In the current climate of digital transformation, few technologies command as much attention as Artificial Intelligence. Whether it is enhancing operational efficiency, enabling new forms of automation, or underpinning entire product offerings, AI is no longer optional for innovative companies. As such, investors and acquirers are increasingly scrutinising a company’s AI maturity during technical due diligence.
But what does it actually mean to be “AI-ready”? More to the point, what should technical due diligence teams be looking for when assessing a company’s capability to build, integrate, and scale AI systems responsibly and effectively?
Unpacking that question offers a practical framework for understanding AI readiness in the context of technology due diligence. In doing so, it also explores emerging standards such as ISO/IEC 42001 and the UK Government’s recent guidance on responsible AI innovation.
Laying the Groundwork: Data Infrastructure and Governance
AI systems do not operate in a vacuum. Their efficacy is determined in large part by the data they ingest. As such, the foundation of any AI-readiness assessment must begin with an evaluation of the company’s data infrastructure.
Key areas for consideration include:
- Data architecture: Is the organisation’s data centralised and structured in a way that supports machine learning pipelines? Is there a data lake, warehouse, or similar infrastructure in place?
- Data quality: To what extent is the data clean, labelled, and fit for purpose? Are there protocols for handling missing or biased data?
- Data governance: Are there robust policies covering access controls, data retention, and compliance with data protection legislation such as the UK GDPR?
- Operational maturity: Does the company conduct regular audits of its data assets? Is data treated as a strategic asset, or as an operational afterthought?
The answers to these questions often reveal how far along an organisation is in its journey towards AI maturity. Startups that have not yet established clear data ownership or quality controls may struggle to deploy trustworthy AI models at scale.
Beyond Prototypes: The Maturity of AI Models and Practices
Many companies claim to “use AI”, but few do so in a scalable, maintainable, and responsible manner. During a diligence, it is important to go beyond the marketing language and probe the maturity of the AI systems in place.
The following dimensions provide insight into how a company approaches AI:
- Build versus buy: Has the company built its own models, fine-tuned open-source ones, or is it simply consuming commercial APIs such as OpenAI’s GPT or AWS SageMaker? Each approach carries different implications for defensibility and risk.
- Lifecycle management: Are there structured processes for model development, testing, deployment, and monitoring? Is there a CI/CD pipeline for machine learning models?
- Performance tracking: How are models evaluated over time? Are there measures in place for detecting drift, bias, or performance degradation?
- Model governance: Does the company understand and manage the explainability and auditability of its models? Is there documentation supporting how models make decisions?
Organisations that are truly AI-ready will have operationalised their approach to AI, treating it as a living system that must be maintained, governed, and improved continuously.
The Human Factor: Team Competence and Culture
Technology is important, but so is the team behind it. A technically mature AI capability requires interdisciplinary collaboration between data scientists, engineers, product managers, and legal or compliance professionals. In many cases these skills are currently being developed and team members may not be exclusively focused on AI; this will change over time as new, dedicated, roles emerge.
Due diligence teams will ask about:
- Team composition: Is there in-house AI expertise, or is it outsourced? Do the team members have production experience, or are they primarily from academic or research backgrounds?
- Cross-functional alignment: Are AI initiatives integrated into the broader product strategy, or siloed within a research team?
- Understanding of risk: Do the founders and technical leads grasp the regulatory and ethical implications of AI use, such as model fairness, transparency, and the risk of unintended outcomes?
A culture that prioritises responsible innovation is often a stronger predictor of long-term success than raw technical horsepower and, critically, talk more knowledgeably and convincingly of how AI will improve the Product or Service offered by the company.
Strategic Integration: AI as a Product Enabler
Perhaps the most overlooked aspect of AI readiness is strategic alignment. In almost every case AI should not be a bolt-on or an experimental skunkworks project. Rather, it should be tightly integrated into the company’s value proposition.
Questions to explore include:
- Product fit: Are AI capabilities solving a genuine business problem, or are they solutions in search of a problem?
- Competitive advantage: Does the use of AI create defensibility through automation, personalisation, or insight generation?
- Roadmap discipline: Is there a clear strategy for evolving AI features, including stages of testing, release, and iteration?
AI readiness is not about adopting the latest algorithmic trend, but about how the business uses it to execute with clarity, relevance, and rigour.
Regulation, Standards and Risk: A Maturing Landscape
Regulatory scrutiny around AI is rapidly evolving and has intensified in multiple ways. In the UK, the Government’s white paper “A pro-innovation approach to AI regulation” set out principles to guide the development and deployment of AI technologies. More recently, the UK AI Safety Institute has begun publishing technical guidance for assessing large language models and other advanced AI systems.
At the international level, ISO/IEC 42001 has emerged as the first management system standard specifically for artificial intelligence. It provides a governance framework for organisations to manage the development, operation, and lifecycle of AI systems. It complements other standards like ISO 27001 (information security) and ISO 9001 (quality management), but focuses specifically on issues such as:
- AI risk assessment and control
- Transparency and accountability mechanisms
- Ethical guidelines for design and deployment
- Monitoring of model performance and bias
During technical due diligence, signs of engagement with these standards, whether through internal policies, external audits, or certification roadmaps, can indicate a level of operational and ethical maturity that will be increasingly required in regulated industries such as healthcare and finance.
Security and Resilience in an AI Context
AI systems introduce new types of risk that traditional software might not account for. These include adversarial attacks, model inversion, training data poisoning, and hallucinated outputs.
If you are undergoing a Technical Due Diligence you can reasonably expect to be asked about:
- Security controls for model endpoints and APIs
- Privacy measures for sensitive training data
- Robustness testing for model failure modes
- Safeguards to ensure human oversight, particularly in high-stakes applications
The UK Government has signalled that AI assurance will be a priority in the years ahead. Companies that treat security and resilience as first-class concerns, rather than afterthoughts, will be better positioned for regulatory scrutiny and public trust.
Readiness is a Discipline, Not a Buzzword
The ability to adopt and scale AI responsibly is no longer a future-looking aspiration but an active requirement. For investors and acquirers, assessing AI readiness during technical due diligence is essential for identifying both opportunities and red flags.
But AI readiness is not only a question of technology. It encompasses culture, governance, strategic intent, and the ability to operate within a changing regulatory environment.
To be AI-ready is to be execution-ready. It is to understand that artificial intelligence is not a shortcut to growth, but a capability that demands discipline, clarity, and care.
As the regulatory and technical landscape matures, expectations of what it means to build AI that is not only powerful, but principled, are being elevated from side-questions to the primary scope driver for Technical Due Diligence.