Key AI and ML levers: Strategies and expectations
1. Process automation
Low-code and no-code methods, such as robotic process automation, have existed for over a decade. These tools allow teams to automate manual, repetitive tasks and reduce lower-value personnel. Firms aiming to reduce IT and finance operating model costs are now seeking solutions with clear business use cases and a return on investment instead of more advanced, nascent AI/ML capabilities like ChatGPT.
HIGHSPRING EXPECTATION
Private equity firms, particularly those with less in-house data and AI/ML capabilities in the lower middle market, will adopt process automation as one of their primary value creation levers, particularly given the impact on revenue and earnings. The adoption of these capabilities will also lead to potential nearshore and offshoring opportunities for portfolio companies, with simplified finance and IT processes.
2. Data analytics
Advanced analytics tools like Alteryx enable portfolio companies to assess structured and unstructured data to find hidden patterns or generate predictive analytics. The low cost of these tools, along with the increased investment in data science teams at private equity firms and portfolio companies, has enhanced their value in revenue-generating capabilities like sales and go-to-market planning and finance reporting costs.
HIGHSPRING EXPECTATION
Given the lower barrier of entry compared to more advanced AI/ML capabilities, we expect that many private equity firms will continue expanding their in-house data science capabilities. Given increased margin pressures, portfolio companies will adopt better master data management strategies to accelerate their ability to identify low-margin or time-intensive activities to remediate through data analytics.
3. Artificial intelligence and machine learning
The pace AI/ML has accelerated has necessitated that private equity firms place additional focus on prioritizing the right use cases like automating customer service responses versus integrating AI/ML on top of more advanced or emerging large language model capabilities like ChatGPT. The fundamental limitations private equity firms will face are the lack of in-house AI/ML talent, high start-up costs, AI/ML tools with unproven track records, potential data and security concerns, and ROI.
HIGHSPRING EXPECTATION
The biggest adopters of AI/ML and GenAI will be deal teams. Aiming to improve the quality and speed of their deal sourcing and diligence processes, these teams will seek out technology operating partners to enhance product and code development processes and identify underlying margins in their portfolio companies.
