Two-thirds of finance operating partners say that PE-backed CFOs with whom they work are still struggling with inefficient finance workflows and processes that hinder finance function performance.
That’s one of the big takeaways from finance-focused operating partners at the inaugural LevelOP Summit. The annual gathering brings together influential leaders in finance and operations to share best practices, strategies, and solutions to navigate the unique challenges faced by CFOs of private equity-owned companies.
One of the key points that emerged from the recent event was this: technology continues to be a critical investment for PE-backed CFOs who need to minimize inefficiencies in order to maximize value creation. And the term on everyone’s lips was “Artificial Intelligence.”
Attendees noted that sponsors are eager for their portfolio CFOs to begin implementing AI technology. But while finance teams acknowledge widespread process inefficiencies, many within the team remain skeptical of AI.
Many…but not all.
Whether it’s in response to sponsor demands or because they recognize that technology is critical for solving efficiency and performance concerns, CFOs are warming up to the idea of further digitizing and innovating their departments. In fact, according to one finance operating partner in attendance, “Controllers are skeptical of an over-reliance on emerging tech. But CFOs, who see the big picture and understand the need for finance to scale, are actively seeking machine learning.”
Of course, there are some critical caveats for these CFOs—two specific ones were raised repeatedly.
1. AI can’t be a generic answer – it needs to be a specific solution
CFOs recognize that they can’t simply decide to invest in AI and then search for problems to solve. Instead, they must first identify their most significant pain points. Only then can they ask three key questions to determine whether AI can effectively improve the situation:
- Does the problem identified have sufficient maturity in terms of technology, data, process design, and team support to be ripe for digital disruption?
- Are there AI/Gen AI solutions available to streamline, automate, and disrupt this process?
- Does fixing the process provide enough ROI—financial benefits, time savings, improved accuracy and business insights—to justify the investment? And does the potential organizational impact outweigh the required effort?
Our take:
We believe there are several use cases across FP&A, financial operations, accounting/close, and treasury that are prime candidates for AI/GenAI-led transformation. These include close automation, cash flow forecasting, contract intelligence, invoice-to-cash automation, and sell-side readiness using data cube automation.
Close automation, in particular, is a leading candidate, as it continues to be a critical area that vexes CFOs (and frustrates their sponsors). Embedding the right Gen AI, large language models, and machine learning tools into the close process (via AI-powered transaction matching, journal entry automation, automated reconciliations/workflows, and variance analyses) can streamline and accelerate workstreams while allowing CFOs to redeploy their teams to higher value add initiatives. What’s more, AI can drive a 20-30% reduction in days-to-close, reduce error rates, and improve audit and compliance outcomes.
2. AI can’t just be about tech – CFOs must marry tech with people and process
Technology alone is not the answer to fixing an inefficient workstream or increasing visibility to understand performance drivers. But it’s certainly part of the broader solution.
Our take:
CFOs need to take a holistic approach toward AI introduction and finance function digitization.