The evolution of due diligence: from manual labour to AI

Due diligence in mergers and acquisitions is evolving. Long an indispensable but notoriously labour-intensive process, this critical stage sets the tone for the entire transaction to come. And that’s not about to change. What is changing within due diligence is the advent of Artificial Intelligence (AI), which is ushering in a new era of efficiency and accuracy.

Read on as we delve into how AI technologies are transforming due diligence, offering not only gains in speed, but also (and crucially) deeper insights and more reliable outcomes.

The age of manual review
Due diligence used to be an intensely manual process. Picture teams of lawyers, accountants and other professionals sitting in a room sifting through reams of paper documents, one by one and line by line, to ensure that every aspect of a potential transaction was above board. This approach was time-consuming and prone to human error. Very definitely sub-optimal.

The Virtual Data Room era
The introduction of Virtual Data Rooms (VDRs) in the early 2000s marked the first significant step toward modernising due diligence. VDRs allowed professionals to review documents in a secure digital environment, enabling quicker access and easier collaboration. Yet, it’s important to note that while VDR-enabled reviews represented a leap forward compared with the era of manual reviews, they didn't eliminate the need for hands-on tasks. Professionals were still confronted with having to manually change names, create folder structures, redact sensitive information, and set up permission rights. So, while VDRs tackled challenges like unstructured data management and enhanced security, manual labour in various forms persisted, keeping the risk of oversight and errors in play.

The future, part 1: the rise of predictive and analytical AI
With the advent of AI, the process of performing due diligence is entering a new era. First, predictive and analytical AI technologies, such as AI legal Large Language Models, automate complex tasks like document categorisation and clause extraction. This is predicated on making use of past data to predict future trends. Analytical AI therefore helps with organising documents and pulling out important clauses during the due diligence process. For example, these technologies can quickly plough through years of corporate contracts to highlight the parts that matter for a particular merger or acquisition.

 While predictive AI speeds up the pace of work and is great for specific tasks, it doesn’t offer new, creative solutions. Don’t be disappointed however – to paraphrase, there’s other AI for that, too.

The future, part 2: generative AI’s new frontier
The second, more advanced type of AI is generative Artificial Intelligence. Unlike its predictive cousin, generative AI doesn’t merely predict – it creates. And in doing so it creates new pathways for speed and precision, fundamentally reshaping the M&A process. While predictive and analytical AI models automate and streamline existing processes, generative AI introduces new approaches to challenges in due diligence like:

  1. Document generation: Generative AI can write concise summaries from a multitude of complex contracts, presenting them in coherent reports, thereby saving time and ensuring thoroughness.
  2. Data augmentation: Sparse or missing data can be augmented by generative models to ensure a comprehensive analysis.
  3. Scenario modelling: Generative AI enables advanced financial forecasting, providing stakeholders with a detailed landscape of potential risks and rewards.
  4. Anomaly detection: Generative models flag inconsistencies or potential fraud by identifying deviations from what is considered “normal” within a dataset.
  5. Contract drafting: Generative AI assists legal teams in drafting contract clauses or even entire sections that resonate with prevalent practices.

Generative AI models are designed to generate new data that is similar to the data they were trained on. This enables them to generate anything from summaries of lengthy contracts to entirely new financial scenarios, so providing creative solutions to complex problems.

When evaluating a company’s worth or risk profile, for example, generative AI can simulate various business scenarios to predict future performance or potential issues. It can generate synthetic data to fill gaps in existing records, making the evaluation more comprehensive and reliable.

Throughout this, the key thing to understand is that generative AI doesn’t just retrieve existing data in response to queries; it can craft detailed, human-readable answers by drawing from multiple data points. Imagine asking, “What were the revenue trends over the last five years?” and receiving a report that not only gives numbers but also offers insights into seasonal fluctuations, market impacts, and growth drivers.

AI challenges and considerations
As lawyers and legal-tech experts at Legadex, we see these developments as being incredibly exciting. At the same time, we are also the first to recognise that adopting AI isn’t without its challenges. AI systems offer unparalleled speed and efficiency, but their reliability is contingent upon the quality of their training data. Biased or skewed training data can lead to biased outputs. The phenomenon of “hallucinations” in generative AI, where the system produces confident yet erroneous outputs, stands as a testament to potential pitfalls. Such instances underscore the need for rigorous checks and balances.

The rapid advance of AI also underscores the lag in AI-specific regulations globally. Different jurisdictions are only now beginning to draft regulations tailored to AI, leading to potential uncertainties in AI-based agreements. Issues concerning the origin and training data of AI systems, especially if personal data is involved, can raise privacy concerns. Additionally, the digital nature of AI should remind us of the ever-present threat of cybersecurity breaches, with potentially critical implications if an AI system is compromised. 

The Human-AI collaboration
To mitigate these risks, it is increasingly important that organisations can call on AI-specialist legal expertise, whether that is internally or via third parties. At Legadex, we are trained to recognise the subtle nuances and potential inaccuracies that can arise from AI-generated outputs, including the so-called “hallucinations”. Our role in terms of AI – and of any AI legal specialists – is not only to interpret the law, but also to understand the intricacies and limitations of AI algorithms. By doing so, we ensure that AI tools are used responsibly and effectively, thereby safeguarding the integrity of the M&A process.

Conclusion: charting the future of due diligence
The due diligence process in mergers and acquisitions is evolving remarkably. Predictive and analytical AI are already streamlining traditional tasks, such as document categorisation and clause extraction. These technologies offer speed and accuracy but are limited to improving existing processes.

Generative AI, on the other hand, is a game-changer as it offers creative solutions to age-old challenges. For instance, it can generate concise contract summaries, fill data gaps with synthetic information, and even draft new contract sections. Beyond that, it’s capable of crafting detailed answers to complex queries, providing not just data but also nuanced insights.

However, this technology is not without its hurdles. Issues surrounding data quality, global regulations, and cybersecurity call for a cautious approach, making it increasingly crucial that organisations can call on specialised legal expertise to navigate these complexities. These professionals are not merely interpreters of the law, but also custodians of ethical AI use, ensuring that the technology serves its purpose without compromising integrity.

As we look to the future, it is clear that the blend of human skill and AI capabilities – both analytical and generative – will redefine the way we approach due diligence.  Combining human expertise with AI promises a process that is not just faster but also richer in insights, more reliable, and ultimately more effective in driving successful mergers and acquisitions.

Join us next week to delve deeper into the future of AI-powered predictive analytics.