Supercharging Due Diligence: Unleashing AI's potential in outside-in analysis

Ongoing market disruptions are changing the private equity (PE) landscape. Winning firms are increasingly relying on the outside‑in due diligence (OIDD) approach to help identify opportunities, mitigate risks, and increase returns—all of it with minimal up-front investment.

According to the AlixPartners 8th Annual PE Leadership Survey, about half of industry leaders now expect deals to be fewer, take longer, and be harder to exit.

The Objectives of OIDD

OIDD provides rapid insights to (1) help PE firms mitigate risk and optimize the chances of success for potential acquisitions and (2) help investors make informed decisions about how best to proceed. The core idea is to invest a minimal amount as a way of kicking the tires before committing more time and resources to a deal that could go nowhere.

Comprehensive OIDD can help companies address four common challenges in the pre-due diligence stages:

What Great PE Firms Are Doing Well in OIDD

When it comes to effective OIDD, successful PE firms have five characteristics in common:

1. They look at the entire business: Successful PE firms consider the business as a whole —top-line structure and bottom-line structure — when assessing an investment opportunity. This consideration includes current financials, organizational structure, staff sentiment, and corporate culture. Taking this type of end-to-end approach enables firms to make well-informed decisions.

2. They consider operational implications: Investments come with operational implications. For example, the merging of two companies requires consolidation of technology tools and software. And depending on each organization’s systems, the process could be relatively straightforward, or it could take a significant amount of time.

3. They ask how opportunities can be realized: It’s not enough to know that opportunities are available; PE firms have to ask questions that help them uncover how the opportunities can be realized via both short- and long‑term investment plans.

4. They leverage actionable insights: Having comprehensive information about operations and opportunities is critical, but such knowledge is useful only if a company can convert data into actionable insights and then use the data to drive decision-making.

5. They implement AI-driven analytics: AI-driven analytics makes it possible to streamline and automate each of the processes listed above, thereby helping PE firms make better decisions faster.

The AI-driven Approach 

With an AI-driven digitized approach, a company can gather and cluster a vast array of public information to create and apply customized OIDD models.

The OIDD approach makes it possible to obtain data from key competitors to develop benchmarking analyses, conduct investigations regarding potential mergers or acquisitions, and identify growth opportunities. Combining an AI-driven, automated approach with an experienced team makes it possible for companies to gain both broad and deep insights that are useful in the decision-making process and that help shorten decision time to weeks rather than months.

AI-Driven Integrated Outside-In Due Diligence

Six areas AI-enabled due diligence should cover

1. Organizational Structure

By collecting and curating publicly available data, PE firms can obtain information on a company’s organizational structure to get a rapid X-ray of a prospective investment opportunity. Using this information, further analysis can investigate spans and layers, learn about the level of organizational efficiency, find opportunities for consolidation, analyze the target with reference performance metrics, and compare cost structures for a target company versus its competitors.

2. Functional Operating Model and Footprint

Relocations of people or locations of facilities in the proper places are initiatives that can be improvements for the target company to ensure that facilities are strategically located, offices are rightsized, and people are situated in the best-cost locations. Obtaining data from the target facilities and mapping every individual to the closest facility based on their location serves to identify and quantify footprint consolidation opportunities. Furthermore, the information is highly valuable to understand how well-distributed a company is versus its competitors.

3. Employee Sentiment

Natural language processing (NLP) enables PE firms to understand employee sentiment across critical areas such as corporate culture, management structure, and overall engagement. This understanding is especially critical in the assessment of a merger, when staff synergies are critical to the investment thesis. As a result, understanding company culture and employee sentiment may be even more important than a company’s financial data. This could define current or potential efficiencies and synergies or understand what best-in-class companies are doing and how they are performing.

4. Market Dynamics and Growth Potential 

Using a combination of analyst reports, primary research, and expert validation, a full industry research analysis can quantify market size, growth rates, and trends. Additionally, comparing management’s revenue plan and external data can help determine whether it appears realistic, can identify external drivers, and can pinpoint both risks and opportunities.

5. Product Value Proposition 

How do consumers perceive the products being sold? Answering that question helps develop a growth hypothesis for the company’s product portfolio and marketing strategies. Using AI-driven algorithms to gather pricing, unique-selling-point (USP), and portfolio data and comparing the data with customer reviews helps pinpoint customer perceptions.

6. Perceived Customer Sentiment

Using structured interview guides and collecting various kinds of customer feedback, AI-enabled due diligence results in an account-level analysis of revenue, profitability, trending, and inputs to the go-to-market modeling of branding and selling priorities by segment. In addition, PE firms can assess recurring revenue performance potential and investigate common performance gaps to identify the potential for winning wallet share based on competitive take-out and cross-sell opportunities.

Delivering OIDD with AI

Taking an outside-in approach adds value to the initial stages of the decision‑making process by helping investors achieve both quick and informed judgments. However, the increasing complexity of PE deals combined with the sheer volume of relevant M&A data speaks to the need for AI-driven OIDD capable of collecting, curating, and correlating relevant data at speed. The result? Higher-value, lower-risk decisions at speed. 

Act now to learn how winning firms are using OIDD methodology to identify opportunities, mitigate risks, and increase returns.

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