How Can PE Firms Engage With AI?: Reflections From Our Recent Investor Conference

Special thanks to Grant Thornton, who partnered with us to create this content.

As is the case in nearly every industry, artificial intelligence or AI is on the minds of many in the private equity sector, including investors, strategic partners, and portfolio company leadership. This was certainly the case at our recent Investor Conference. Over the course of the two day conference, we spent a significant amount of time discussing the opportunities and uncertainties that come with groundbreaking AI advancements.

The topic of AI is expansive and overwhelming, and it’s hard to know where to begin and what guardrails should be implemented. We’re grateful for partnerships with companies like Grant Thornton to create space for dialogue about this technology. Will Whatton, Principal at Grant Thornton shared insights with Investor Conference attendees about how to integrate AI into work in the world of PE.

Defining a Strategy

To kick off his presentation, Whatton mentioned that he always begins conversations around AI by asking clients what they want to do with the technology. By knowing where you want to go and what your goals are, you can work backwards to build a plan to accomplish objectives. A solid plan will work forward toward real business outcomes, making the most of your AI integration while managing risk.

One way to organize these goals is through the lens of defense, looking to impact a company’s bottom line, or through the lens of offense, supporting the top line.

Example AI GoalsExample Use Cases
Risk Management
Cost Management
Provide efficiency
On-Demand Management Reporting
Improve Accuracy in Forecasting
Reduce Human Bias
Improve Budgeting Accuracy
Performance Improvement
Brand Recognition Higher Margins
Performance Management
Design Compensation Plans
Leveraging Internal and External Factors
More Accurate M&A Due Diligence
Prioritize Investment Opportunities

Identify Data Sources

With success defined, it’s time to look at some use cases for AI that will deliver your desired outcomes. As demonstrated in the examples above, both defensive and offensive goals will have their own use cases to help meet these objectives. For example, if our firm was seeking to manage or reduce costs, one use case might be to leverage AI to improve accuracy in forecasting market trends and customer behavior to ensure we stay within our budget. Within that use case, we need to identify which data sources are needed to meet our goals – within forecasting, that could include market trends, historical cost data by month/week/day, seasonality, weather and other data streams. The capabilities of AI may mean you can include data domains you had not previously considered and look for correlations as well.

Assessing Current AI Maturity

Once you define your use case and how you’ll be engaging with artificial intelligence, it’s important to stop and consider your organization’s process readiness and the quality of data you’ll need to leverage AI. It is vitally important to ensure your data is robust and accurate before providing it to any AI tool you choose to use. Grant Thornton’s team works with clients to ensure their data is ready to use, following an AI maturity framework that includes things like policies and procedures. Both the data going into an AI model and the output need to be scrutinized.

As Whatton shared in his presentation, “the worst thing you can do is absolutely trust whatever data comes out” from a generative AI. He recommends that teams work to understand how models were built and where data comes from, to discern what may cause outputs that do not meet expectations. Confidence comes through repetition, validation and processes.

Developing Capabilities

In the realm of content creation, generative AI tools can automate tasks that help accelerate your team’s speed. The end result can be a variety of products, including analyses, organized data, calculations, gathered knowledge, and written code. For example – when given a prompt such as “organize the following data” (along with your goal and parameters for output), generative AI can create financial statements or reports, customized to fit user needs – all within minutes. The tools can also create repetitive analyses and reports, saving time for other members of your team. 

Artificial intelligence is also incredibly powerful in the way it can create valuable insights when users ask the tool to create a new scenario or simulation, and report back results. This can look like a team asking the question “what happens when a competitor launches a new product two months ahead of our company?” There are tools that can simulate market conditions, economic environments, and events to demonstrate how each variable can impact a company’s performance. Generative AI can even take things a step further to scrutinize its own initial analysis, suggesting improvements or validating logical connections that may exist within the data.

Evaluating Results

Like we’ve previously mentioned, it’s wise to remain cautious about the validity of outputs from generative AI. There can be risks involved in immediately deploying content created by artificial intelligence, and it’s important to carefully manage that risk. This can be done in a variety of ways, including reviewing outputs that don’t meet expectations, and acknowledging biases that may be present in the data that trains the tool. And of course, all outputs should also be used in compliance with state and regulatory requirements.

Our Partnership with Grant Thornton

The Blackford Capital team is grateful for our partnership with Grant Thornton and for their support of our 2023 Investor Conference.

Grant Thornton has been providing audit, tax and advisory services to Blackford Capital and its portfolio companies for many years. The companies work together collaboratively to help Blackford build out its platforms and generate exceptional returns for its investors. We look forward to continuing this partnership in the years to come!