Artificial Intelligence and law firms – considerations for lawyers and insurers arising from M&A transactions
Law firms continue to broaden their reliance on artificial intelligence (AI) across different practice areas. Many are partnering with third party tech companies to develop the application of AI in corporate due diligence and contract drafting, litigation document disclosure and as an aid to predicting litigation outcomes. However do insurers really understand what challenges AI technology presents to law firms and how it might be changing their risk profile?
In this blog, the first in a series looking at AI and law firms, we consider the use of AI in just one law firm practice area – mergers and acquisitions (M&A).
So how is AI being relied on by M&A lawyers?
No two corporate deals are the same right? Well correct but similar deals require similar processes of document due diligence, a similar matrix of contracts and, within each contract, similar contractual terms. That said the individual characteristics of each deal and the limitations of older technology have historically lead to firms starting each transaction if not with a blank sheet of paper (or sale and purchase agreement) then a sparsely populated one.
AI is changing the landscape for corporate lawyers. Increasing numbers are now using AI (specifically 'machine learning' technology – machine learning being an application or subset of AI) to achieve efficiencies and improved outcomes for their clients. To date Insurers might be most familiar with the advertised efficiencies achievable during the document due diligence phase of a corporate transaction. However they may be less familiar with AI software capable of learning from the large amounts of data created by lawyers from previous deals and stored "on premises" in the firm's own servers or via the cloud. Either way this data set is capable of acting as an enormous data bank for future deals – only one which AI can filter and extract data from incomparably quicker than a team of lawyers. AI has the ability to learn from these data sets in order to then:
- Make clause recommendations to lawyers based on previous drafting and best practice.
- Identify "market" standards for contentious clauses.
- Spot patterns and make deal predictions.
- Benchmark clauses and documents against given criteria.
So what are the challenges for law firms implementing this type of AI and why might it have a material effect on their risk profile?
- AI might be more efficient than a human lawyer at performing these tasks but who is responsible when things do go wrong? Missing clauses, mis-referenced definitions, incorrect outcome/price predictions caused by AI software all risk claims. Clients will look to their lawyers (and their insurers) first to put things right. But do the law firms have the contractual means of passing liability onto the third party tech company and does that company have sufficient resources of its own, including sufficient insurance, to act as a reliable source of recovery?
- How are firms navigating the issue of client consent in order to meet their legal and regulatory obligations applicable when sharing data with third parties?
- Finally, an obvious issue law firms are grappling with surrounds security, and the dataset required for the machine to learn, when that data is being drawn from the firm's own document management system(s). Client data is confidential and in an M&A setting potentially highly market sensitive. Issues law firms need to consider include what safeguards are embedded within the technology, and put in place by the firms themselves, to ensure client confidentiality is protected.
In our next blog about AI and law firms we'll be looking at how AI is changing the way firms act for clients in a litigation context and what further risk considerations this give rise to for the firms and their insurers.