Over a third of businesses claim to use AI solutions effectively or see them as a major competitive advantage. Yet the reality behind those claims can vary widely. For investors, the key is to look beyond the headline narrative to understand the underlying value, maturity, and innovation within a target’s AI capability and whether that capability is translating into measurable operational and commercial impact that can sustain growth and differentiation over time.
The pace of AI advancement is exponential, with each generation of models building rapidly on the last. As its capabilities expand, it now encompasses a wide range of applications.
Narrow AI
Focused systems designed to perform specific tasks such as recommendation engines, chatbots or fraud detection. These rely on techniques such as machine learning, deep learning, and natural language processing (NLP).
Generative AI and large language models (LLMs)
Systems trained on vast data sets to create content such as text, code or analysis. LLMs are the text-based subset of generative AI and can be powerful for automation and communication, but are also prone to generating inaccurate or fabricated outputs – a phenomenon known as hallucination.
Retrieval-augmented generation (RAG)
An emerging approach that grounds model outputs in verified, real-time data sources to improve reliability and reduce hallucination risk.
Advanced AI research areas
These include theory of mind AI, which explores whether systems could one day interpret human intentions, and self-aware AI, which considers the possibility of machine consciousness. Both remain theoretical, with no commercially viable applications today.
Taken together, these technologies are giving rise to a new generation of AI agents– autonomous or semi-autonomous digital workers that can understand context, make basic decisions, and perform defined business tasks. By combining the reasoning power of generative AI, the reliability of RAG, and the structured process discipline of automation tools, AI agents are beginning to extend automation beyond routine workflows into areas of judgement and knowledge work. For investors, this marks a practical intersection between today’s proven automation and the emerging frontier of intelligent systems – an area where capability maturity and integration strategy will increasingly determine enterprise value.
Validating technology value in due diligence
As AI becomes embedded across business operations, technology due diligence has to evolve. The task is no longer to simply confirm what exists, but to test the depth, quality and governance of the capability described as AI and how integral it is to enterprise value. A closer look often shows that what's labelled as AI relies on established automation or analytics rather than true intelligent systems, making it essential to understand what differentiates the technology and where value is created.
Building on the taxonomy outlined earlier, from narrow AI to generative and RAG approaches, diligence assesses what genuinely qualifies as AI, where it sits within the technology stack, and how material it is to the company’s proposition. Once the nature of that capability is clear, the assessment broadens to the fundamentals that apply to any technology-enabled business. This includes how data is sourced and governed, how securely and efficiently systems are integrated, and whether the overall architecture can scale. The objective isn't to test algorithms in isolation but to understand how AI-enabled systems perform within the wider environment and how well they're managed and controlled.
AI introduces new layers of consideration across familiar diligence themes. Data provenance, intellectual property rights, cybersecurity and dependency on third-party platforms all influence resilience, cost and compliance. In AI environments, cybersecurity focuses as much on data and model integrity as on infrastructure protection. It demands controls that safeguard training data, manage open-source components responsibly, and reduce third-party risk across the stack. Evaluating how the business manages these factors through governance, controls and supplier oversight provides insight on the discipline and maturity of its technology management. That discipline often matters more to the long-term value than the sophistication of any single model on its own.
Regulatory awareness is becoming a defining factor. Frameworks such as the EU AI Act are formalising expectations around transparency and accountability in how AI is developed and used. Diligence should test whether the target understands these obligations and has the flexibility to adapt as regulation and guidance continue to evolve.
Scalability, cost efficiency and people capability remain central. Investors should consider whether the technology environment can scale with demand without eroding performance or margins. Are dependencies on vendors or hyperscalers (large cloud providers such as AWS, Azure, or Google Cloud) well managed, or do they create operational or cost risk? And does the business have the right expertise and partnerships to sustain and evolve its AI-enabled operations over time?
Validating future scalability and innovation
Beyond current capability, investors need confidence in where the technology is heading. A clear, well-governed roadmap shows whether innovation is focused, commercially aligned and capable of responding to regulatory and market change.
AI-driven development is often iterative and experimental, which makes the roadmap a practical indicator of maturity. The strongest plans translate technical ambition into measurable business outcomes – enhancing customer experience, improving efficiency or unlocking new revenue streams. Diligence should consider how effectively the roadmap balances innovation with delivery, ensuring that experimentation is supported by structure and that investment priorities link directly to enterprise value.
The roadmap also reveals how a company manages its innovation risk. Investors should look for evidence of governance mechanisms that anticipate issues such as data quality, bias, cybersecurity, and ethical compliance before they affect users or reputation. The most credible roadmaps build these safeguards within their design and development process from the outset, ensuring innovation remains both responsible and resilient, and that governance and trust are reinforced as the technology evolves.
Because AI technologies evolve rapidly, a roadmap can't be static. It should be agile, informed by feedback, and flexible enough to absorb advances or shifts in regulation. The test for investors is whether that flexibility would also allow the technology to integrate smoothly within a wider platform or be separated efficiently as exit, supporting different portfolio strategies without loss of value or control.
Ultimately, a strong roadmap connects today’s proven capability with tomorrow’s commercial opportunity. It provides visibility on how innovation will be governed, scale and monetised-ensuring that technology remains a reliable driver of value rather than a source of uncertainty.
The rise of AI has redefined what investors look for in technology, but the fundamentals remain unchanged; value comes from capability, not claims. In diligence, the focus must be on how intelligently AI is designed, governed and applied to create measurable impact. Those who can look beyond the narrative and assess where technology truly adds resilience and scale will be best positioned to capture the opportunity with clarity and confidence.
For more insight and guidance, get in touch with Aislinn Mae.
