Artificial intelligence is reshaping the deal economy faster than most businesses can comfortably absorb. For private equity firms and their portfolio companies, the challenge is no longer whether AI matters. It is how to protect value, uncover new sources of advantage, and avoid being caught on the wrong side of a structural shift.
The noise around AI is unhelpful. Some claim it will wipe out jobs and commoditise software. Others treat it as a shortcut to exponential growth. In practice, neither extreme tells investors or operators what they actually need to know.
AI is not eliminating the need for good businesses. It is changing what makes a business valuable.
That means the questions for dealmakers are becoming more specific. Where is value now being created? What becomes more defensible as models commoditise? What risks could materially weaken a business? And what capabilities will separate businesses that adapt from those that fall behind?
A lot has changed in a short period of time. But some fundamentals remain stubbornly important.
Businesses still need strong leadership. They still need customer relevance. They still need a clear market position. What AI changes is the speed at which weak differentiation gets exposed.
For years, software businesses could create value through feature breadth, workflow complexity or technical novelty. That is becoming harder to defend. As foundational models become more accessible, the technology layer alone is less likely to be the moat.
The new question is not “do you use AI?”
It is “what do you have that AI alone cannot replicate?”
That is where value is shifting.
One of the clearest shifts is the growing strategic importance of proprietary data.
Many businesses have always had valuable data. What they lacked was a practical way to reason over it, structure it and convert it into useful outcomes at scale. AI changes that. It allows organisations to interrogate data in natural language, identify patterns faster, and turn previously underused information into product capability, operational insight and customer value.
That makes high-quality, proprietary data significantly more valuable than it was even two years ago.
For portfolio companies, this includes more than customer records or transaction history. It can include:
As models become more interchangeable, the real advantage lies in the quality, relevance and uniqueness of the data sitting behind the experience.
Alongside data, proprietary knowledge is emerging as a major source of defensibility.
Many organisations are sitting on valuable knowledge assets hidden across policies, documents, service interactions, internal playbooks, support histories and expert workflows. The opportunity is not simply to store that knowledge, but to structure it and apply it in useful contexts.
That is where AI can create outsized value.
Businesses that can turn fragmented knowledge into embedded capability — inside products, workflows and decision-making — are doing more than adding AI features. They are building differentiated intellectual property.
For investors, this matters. A business with real proprietary context is in a very different position from one simply layering a model on top of publicly available information.
One of the more useful ways to think about AI is that it is replacing tasks, not replacing entire jobs. That distinction matters.
Most roles consist of a mix of repetitive work, analytical work, judgment, communication and accountability. AI is already proving useful in reducing lower-value, time-consuming work. It is much less effective at replacing the context, responsibility and judgment that sit around it.
This is why the best AI adoption stories are often more grounded than the headlines suggest.
The real gains are showing up in:
That is valuable because it removes toil and frees people to focus on higher-order work. But it is not the same as replacing the human layer altogether.
For private equity-backed businesses, the implication is clear: the goal should be to redesign work around leverage, not chase simplistic narratives about headcount elimination.
The most effective uses of AI are rarely the most theatrical.
In many businesses, the strongest results are not coming from broad attempts to automate the entire organisation. They are coming from AI embedded inside vertical software, existing workflows and domain-specific tasks.
That might mean:
This is an important point for investors and operators alike. The market often talks about AI as if value is created through general-purpose capability alone. In reality, much of the most practical value is being created through focused 'AI injections' into tools and processes businesses already rely on.
That is often where competitive advantage becomes tangible.
As AI lowers the cost of building features, understanding customers becomes even more strategic.
Businesses that know their users deeply, fit solutions into actual workflows, and solve real pain points are better positioned to defend value than those relying on generic capability. Large platforms may have scale and broad tooling, but they do not automatically have the same proximity to the customer problem.
That is why old-school business fundamentals still matter.
Customer intimacy, trusted relationships, responsiveness and service quality are not outdated in the AI era. In many markets, they are exactly what prevent commoditisation.
For portfolio companies, this should be reassuring. AI may change how products are delivered and how teams operate, but it does not eliminate the advantage of being closer to the customer than larger, less specialised competitors.
If AI creates new opportunities, it also creates new forms of risk.
