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  • Writer's pictureChelsea Wilkinson

Value creation & risks: Generative AI for private equity managers and portfolio companies

Generative AI is an exciting and transformative technology. It presents significant value creation opportunities but also inherent risks for private equity managers and their portfolio companies. Actually, for all companies!

As private equity firms, specifically, navigate the AI landscape, it's crucial they carefully assess the potential benefits and challenges of integrating generative AI into their investment strategies.

Below are some of our immediate observations:


  1. Enhanced customer engagement: Generative AI enables personalised and immersive experiences for customers. Private equity portfolio companies can leverage this technology to create tailored content, interactive campaigns, and product recommendations, ultimately boosting customer engagement, loyalty, and revenue growth.

  2. Streamlined operations and efficiencies: Generative AI offers powerful optimisation capabilities. By automating processes, optimising resource allocation, and enhancing supply chain management, private equity-backed companies can improve operational efficiencies, reduce costs, and gain a competitive edge.

  3. Accelerated innovation and product development: Generative AI fuels creativity and innovation. It can aid in rapid prototyping, design iteration, and idea generation, empowering portfolio companies to bring novel products and services to market faster, driving differentiation and market share expansion.

  4. Data-driven decision-making: The abundance of data in today's business landscape can be harnessed with generative AI. Private equity managers can leverage this technology to analyse large datasets, derive actionable insights, and make more informed investment decisions, increasing the probability of successful outcomes. So can their portfolio companies.

  5. Unlocking untapped opportunities: Generative AI can uncover hidden opportunities and new revenue streams. By exploring its potential in healthcare, sustainability, content generation, art and other creative domains to name just a few, private equity-backed companies can tap into unexplored markets and revenue sources.


  1. Ethical and legal challenges: Generative AI raises ethical concerns, such as misinformation, deepfakes, and unintended bias. Private equity managers must establish robust ethical guidelines, ensure compliance with legal frameworks, and navigate potential reputational risks associated with misuse or unintended consequences of generative AI technology.

  2. Data privacy and security: The integration of generative AI requires handling vast amounts of data. Private equity managers and their portfolio management teams must prioritise data privacy and security, implementing rigorous measures to protect sensitive information and comply with data protection regulations.

  3. Adoption and talent gaps: Integrating generative AI demands specialised expertise. Private equity firms need to assess their teams' capabilities and those within portfolio companies, identify skill gaps, and invest in upskilling or partnering with experts to successfully leverage the technology's potential.

  4. Transparency and explainability: Generative AI systems can sometimes operate as black boxes, making it challenging to understand their decision-making processes. Fund managers and management teams should strive to ensure transparency, explainability, and accountability to gain trust from stakeholders and regulatory bodies.

  5. Integration with existing processes: Adopting generative AI may require modifying existing workflows and integrating with legacy systems. PE firms and their portfolio companies need to carefully plan and execute the integration process to minimise disruptions and maximise the technology's value.

  6. Data foundations: Lastly, but arguably the first thing to tackle, it must be noted that many portfolio companies lack the data infrastructure and computational power to leverage generative AI. In the immediate short term, they must focus on:

    • cleaning and carefully labelling existing data (and potentially sourcing new, third-party data);

    • building robust, flexible and scaleable data storage and infrastructure;

    • drafting and enforcing tight data governance and rule layers; and

    • creating data models which accurately reflect their business

to enable them to fully realise the power of any AI – generative included.

After all, you need to walk before you can run and the old adage: "rubbish in and rubbish out" most certainly applies.

Embracing generative AI requires a thoughtful and strategic approach. Private equity fund managers should actively engage with data and technology experts, plus AI practitioners to assesses AI readiness, identify the most promising use cases, and navigate potential challenges. It is essential they and their portfolio companies address considerations such as data privacy, ethical implications, and aligning AI initiatives with the overall business strategy.

Only a thoughtful and strategic approach, coupled with continuous monitoring and adaptation, will pave the way for successful and responsible implementation.

Meaning, starting small and deliberately is vital to avoiding wasted investment and wasted time.

It's so easy to be attracted by the ‘flashing lights’ and excitement of generative AI. After all, it's super cool! But, we urge private equity managers and their management teams, to deeply assess the business case first, plus their company's readiness and data foundations, before over committing and under delivering on generative AI's promise.


Is your company ready to embrace AI?

Does it have the data, capabilities, people, and infrastructure?

We can help you assess these, identify risks and opportunities, plus create roadmap to effectively close those gaps. Give us a call!


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