Artificial intelligence (AI) has not reduced the need for thorough due diligence. At the core, buyers still need to understand adjusted earnings before interest, taxes, depreciation, and amortization (EBITDA) and quality of earnings, adjusted net working capital, and potential debt-like items, as well as accounting policies, revenue, customer, margin, and expense trends and the track record of management. Teams will still need to identify key issues, risk areas, and deal implications while using judgment to assess likelihood, magnitude, cost/benefit, and other impact areas while scoping, sorting, identifying, prioritizing, and communicating what matters to a deal. Here are three ways in which AI is changing the due diligence environment and its execution.
1. Speed
The capacity to perform due diligence quicker is markedly enhanced. Teams begin engagements with a running start by having AI assist with trial balance mapping and producing the first version of schedules. AI can help identify trends and anomalies, identify missing data, perform and review reconciliations, and prepare questions for management tailored to gaps in information received to date. First views come quicker, and this thrust continues through the engagement. That said, AI-assisted diligence teams may still be bottlenecked by a target company’s ability to pull requested information as those target teams may have more limited transaction experience. But overall, speed is increased and key issues move forward in the process as AI frees teams to spend a higher proportion of their time on value-added analysis.
2. Depth
By automating the routine, AI allows diligence teams to dive deeper. More focus is made on stock keeping unit (SKU) level data, customer and vendor insights, employee productivity, and working capital trends. The result is more analysis, more throughput, more support, and more data-driven insights. Sector-specific experience and service-line execution helps strengthen the work. Because identification of an EBITDA adjustment can translate to many times the fee incurred, transaction risks and their implications in today’s market mean diligence does not go away. It drives further and creates more confidence and value.
3. Talent
Strong teams are created by pairing experienced professionals with the latest tools. Firms that overly rely on automation can go down the wrong paths, not know when to stop, miss the true issues, and have thin and unsupported conclusions. AI may make their reports and output look professional, but they won’t hold up under scrutiny. Their people will not have the proper training to know the difference and there will be a noticeable gap in their talent in the coming years. Their depletion of junior ranks will create future holes in executive-level insights provided to clients. Conversely, firms that underuse automation may be slow, inefficient, expensive, and also miss out on identified key issues. Firms need to find the right balance. The talent flywheel will provide an environment for staff of high-performing firms to ask better questions, apply their learnings repeatedly, and continuously improve.
Need for Diligence
AI can improve quality of earnings and due diligence execution for clients of higher-performing advisory firms. Unfortunately, the technology is also increasing the prevalence of lower-quality products in the market by firms relying on it as a cost-cutting measure. In the right hands, AI can help users move faster, at a deeper level, and support the training needed to provide high-quality decision-making data for clients. Robust due diligence has never been more needed and AI and automation tools are as beneficial as ever for clients in the transaction process.
How Forvis Mazars Can Help
Our dedicated transaction advisory professionals have completed due diligence engagements on billions of dollars in transactions across a wide range of industries. If you have any questions or need assistance, please reach out to a professional at Forvis Mazars.