“It is now necessary to attempt to develop computer systems in law that can be said to embody knowledge, and even exhibit intelligence. Achievements over the last twenty years, in the branch of computer science referred to as Artificial Intelligence (A.I.), have perhaps now provided the appropriate technological framework within which the construction of such knowledge-based systems in law might be undertaken.” – Richard Susskind, 1986
Machine intelligence applied to the law is not a new concept – yet its gestation has been slow, and it has not inflected into widespread adoption. Despite the vast size of the legal services industry, only a handful of venture-scale software businesses have arisen in the last decade. Legal contracts are the atomic unit of any business interaction, yet few enterprises and law firms apply machine intelligence to them systematically today.
Perhaps this is little surprise when we consider: (a) the conservatism of end users; (b) the traditionally high cost and complexity of building sophisticated NLP products; and (c) the tension between law firms’ hourly billing structures and the incentive for efficiency.
Might things be about to change? We’re currently seeing a wave of NLP-driven legaltech startups that are translating contracts into structured data, experimenting with new use cases, and finding early product-market fit. Those who craft the right distribution strategy today will emerge as the next decade’s winners in legaltech.
Thanks to those who’ve helped us clarify our thinking – including the founders who joined our recent roundtable, as well as Mike Callahan and Joshua Walker at Stanford, and the GCs we’ve interviewed in recent weeks.
Investing in contract analysis: why now?
Legaltech has grown steadily in recent years, and certain pain points are clear. We know that natural language is difficult to parse at scale. MS Word is an ineffective way to collaborate on contracts. Monolithic file structures and email transmission create major issues with document control. But industry inertia has been strong. Now, there are new catalysts on the supply side and the demand side. On the former, advances in NLP are enabling cheaper, better products to be built. On the latter, COVID has accelerated the urge for efficiency among resource-constrained internal counsels.
The overall bull case is supported by:
Where to play: customers and use cases
In B2B legaltech, there are three generic customer sets: law firms, in-house counsels, or other enterprise functions? Many startups experiment with a combination as they pursue product-market fit. What is the ideal go-to-market strategy?
Selling to law firms – especially smaller, specialist firms – is a viable early strategy given the short sales cycles and potentially easy access to a library of training data. These firms can be effective partners to iterate on use cases, test product-market fit, and gain a valuable source of model training data. The key use cases tend to be big-ticket items where assisted review can have the greatest financial impact: we’ve repeatedly heard senior partners calling for assisted reviews in M&A due diligence, litigation prediction, and force majeure claims.
The key challenge is alignment with practitioners’ incentives: the goal is to build use cases for automation that improve client outcomes, reduce write-offs, or improve throughput – and not compromise the number of hours they can bill.
In the long run, we expect successful players to move in-house, upmarket, and cross-jurisdiction – each calling for careful product and go-to-market manoeuvres. Enterprise users will generally be the more attractive customer set, given the larger market size and internal incentives for operational efficiency mandated by the C-suite.
Selling to in-house counsels is rarely fun or fruitful. GCs are time-poor, often tend towards conservatism, and tend not to come with a direct business case for contract analysis solutions. Indirect, partner-led channels may be a viable route to acquiring customers in the short term, but the incentives need to be aligned carefully. Direct relationships with the customer are needed, e.g. to iterate on product, and maximise access to training data
Selling to enterprise functions is where we expect the most long-term value to be created. While in-house legal teams are often the primary buyer / sponsor of a software product, the end users are often non-lawyers in other enterprise functions. Hence, a productive go-to-market motion is likely bottom-up, via non-legal teams who engage with large volumes of contracts, and are seeking process efficiency.
Sales, Procurement, HR, Compliance, and Finance teams are viable entry points. Sales teams, as revenue centres, tend to buy faster than other departments, so are a particularly attractive target. Today, all rely heavily on in-house counsels as internal service providers, and process bottlenecks are rife. We’re excited to see simple, self-service tools that empower these teams to review and generate contracts more smartly than ever.
Evaluating early-stage startups
Here are some of the heuristics with which we’re evaluating new entrants:
These questions might help us separate good companies from great ones today. Taking a longer term view, there are plenty of questions that remain unresolved. Will users’ dependence on MS Word diminish? Will new products remain defensible amid the rise of pre-trained, cheaply available language models? Might there even be new ways to encode legal knowledge outside of natural language and static documents?
If you’re building a startup in this space, we’d love to trade thoughts on these questions – please get in touch.