Unlocking M&A Success: How AI Streamlines Diligence, Target Identification, and Value Capture

Insights from Aniline’s Chief Commercial Officer, Brian Cochrane

I’ve built my career providing M&A solutions for financial sponsors and enterprise clients and recently transitioned to investing in and advising AI-driven SaaS companies. As Chief Commercial Officer at Aniline, an AI-powered SaaS startup, I’ve developed a strong appreciation for AI’s transformative potential in reshaping the M&A value chain.

Traditional M&A processes are notoriously complex, time-consuming, and heavily reliant on manual, labor-intensive efforts. Although banks and financial sponsors have refined M&A processes and checklists to streamline the workflow, the approach remains largely artisanal. The M&A value chain requires processing vast amounts of unstructured data, much of it historical and backward-looking, with minimal forward-looking insights. This creates a significant opportunity for AI to drive transformation and innovation. 

In my view, the  benefits of AI fall into three categories:  

1. Synthesizing multiple, unstructured data sources into usable information

2. Automating certain tasks

3. Leveraging new data sources that have been brought into the mainstream by AI 

The remainder of this article will outline my vision for the potential evolutionary trajectory of M&A in the future.

Deal Sourcing

Deal sourcing can be viewed from two perspectives: 1) which  specific companies best match the acquiror’s appetite; 2) which companies the acquirer has a  relationship with or can leverage intermediaries such as bankers and lawyers to gain access to the companies.  

While AI can't build relationships, it can identify key decision-makers when those relationships are missing. More crucially, AI has the potential to greatly improve deal sourcing. The current state of the art normally requires an entire team constantly reviewing financials, new articles, and profiling recent deals, as well as  reviewing segment-specific, often technical aspects such as IP quality, quality of product development pipeline, etc.  In recent years, other less traditional data sources such as social media are being leveraged to look for managerial shifts that could signal potential openness to a sales process.   

What  M&A professionals have done is built an algorithm to source deals, even though that is not how the approach is typically articulated.  AI can automate some, perhaps most, of that “algorithm”.  The AI can be “taught” what to look for and comb through the entirety of the financial news data to identify  deals that fit a particular  appetite.  Some forward-looking PE firms are also leveraging AI to review their historic deal flow, profiling successful deals and looking for non-traditional patterns that can be incorporated into deal sourcing strategy.  

Diligence and Valuation

Diligence and valuation are also prime candidates for AI re-engineering.  M&A diligence requires sifting through data rooms filled with  thousands of pages of important information.  AI and natural language processing can process this information and synthesize it into usable diligence reports, highlighting areas that require additional deep dives—all within minutes. 

AI can also streamline valuation by automating comparable analysis (comps) and utilizing real-time data feeds to create dynamic financial models. It can incorporate economic conditions and industry trends to predict future cash flows and valuation multiples under various market conditions and competitive moves. 

Negotiations and Close

Negotiations and closing are often expensive and time-consuming process, with lawyers exchanging term sheets and drafting contracts that can be iterated dozens of times.  This can add time to the process and can cost 1-5% of deal value, depending on deal size and complexity.  

AI can deliver near instant contractual reviews, which can be particularly powerful with law firms that have many years of experience and a wealth of contracts they can leverage to train the AI.  AI can also quickly highlight off-market terms.  If sufficient valuation data is available, this process could value off-market terms to determine what purchase price adjustment would be necessary to achieve the terms in question.  

From a negotiating strategy perspective, AI can examine email and meeting transcripts, providing insights into negotiation strategies, risk tolerances, and reservation pricing.

Value Capture/Integration

Value capture comes in two flavors: acquisition and M&A integration.  Value capture comes into play when a PE firm acquires a stand-alone company, whereas synergy capture comes into play when merging two entities.  Synergy value is primarily derived from reducing redundancies while mitigating deal risk.  There is a degree of overlap between the two concepts and both approaches can benefit from leveraging AI.  AI is adept at leveraging large data sets, such as comparing supply chains, to identify areas of potential pricing power or work forces identifying areas of redundancies.

Leveraging Unique Data Sets Generated by AI

To this point, we have discussed how AI can automate certain tasks and synthesize data sources to provide rapid insights.  However, the ubiquity of AI is also creating a new frontier of data sources and insights that did not exist previously.  Employee sentiment is one often overlooked but critical such data source.  There are now over 20 social media sites (e.g., Glassdoor, Indeed, etc.) that contain billions of employee perceptions.  The M&A value chain has recognized the value of this data for some time, usually allocating associates from financial sponsors, I-banks, and/or law firms, to “go online and see what is going on”.  This manual approach is time consuming, often onerous, frequently biased, and almost always non-statistical.

AI can not only sift through the entirety of this data continuously, it can also be taught which  perceptions are positive, negative, and neutral, which perceptions are relevant to particular topics, and how these insights are relevant to M&A processes.  Since 2019, Aniline has been at the forefront of this work, gathering over 1 billion insights from 70,000 companies worldwide.  Aniline’s algorithms and its generative AI essentially allow a potential buyer to have an on-demand employee focus group that provides statistically meaningful insights on a broad array of topics.  The benefits of leveraging this insight span the M&A value chain:

• Deal Sourcing:  Many PE firms have very specific appetites.  Some are industry based (e.g., RTP focused on SaaS; Silver Lake on technology; Stone Point on insurance); some are situational (e.g., Fortress on distressed; Cerberus on turnaround situations) and some on size top line growth potential(e.g., VC vs growth equity vs private equity).  Aniline’s ability to assess employee sentiment can provide an added dimension for segmentation.  For example, If you are investing in:

• A high growth business: understand the employee’s base faith that the growth rates are sustainable would be valuable 

• A business on the verge of launching a new solution: analyze potential customers of the solution to better understand latent demand triggers.  

• A leadership-focused investment: understand what the employee base thinks of leadership and how leadership compares to other leadership groups within industry competitors can offer another critical perspective

• Avoiding blow ups:  analyze how employees view integrity and compliance, potentially kicking out deals below certain thresholds or requiring broader reps and warranties and large escrows to account for the increased risk.

• Diligence and Valuation:  Aniline provides critical support in the due diligence process. In most situations, a potential acquirer’s only interaction with the target tends to be highly curated, with companies “putting their best foot forward”.  With Aniline, you can create a full diligence report at a push of a button.  While it will not replace legal or financial diligence, it can provide instant insights into important operational diligence areas.  Employee perceptions are critical in understanding many operational and organizational elements:

These and many other elements can be accurately assessed by leveraging Aniline’s solution.  This diligence can then be used to inform the detailed deep dive work of the M&A team, providing guidance on  where to focus their efforts

• Value Capture and Integration: Aniline's insights can help optimize post-close value capture and integration processes. In almost all situations, value capture requires some form of process re-engineering and head count reduction in some areas and investment in others.  Aniline gives the new owner the ability to track employee sentiment around these changes, allowing for course corrections if issues are identified.  For example, if the employees are expressing concerns about the go-forward incentive structures, and retention is becoming a concern, communication around the attractiveness and industry competitiveness of the go-forward incentives can be increased.  

M&A processes will always be arduous and anxiety-inducing given the tight time frames and the value usually at stake.  While technology has made it easier to share information, it has also vastly multiplied the amount of information available to be analyzed.  AI has the ability to synthesize and analyze much of this data, allowing the users to identify and focus on the most complicated issues quickly.  Early adopters of AI will see better targets, improve their diligence, and close on those targets more quickly, as well as improvements in value capture quantum, timing, and certainty.