Every stage of the contract lifecycle creates data. And at every stage, traditional contract processes lose, mislabel and discard that data. Fixing this could transform legal process forever.
This is a chapter from our Modern Contract Handbook, featuring insights from expert authors on every stage of the contract process.
The default currency for contracts, with almost 100% market penetration, is a product that was never designed for legal documentation: Microsoft Word. People create drafts in Word, often by copying and pasting parts from old documents, and then make tracked changes, bouncing around between different versions via email. At some point, everyone’s happy and the document becomes a PDF.
This PDF jumps into various email chains until it’s printed, signed and scanned, or perhaps signed electronically with a tool like DocuSign. It’s then saved on a shared or personal drive, where it sits pretty much forever.
There are lots of problems with this process: it’s hard to collaborate, it’s inefficient, it takes a long time and version control is difficult. But a key problem, that only increases as time goes on, is data loss.
In the process described above, almost none of the data on contract collaboration is retained:
- If suggestions are made over email about edits, that data doesn’t make it to Word
- If edits are tracked as changes in Word, that data doesn’t make it to PDF
- Time-to-signing data and new version data doesn’t make it to the shared drive
Generating all this collaboration data, only to discard it throughout the process, is a huge waste of lawyer time and expertise. The main reason why so much data is lost throughout this traditional workflow is that the data generated in static Word and PDF files is unstructured. Key fields like dates and signatories aren’t tagged with metadata, so when the document passes to a new system, that system can’t differentiate between important and less important information.
This makes it almost impossible to have a genuine audit trail of a legal document’s journey from inception to agreement. Who made which edits, when, why, and how - all of this is either never captured or lost along the way. Without data to identify problem clauses and negotiation bottlenecks, they are almost certain to reappear next time.
“The most obvious way to capture and retain collaboration data is to stop creating unstructured data, and instead use a machine-readable editor to create your contracts”
There exist excellent AI contract review products out there that can structure your unstructured data. They can ‘read’ old contracts and offer some level of automated tagging, bringing structure to the collaboration data that’s been mismanaged. However, data that was lost is not recoverable; and more than this, if your problem is that collaboration data is unstructured, then creating more unstructured data and buying a solution to structure it for you is an odd way to solve it.
The most obvious way to capture and retain collaboration data at all stages of the lifecycle is to stop creating unstructured data, and instead use a machine-readable editor to create your contracts. When the document receives approvals, or is amended internally, or viewed and negotiated externally, the metadata that captures all these changes, stakeholders and events is created and stored.
If the platform in question also offers signing and contract management, then there’s no need to move the contract to Word, PDF or email. However, if the contract was created from structured data, then users have that option, because the document is rich enough in insight to carry useful data from system to system.
The impact for your bottom line
There are two immediate advantages to your business of this approach:
With negotiation data preserved and captured, you can get real-time insights on your contract workflow’s efficiency. Which templates typically lead to the longest contract lifecycle? Which clauses usually block signing? Which business colleagues make the most edits? How do approval rates change over time? Analytics will enable you to answer these questions, not only to improve your processes, but to be accountable to your colleagues in other teams.
Search might seem like an unsexy feature, but a powerful search mechanism can have a huge impact when it comes to response times and efficiency. If your contract data is properly structured, users can search for documents via team members, dates, company names, clause titles - even using free text queries. Without structured data, your ability to search contracts will begin and end with the file name and the modified/created dates.
“Without structured data, your ability to search contracts will begin and end with the file name and the modified/created dates”
These factors can both have an impact on your bottom line: more efficient collaboration is likely to lead to faster results, better alignment from stakeholders, and transparency across the business into legal’s workload. It also creates a feedback loop that will make your team, and your systems (if they incorporate machine learning), smarter. The more contracts data you can collect, analyse and action, the better your ability will be to make decisions about deviations from standard terms, negotiation positions, and so on.
The business world as a whole is aware of the benefits of retaining structured data, and a shift is underway towards dynamic documentation, created in-browser, often collaboratively. This is what Google Docs has looked to solve in relation to Microsoft Word, and next-generation providers like Notion and Coda are taking other legacy document formats like Excel and making them collaborative and data-rich too. Legal is a long way behind the curve, and running out of excuses as to why that’s the case. By embracing contracts data now, you could be future-proofing your legal team for decades to come.