The legal contracts landscape in any organisation is in a perpetual state of flux because of the wide and continually expanding variety of formats. Over the years, traditional paper contracts have been joined by digital alternatives like PDF and Word contracts, which in turn, are being joined by smart contracts that are underpinned by emerging technologies like blockchain and digital government systems.
To this growing pile of formats, add ‘visual contracts’ – a new alternative that is gaining interest.
Visual contracts are an attempt to address an all‐too‐common issue, which is that most contracts are utterly incomprehensible for non‐lawyers – despite attempts by lawyers to write contracts in ‘plain English’. Have you ever read the terms and conditions from companies like Amazon or Facebook – or the policies from your bank or insurance provider – and walked away with a clear understanding of what you’re agreeing to? Of course not. For individuals and business professionals alike, it’s difficult to review a contract and immediately understand the rights and responsibilities of the involved parties.
This is where visual contracts come in: as the saying goes, a picture is worth a thousand words. When the contents of an agreement are clearly depicted visually – utilising design elements such as clear font, bullets, colour highlights, graphical images, flowcharts – it’s easier to grasp key takeaways such as the rights of the parties, how the agreement can be terminated, what the terms of engagement are, or how some rights will be adapted if the current situation changes.
Many organisations are already looking at this new approach to communicating contractual terms. However, beneath the pretty visual layer, there must be machine readable data if insight and intelligence is to be drawn from those contracts for business advantage. Otherwise, all we are going to do is create another ‘next generation’ of dumb (but this time, pretty) contracts.
The ‘Dumb Digital Data’ Problem
The word ‘dumb’ is used here to highlight the limitations of previous formats of contracts, particularly with regards to the ability of organisations to extract the intelligence that is natively embedded into them by lawyers.
Paper contracts, for example, aren’t digitised, which makes it difficult to search for and extract key pieces of information. PDF contracts are digitised, but they aren’t machine readable if they haven’t been OCR’ed, and the data within them is unstructured from a computer programmer’s perspective. Likewise, Word contracts are machine readable, but also unstructured with regard to semantic mark-up.
Visual contracts will suffer from similar limitations if a careful approach isn’t taken. Having a clear visual representation or picture that illustrates the key points of the contract is a great step towards making contracts more understandable to a wider audience, but there needs to be a data model underneath those visuals that AI can pick up on.
Put more bluntly, AI isn’t going to be able to magically look at a graphic or image in a visual contract and derive meaningful intelligence from it; there needs to be a data‐driven approach underlying it.
Unlocking the Intelligence
Consider the above a crucial step for ensuring that organisations don’t further abet the ‘dumb digital data’ problem that already exists. But how should they handle their existing pile of contracts? How can organisations best move forward in their quest for intelligent contract management – one that allows them to unlock the intelligence in contracts of any format?
The following steps provide a framework for quickly getting started while avoiding any potential pitfalls that could unnecessarily hinder the effort.
For starters, identify where all your contracts are. This might sound basic, but many organisations came face to face with a deadline‐driven event like the LIBOR transition or Brexit only to realise that contracts that might need to be updated were scattered who‐knows‐where across the organisation. Rather than waiting for an outside event, organisations should pause and take stock of all their existing assets and their locations. Moving forward, any new contracts that are generated can be kept in a centralised location, so that they are easily accessible.
Next, think about which data points are important to you in those contracts. What intelligence do you want to pull out of them? Don’t overthink it: there are probably five to ten data points which matter in every contract – and they will vary from organisation to organisation. For some, it might be deal type; for others, it might be deal length, parties involved, or contractual rights obligations. Ultimately, there isn’t one model to say what data you need to be capturing.
After you’ve thought through what data points are important to capture, you can utilise AI and machine learning to pull the intelligence that’s important to your organisation out of these contracts. Note that AI only comes into the picture after you’ve tackled the preceding steps.
Think of this as ‘IA before AI’ – which is to say, Information Architecture before Artificial Intelligence.
Once you’ve started collecting meaningful data from your contracts, you can start to uncover patterns in the data and build models that give you predictive capabilities around business decisions. For organisations looking to digitally transform themselves and make analytical, data driven decisions, there are few capabilities more valuable.
Take the Right Approach
Visual contracts are still in their early stages, but the use cases are interesting, and the potential benefits around clearly translating ‘legalese’ are significant. If organisations hope to successfully leverage the intelligence in this new contract format, however, they will need to take a data driven approach – just as they must for contracts of any format.
Well structured data especially at the creation stage could allow lawyers to create multiple outputs of their work from a central master – a textual version, smart logic components (in smart contracts) and maybe visual aspects. This is how the publishing industry digitised in the 2000s, lessons to be learnt.
Taking stock of all existing contracts, identifying the important data points to capture, and then using AI to extract and learn from the data provides a way to make this data‐driven approach a reality. The result is a world where contracts can readily yield analytical insights and intelligence for business advantage. After all, organisations need not make a choice between legal contracts that are either pretty or intelligent – they can be both.
About the author
Alex Smith is the Global Product Management Lead for iManage RAVN. He has over 20 years of experience in product management and service design, including new and emerging technologies such as artificial intelligence, semantic search and linked data, as well as content management. Prior to iManage RAVN, Alex has held positions at Reed Smith LLP and LexisNexis UK.