For many businesses, especially those in regulated or high-stakes environments, the biggest challenge is not whether AI can automate something. It is whether that automation can be trusted, understood and governed.
This is particularly relevant when businesses start using agentic models or more autonomous workflows. Autonomy sounds attractive, but black-box behaviour introduces real risk. If a company cannot explain how an AI system reached an answer, what checks were performed, or where accountability sits, that becomes a governance problem very quickly.
Guardrails therefore need to be treated as a strategic necessity, not a compliance footnote.
That means putting in place:
The more embedded AI becomes, the more important these controls are. Speed without traceability is not innovation. It is exposure.
From a technology perspective, one of the biggest mistakes businesses make is assuming AI can compensate for weak foundations.
It cannot.
AI amplifies whatever environment it enters. If the underlying systems are fragmented, the data is poor, the codebase is brittle, or engineering practices are weak, AI can accelerate the wrong outcomes. It creates more output, but not necessarily more value.
This is especially visible in software delivery.
AI can significantly improve engineering productivity. It can support coding, testing, review and iteration. But without modern architecture, small controlled change sets, disciplined review practices and shared technical understanding, it can also create larger, less understood risks at speed.
At Codurance, we often frame this simply: code is data. If that data is messy, AI creates more mess.
That is why modernisation and AI strategy belong together. Businesses that want real, repeatable value from AI need the right software foundations beneath it.
For private equity firms, AI is now part of both the upside case and the risk case.
It should influence how businesses are diligenced, how value creation plans are shaped, and how exit readiness is assessed.
That means looking beyond surface-level claims and asking harder questions:
Traditional growth metrics still matter. But on their own, they are no longer enough to understand whether a business is positioned to defend and grow value in an AI-enabled market.
At Codurance, we see AI creating the greatest impact when it is tied to strong software engineering, modernisation and disciplined execution.
That is why our work with investors and portfolio companies focuses on more than experimentation. We help organisations assess AI readiness, modernise legacy systems, build AI-enabled software responsibly, and establish the guardrails needed to scale with confidence.
Because in reality, AI is not a shortcut to value. It is a force multiplier.
And like any multiplier, it amplifies what is already there — good or bad.
The businesses that will create the most value in the years ahead are not the ones with the noisiest AI messaging. They are the ones with the clearest differentiation, the strongest foundations, and the discipline to turn AI into measurable commercial advantage.
The AI storm is real. But it is not a reason for panic. It is a reason for clarity.
For private equity firms and their portfolio companies, the path forward is not about chasing every new model or making sweeping automation claims. It is about understanding what is uniquely defensible, where AI can unlock practical value, and what controls are needed to protect the business as it evolves.
The winners will be those that double down on proprietary data and knowledge, stay close to customer needs, modernise their technology foundations, and apply AI with discipline rather than theatre.
If you are assessing AI adoption, preparing for diligence, exit or strengthening a Value Creation Plan, technology strategy must be directly aligned to enterprise value outcomes.
For over a decade, Codurance has partnered with private equity firms and their portfolio companies to unlock and accelerate enterprise value through AI-driven technology, while proactively identifying and mitigating technical risk.
Across the investment lifecycle, we help businesses modernise legacy platforms, implement scalable, and resilient architectures, and build high-performing engineering capabilities through Software Craftsmanship. The outcome is faster delivery, lower technical risk, stronger operational performance, and a more resilient, exit-ready asset.
If you'd like to find out more, get in touch with us today.
The difference lies in whether AI is a core pillar of the business model or just a performance enhancer.
In an era where AI models are becoming interchangeable commodities, the real value shifts to the unique data sitting behind the experience. Defensible moats are built on:
Historically, software companies won by having the most features or the best user interface. Today, AI makes building features and complex workflows much cheaper and faster, which erodes traditional technical moats.
Natalie Gray is Director of Marketing & Growth at Codurance, a global AI-first software engineering consultancy that supports businesses and their investors to drive value through building and modernising sustainable software and platforms at all stages of the investment lifecycle. With more than 20 years in the tech industry, Natalie believes the power to innovate is accelerated when people are able to try new ideas, fail fast and collaborate. This is why, to her, community is at the heart of her approach to aligning business and technology outcomes.