AI/ML Diligence and Practices
With the advent of generative AI, private equity firms have added artificial intelligence, machine learning, data maturity and automation scalability to their assessment checklists for target businesses. Since these assessments involve evaluating a target’s non-IT commercial and operational AI/ML capabilities, which require a separate workstream from traditional IT and cyber diligence reviews, the value and importance of AI/ML is dramatically heightened across all organizational functions.
With tech-focused private equity firms adopting an outside-in diligence approach to benchmarking AI/ML, data organization and learning language model maturity will becoming increasingly relevant to determining the investment required post-close. To ensure private equity firms and portfolio companies receive favorable valuations in current and future markets, teams must embed a comprehensive AI/ML due diligence approach and leading practices into their organizations, for which a strategy is outlined below.
The AI/ML Diligence Approach
A cross-functional approach is the best method for evaluating the technology, talent, compliance, ethics, biases and business aspects of AI/ML. A firm must consider the complexity of the AI/ML models, data curation and optimization, and internal AI/ML standards and processes. Measuring the AI/ML maturity of a potential target covers several interdependent areas, each relevant to the previous for operational success.
This approach allows businesses and private equity firms to develop comprehensive frameworks for evaluating and growing their AI/ML processes for current and future market shifts.
These are the five vital areas private equity firms should consider when evaluating a target:
AI/ML Readiness:
During the diligence process, a key criterion for a portfolio company’s readiness is the scalability of an organization’s cloud and AI/ML infrastructure. Through a detailed review of the organization’s current talent and capabilities, current data, cloud architecture, current usage of AI/ML and data management tools, an assessment can determine their present and future capabilities.
Legal/Compliance Readiness:
There have been increasing concerns around Personally Identifiable Information, Intellectual Property and other sensitive data used for AI/ML activities that might not comply with specific regulations such as the General Data Protection Regulation or California Consumer Privacy Act. Assessing whether companies have existing guardrails, such as responsible AI/ML frameworks, and identifying critical gaps to address in order to build this framework and implement appropriate AI/ML usage guardrails will be crucial for maintaining compliance.
Data/Model Quality and Governance:
For companies with existing AI/ML capabilities, the data used to train and test AI/ML, including the quality of the master data and any bias in data, needs to be evaluated. The existence of current AI/ML capabilities does not mean a private equity firm will not have to invest significantly in improving AI/ML, particularly if the training datasets will need to be overhauled post-close.
Model Performance and Validation:
Many portfolio companies’ existing AI/ML models perform well during a due diligence but display significant issues post-close since their models’ performance (e.g., F1-score and Receiver Operating Characteristic curves) have not been effectively reviewed versus real-world data. Private equity investors and their IT advisors are now requesting walkthroughs of these models, along with benchmarks against real-world data, to determine the level of investment required to scale these capabilities during the value-creation process.
Scalability and Infrastructure:
Even if a portfolio company’s existing AI/ML models, in-house talent and performance are strong, the ultimate driver of success will be scalability for future growth and acquisitions. During diligence, pressure testing underlying infrastructure and architecture (e.g., cloud services, computing resources and data storage solutions) will determine if it can adapt to future add-ons and the estimated investment required if the current infrastructure is not easily scalable.
Leading Practices
The relative “newness” of AI/ML for most private equity firms means there is a lot of confirmation bias around AI/ML capabilities. The lack of standardized leading practices makes each evaluation an individualized process, ultimately hampering a business’ ability to determine which elements of an AI/ML implementation they should prioritize.
The various elements and factors involved in an AI/ML implementation and the ensuing assessment must be contained within guidelines, or else many businesses risk running into roadblocks in the future. Since AI/ML assessments now factor in scalability for the five-to-seven-year investment horizon in addition to the current state, private equity firms must plan for and determine potential high-risk or high-cost AI/ML situations through the lens of due diligence, a crucial tool for informed decision-making.
Considerations, such as data security/privacy and ethical AI/ML use concerns, must be taken at face value. As AI/ML continues to grow in value and capability, consistent leading practices for compliance and data management must factor into growth plans through an end-to-end AI/ML due diligence framework. In light of anticipated changes in legal and compliance regulations, private equity firms should adopt a rigorous end-to-end assessment as a key best practice to ensure they remain in compliance with the new requirements.
As businesses continue to navigate the evolving landscape of AI/ML within private equity, building robust due diligence and leading practice frameworks will become paramount to success. The need for comprehensive assessments encompassing AI/ML readiness, legal compliance, data governance, model performance and infrastructure scalability grows more urgent as technology and regulatory landscapes shift. Prioritizing these critical elements will enable private equity firms to effectively evaluate potential investments and optimize operations for sustained growth and adaptability in an increasingly AI-driven economy. By embracing these principles, firms will be better equipped to navigate future markets, confidently set priorities and maintain a competitive edge in the AI/ML race.
To learn more about AI/ML in private equity and the impact it has on the M&A lifecycle, read our latest whitepaper, AI’s Impact on the Private Equity M&A Lifecycle. Inside you will find insights on MorganFranklin Consulting’s 2024 AI expectations, key use cases for businesses to leverage AI/ML and our recommendations on how businesses should approach implementing their own AI/ML programs moving forward.
Frequently Asked Questions
Why is AI so important in private equity?
Private equity firms have added artificial intelligence, machine learning, data maturity and automation scalability to their assessment checklists for target businesses, dramatically increasing the value and importance of AI/ML throughout all organizational functions.
How do I implement AI in my organization?
A cross-functional approach is the best method for evaluating the technology, talent, compliance, ethics, biases and business aspects required to implement AI/ML, especially the data curation and optimization necessary for complex AI/ML models.
What are AI leading practices?
The various elements and factors involved in an AI/ML implementation and the ensuing assessment must be contained within guidelines, known as leading practices. As AI/ML continues to grow in value and capability, consistent leading practices for compliance and data management must factor into growth plans.
How MorganFranklin Can Help
Looking to build an effective AI/ML strategy? At MorganFranklin Consulting, we focus on understanding your current state and future goals. Instead of offering generic solutions, we look into the specifics of your data, people and processes to deliver tailored strategies that drive meaningful results.
Contact us today to get started.
