As artificial intelligence technologies reshape industries, investors considering investments in AI-driven companies should tailor their due diligence accordingly. There are many areas smart investors should evaluate – from technical integrity and data handling, to regulatory readiness, and even environmental impact. We set out 10 focus areas investors should consider.

Clarity of the AI value proposition

When evaluating a company that has made a material commitment to AI, it is essential to understand precisely what AI does within the organisation. Is it a marketing tagline or a genuinely embedded capability delivering
tangible results? Investors should seek a detailed breakdown of where, how, and to what extent AI is being used. This should include an understanding of the model scope and function (what specific problems the AI solution addresses and how central it is to the company’s value proposition) and its quantifiable impact (whether there are clear metrics that demonstrate that the AI component is a meaningful driver of performance).

Data integrity, quality, and governance

AI performance is only as good as the data it is fed. The quality, structure, and origins of that data will determine the reliability and fairness of the outputs. Data quality issues can result in biased predictions, flawed insights, and reputational damage. Investors should examine where the data comes from, who owns it and how difficult would it be for competitors to replicate it. Investors should also look for how vigorously the data is validated, normalised and maintained.

The company should adhere to data privacy laws and industry regulations, ensuring compliance with frameworks like GDPR or emerging AI-specific policies. The investor should have a thorough understanding
of the company’s internal policies and practices regarding the collection, processing, storage and transfer of any
personal information, and the company’s privacy and information security measures.

Scalability and deployment infrastructure

Building a high-performing model in a lab environment is one thing. Deploying it at scale across geographies, customer segments, or product lines is quite another. Investors should:

• examine whether the company is AI reliant on a particular cloud provider or specialised hardware and if so, what the cost and scalability implications are. Hardware dependencies can limit growth if they can’t easily be scaled or replaced. 
• assess the ability of the company’s technology to integrate seamlessly with CRM systems, ERP platforms, or e-commerce engines. AI solutions rarely operate in isolation and smooth integration is often a marker of the company’s maturity and operational sophistication.
• examine how readily the company can be retrained and recalibrated as market conditions shift, consumer behaviours evolve, or competitors improve their own AI.

Technical robustness and cybersecurity

For AI investments, technical diligence extends beyond model accuracy. Robust engineering practices and security measures must also be in place. All too often, companies tout their AI credentials without ensuring that their solutions are robust and stable. Investors need to probe the technical underpinnings of the AI system and should look for stable and well-supported teams, clear documentation practices, and evidence of active model
monitoring, testing, and performance tuning, as well as resilience to cyber risk.

Ethical, legal, and regulatory landscape

As AI matures, so do regulatory frameworks aimed at preventing the misuse of the technology. For instance, the EU’s AI Act, along with evolving standards in other jurisdictions, will require certain levels of transparency, explainability, and human oversight. Investors must assess whether the company’s products and processes can
meet current demands and remain agile enough to adapt as regulations evolve.

The reputational risks associated with unethical AI usage can be significant. The best AI investments often lie in firms that have integrated an ethical lens into their development processes. Checking for robust policies on bias mitigation, fairness, transparency, and interpretability of AI models can indicate long-term sustainability and brand resilience.

Investors should also understand what safeguards and remediation frameworks are in place in the event the AI makes decisions that cause harm (risk assessments, insurance coverage, and compliance audits are essential here).

As AI matures, so do regulatory frameworks aimed at preventing the misuse of the technology.

Intellectual property and competitive differentiation

A company that can guard its AI technology or models via patents, trade secrets, or proprietary data sets may
hold a competitive advantage. Investors should consider whether there are welldocumented patents, a unique algorithmic approach, proprietary training data, or specialised model architectures that create a significant barrier to entry. Given AI evolves rapidly, companies that don’t continue to innovate can quickly be overtaken. Investors should accordingly look for evidence of ongoing R&D initiatives and incentives that encourage internal innovation.

Talent retention and organisational culture

AI-savvy professionals – data scientists, machine learning engineers, and AI ethicists – are in high demand. Losing key staff can derail even the most promising AI ventures. Investors should confirm that talent is being
appropriately managed and incentivised and that teams are cross-functional, collaborative, and productive.

Environmental and sustainability considerations

Sustainability is no longer a peripheral concern. Many investors now consider ESG factors integral to evaluating long-term value. Training large-scale AI models can be energy-intensive, particularly if they rely on extensive computer resources. Investors should look for metrics on energy usage and strategies for optimising model
training efficiency. Companies that adopt greener hardware options, renewable energy sources, and efficient data centres can position themselves favourably in an increasingly eco-conscious market. 

Global competitive landscape and geopolitical context

AI is a global race, influenced by regional regulations, talent pools and national interests. Investors should:

• Consider how different jurisdictions (EU, US, Australia, China) may impose diverse standards on AI transparency, data transfer, or algorithmic accountability. International compliance complexity can affect scalability and operating costs. 
• Evaluate how well the company can navigate global challenges including restrictions on AI technology exports, visa limitations for skilled engineers, and shifting trade policies

AI performance benchmarking and transparency

With AI’s complexity, comparing performance across different companies or sectors can be challenging. Investors need consistent benchmarks and trustworthy evaluations. Sector-specific benchmarks or recognised industry tests will be helpful to gauge relative standing, while the third-party AI audits or certifications can confirm that a company’s models meet certain standards. As AI-based decisions increasingly impact customers and clients, the importance of explainability grows. Transparent models that customers and regulators understand and trust may fare better in the long term.

In this evolving landscape, identifying promising AI investments demands a holistic lens – one that integrates technical competence, data stewardship, robust governance, and forward-looking ethics. By evaluating AI-specific financial ratios, scrutinising cybersecurity measures, considering global regulatory nuances, weighing sustainability factors, and insisting on meaningful performance benchmarks, investors can better discern which enterprises are genuinely harnessing the transformative power of AI. The result is a more strategic, informed, and future-proof approach to investing in companies that will shape – and reshape – our AI-driven world.