across successive releases. This architectural discipline directly underpins its enterprise credibility and valuation trajectory. 

Crucially, diligence teams must test whether CI/CD processes extend meaningfully into machine learning operations. The absence of automated model validation, rollback mechanisms, or environment parity between training and production often reveals technical debt that is expensive to remediate after the transaction. 

Model Evaluation and Governance: Quantifying Risk, Not Just Accuracy

 

Model performance is the most visible aspect of AI capability and the most frequently misunderstood. Accuracy metrics alone are insufficient and often misleading when considered in isolation from the deployment context. Advanced diligence focuses on robustness, generalisation, and governance maturity.

Investors should demand evidence that models have been evaluated across multiple dimensions. This includes performance stability under distribution shift, sensitivity to adversarial or edge-case inputs, and calibration under real-world operating conditions. For regulated or safety-critical sectors, the absence of documented stress testing is a material red flag rather than a technical omission. 

Best-in-class companies maintain a clear lineage from training data through feature sets to deployed model versions. Retraining triggers are defined and monitored. Performance degradation is measured continuously rather than discovered anecdotally. These controls distinguish organisations that operate AI as an infrastructure from those that treat it as an experiment. 

Mid-market analytics firms increasingly provide instructive examples. ZestyAI, based in California and focused on climate and property risk analytics for insurers, combines proprietary datasets with transparent model governance and documented performance benchmarking. This combination has enabled adoption by highly risk-sensitive customers and sustained commercial traction. 

Data Moats: Separating Volume from Defensibility

 

Data remains the most overclaimed and under-validated source of competitive advantage in AI. Effective diligence moves beyond assertions of scale to interrogate exclusivity, durability and economic relevance. 

The first step is provenance. High-quality AI companies can clearly document the origin of data, its collection process, the rights associated with it, and its governance framework. Ambiguity around licensing, consent, or third-party dependencies introduces latent legal and operational risks that directly impact valuation. 

The second step is replicability analysis. If equivalent datasets can be acquired commercially or synthesised with minimal effort, the data provide limited defensibility regardless of size. True data moats emerge where access is structurally constrained, embedded within workflows, or protected by long-term contractual relationships. 

Finally, diligence must assess whether the data actually compounds the advantage. Data flywheels are real only when incremental usage materially improves model performance in ways competitors cannot replicate. Companies that merely accumulate static datasets without demonstrable learning effects rarely sustain differentiation. 

Market leaders such as Google LLC illustrate this dynamic at scale. Its consumer platforms generate continuous, high-frequency behavioural data that is tightly integrated into product ecosystems, reinforcing both model performance and distribution advantages simultaneously. The defensibility arises from integration, not volume alone. 

Infrastructure, Security and Scalability: Hidden Value and Hidden Risk

 

Operational readiness is often underestimated in AI transactions, yet it is where many post-deal failures originate. AI systems place asymmetric stress on infrastructure due to their high compute intensity, rapid storage growth, and latency sensitivity. 

Diligence must examine cloud architecture choices, cost controls, model serving efficiency and scaling strategies. Poorly optimised inference pipelines or uncontrolled retraining costs can erode margins rapidly as usage grows. Security considerations extend beyond traditional application risks to include model theft, prompt leakage and data exfiltration. 

Companies with enterprise-grade monitoring, access controls and incident response protocols demonstrate a level of operational maturity that supports long-term value creation. Conversely, gaps in these areas often signal a research-centric organisation that is unprepared for commercial scale. 

Technical Talent and Execution Capability 

Ultimately, AI systems reflect the teams that build and maintain them. Due diligence must evaluate whether technical leadership possesses the experience to operate models in production environments, not merely design them. 

This assessment goes beyond resumes. Structured technical interviews, code walkthroughs and independent expert validation are essential. Teams that understand the tradeoffs between model complexity, explainability, cost, and reliability consistently outperform those optimised solely for benchmark performance. 

Conclusion 

AI has redefined what technical diligence must accomplish. It is no longer sufficient to confirm that systems function as advertised. The mandate now is to determine whether AI capabilities are defensible, governable and scalable under real-world conditions. 

Organisations that apply disciplined, evidence-based diligence across code architecture, model governance and data economics gain a decisive advantage. They avoid commoditised assets, accurately price risk, and identify platforms capable of compounding value over time. In an investment landscape increasingly shaped by AI, rigorous technical due diligence is no longer a sufficient safeguard against risk. It is a source of alpha.