AI has rapidly transformed the private equity sector and the broader investment landscape, leaving many firms eager to unlock its full potential. And it’s clear to see why.
AI has a remarkable ability to summarise, synthesise, infer and create new insights from vast pools of unstructured data. Consequently, complex investment thesis that might have previously taken months can now be completed in a matter of hours, sometimes minutes.
There has been an increase in PE firms experimenting with the use cases of AI in their transactions and trading strategies. Over the next five to seven years, Deloitte predicts that 25% of PE firms will be using AI to augment their portfolio valuations.
It is, to put it plainly, a very exciting time to be in PE. However, AI-enabled tools such as predictive analytics are often the end point of the journey for many firms – not the start. Getting to the position where an organisation can fully realise the benefits of AI is a process that throws up a number of challenges and opportunities along the way.
A misstep can not only hinder the effectiveness of a PE firm’s overall AI strategy but could also negatively impact the bottom line. With that in mind, here are some of the most common mistakes and how to avoid them:
Absence of a clear initial objective
Such is the scope of impact advanced data analytics techniques can have on a PE firm, that it can be difficult to nail down a precise ultimate goal. Without a definitive objective, AI transformation projects can suffer from mission creep, cost and time overruns and missed opportunities. The best approach is to start with the main problem you want AI to solve. Keep the goal concise, achievable and measurable. For example, enhance the efficiency of your due diligence processes. From there you can move on to defining the questions you need your data to answer to enable this to happen. This will then make it clear what tools, processes and expertise will help you on the way to achieve that goal.
This may all sound very simple, but, according to Harvard Business Review, the failure rate of AI projects is as high as 80%. A leading cause is an absence of defined objectives that align with an organisation’s overall commercial strategy. Too many organisations see the adoption of AI tools across their business as an objective in of itself rather than a means to facilitate a specific improvement.
A shaky data foundation
High-quality data is the basis of any successful AI integration, fuelling everything from decision-making processes to predictive modelling and beyond. By the same token, a poor quality data foundation – characterised by inconsistent, fragmented, outdated, or even biased data – can seriously undermine even the most advanced AI system. In some cases, it can lead to misinformed decision-making with dire consequences.
To combat this, PE firms must take the time to ensure they have a well-defined data management strategy in place. This should not only provide a clear roadmap for leveraging data to drive performance, innovation and AI readiness but also establish data governance procedures which prioritise data quality and security.
Investing in the right platforms to collect, manage and analyse data is absolutely fundamental. This tech infrastructure must be accessible to every team and integrate into all your existing platforms in order to avoid the siloing of data. For many business leaders the nitty gritty of data management can feel, dare I say it, boring compared to the exciting potential of AI. As a result, it can be ignored or suffer from underinvestment. Yet, without these building blocks in place nothing meaningful can follow. It is critical to take your time to understand exactly what state your data infrastructure is in and which available technology platforms will meet your requirements now, and in the future. Conducting a data audit should be the very first step of any meaningful data strategy.
Failure to upskill
AI integration in PE firms isn’t just about technology – it’s a cultural shift that requires firm-wide upskilling. One of the most critical missteps PE firms often make is underestimating the importance of data literacy across the business. Even now, it’s striking how many senior leaders struggle to interpret their own core business data – relying instead on a small group of data scientists or analysts. The reality is AI’s impact will always be limited if only a few individuals truly understand and can challenge the data driving decisions. You can’t act on what you don’t understand – and secondhand insights, no matter how advanced, are no substitute for your own personal analysis.
Therefore, in addition to hiring experts, developing data and AI literacy across the entire firm is critical. Research shows that the skills gap is a key barrier to deploying and benefitting from AI tools. Whether it’s someone in the investment team, legal, compliance, or finance, the ability to understand, interpret, and apply data-driven insights is now essential for making stronger, collective decisions. To this end, PE firms must not only invest in online training and continuous learning but also ensure that AI is aligned with professional goals and embedded into day-to-day workflows. This is especially important in high-impact areas like value creation, investment analysis, and operational efficiency.
Missing guardrails
As with all exciting disruptions, the increased reward is mirrored by increased risk; PE firms should be simultaneously assessing the commercial opportunities to leverage AI, as well as the risks it poses such as data privacy breaches and algorithmic bias. Across industries, we’ve seen that poorly governed AI can backfire, leading to legal liabilities, regulatory scrutiny, and reputational fallout. In the context of PE, such missteps can translate directly into diminished portfolio value. Moreover, regulators across all major economies are rapidly introducing legislation that penalises misuse or lack of transparency in AI systems.
Robust oversight is essential, beginning with clear guidance that outlines the purpose of AI adoption and explicitly defines what is – and is not – okay. This should be reinforced by practical safeguards and defined procedures so that employees understand their responsibilities, can identify irregularities, and know who is accountable for taking corrective action. Rather than being left as an afterthought, getting these structures in place should be established as a strategic priority from the outset of any AI initiative to ensure alignment, compliance, and long-term value protection.
Thinking short-term
It’s also important to note that AI integration is not a once-and-done exercise. The AI landscape is continually shifting in new and often unexpected directions, bringing both opportunities and challenges. Knowledge can be easily lost or become obsolete. Governing policies and working procedures can become rapidly outdated.
To fully leverage AI, PE firms must embrace a culture of continuous learning and improvement. Holding annual or, ideally, bi-annual training sessions for your team helps sustain this mindset. Additionally, conducting regular audits and policy reviews ensures alignment with best practices and emerging risks.
Think of AI as an ongoing journey. Start by allowing employees to experiment with small pilot projects to evaluate their impact. Set clear KPIs for each initiative and conduct frequent reviews. Even on a small scale, these projects will provide valuable insights that can inform larger efforts. To support your progress, it’s always advisable to consider partnering with a specialist data consultancy with experience that can help support your goals.