How to get the most value from GenAI among portfolio companies

Prioritizing the right use cases

GenAI can be applied across the business and leaders are inundated with long lists of use cases. However, these alone are not helping businesses identify and realize value. The most advanced firms are developing frameworks that answer two questions:

• What value (in terms of improving profitability, operational efficiency or quality) will this generate?
• How feasible is it to implement from a data, tech architecture and governance perspective?

For many, this means implementing and innovating quickly to generate high value wins early. We are beginning to see many use cases transforming operations: Software companies are seeing upwards of 30% improvement in efficiency of development; consumer companies are transforming engagement with customers through personalization; and companies from all sectors are improving the employee experience via GenAI integration into central services such as HR, talent or finance.
 

All these successful deployments are marked by the degree of rigor with which they approach value identification in terms of monitoring KPIs effectively as well as understanding and acting on the implications for the business model going forward.

Building the right foundations

In our experience, the PE leaders best positioned to succeed with GenAI are those who are acting now on the quality of data and talent as well as the ethical and responsible use of GenAI.

• The importance of high-quality data

A retailer, for example, can transform sales growth by using AI to personalize marketing offers. The success of that use case will depend on the quality of the customer data used to train the AI model. How to best ensure the collection and maintenance of high-quality, relevant data becomes a critical question.

Where this does not happen, it can be a fundamental barrier to transformation. For example, an AI solution for detecting water leakage, tested by several UK companies, provided almost 90% accuracy and weeks of warning. However, poor data quality from network and ERP sources lowered the achievable benefits by nearly 30% for one company, incurring a significant opportunity cost measured in tens of millions.
 

• The role of talent and culture in AI adoption

GenAI will thrive in a business that is ready to embrace innovation. Fostering a culture in which people are comfortable with and adaptive to AI-driven change will take sustained effort. Healthcare companies, for example, can use GenAI to deliver more accurate and impactful patient data analysis: This investment will deliver the best returns when skilled healthcare professionals see its value.

Neglecting talent and culture can result in a perception of GenAI as a job threat, instead of a tool for enhancing personal productivity, accelerating processes and boosting creativity. Without understanding GenAI's potential, many may just ask for slightly improved existing solutions, missing out on significant innovation.
 

• The imperative of taking a clear position on ethical and responsible use of GenAI

This is critical to ensuring the confidence needed to develop sustained value from AI. For example, financial services companies successfully using GenAI to risk-assess customers are ensuring their use of AI is transparent and free from bias. They are creating guardrails and guidelines to ensure their use of GenAI applications is ethical today, and that it remains aligned with new regulatory standards and customer expectations as they evolve.

Infusing ethics into AI data governance is crucial, especially when leveraging public large language models (LLMs) in corporate settings. It’s key to ensure the output generated, reflecting your brand, is unbiased, apolitical, and adheres to key AI ethics standards like those from the US National Institute of Standards and Technology (NIST), the International Organization for Standardization (ISO), the Organisation for Economic Co-operation and Development (OECD) and the EU AI Act.

GenAI changes value creation

GenAI is more than an enabler; it will fundamentally change the way firms create value. It is improving efficiency in existing portfolios and changing the types of companies that PE firms target.

This requires a strategic pivot, with visionary thinking, a practical approach to assessing value-creation opportunities, and strong foundations. Then GenAI can be turned into a strategic advantage across the portfolio.