Recently, a project manager at a major pharmaceutical company was asked about what benefits her company is seeing from its enterprise-wide contract lifecycle management (CLM) platform. She responded that it was all about data.
"Now, we are able to know how many contracts we are doing, and what is in those contracts. It sounds a little bit basic, but before we did not have that information, at least at a global level," she said. "Also, we are able to measure the cycle time of how long it takes us to make a contract, which was information we did not have before."
This data, she said, has forced the company to be "more disciplined" with contract management: "Who does what, and when do I need a contract."
This story is not unique. One of the major benefits of turning contracts from static documents to digital assets is the wealth of data it produces about contracts and the contract process. Once this data is in hand, companies can start making strategic decisions to accelerate and optimize contracting while reducing risk. You can't manage what you can't measure.
But with so much contract data out there, where to begin? Consider taking a "crawl-walk-run" approach: start with basic data that nonetheless is valuable when measured, then progress to the more complex measurements that drive at the heart of contract risk and value.
Here is a road map to consider on your digital contracting journey.
What to measure: Contract volume (company paper vs. third-party paper; types of contracts and templates used; business unit initiating contracts; geography where the contracts originated).
What you'll get out of it: A comprehensive baseline for your current contract landscape.
By moving contract management to a cloud-based platform, you will get a holistic view of contract actions across the globe. You will see which departments are originating the most contracts, what templates they are using, how often they are using contracts created by your legal department, and how often they are relying on outside contracts (third-party paper).
This is not data for data's sake. Instead, it is a clear portrait of your contract landscape, which is the start of making data-based decisions on how to improve and simplify processes. For example, many companies have greatly reduced the number of contract templates in circulation after gaining an understanding of what's being used, and where. Additionally, they have simplified those agreements based on data from their CLM. This is an early win for CLM deployment that improves contract velocity and reduces risk.
What to measure: Contract turnaround time; delays in approval; contract value.
What you'll get out of it: Data to identify bottlenecks and measure improvements over time; insights into vendor and customer relationships.
Once you understand what your contract landscape looks like, you can start to examine where inefficiencies exist.
A central KPI for any CLM deployment will be turnaround time—how long it takes from when a contract is initiated within the system to when it is executed. Successful CLM deployments can see contract turnaround time decrease by 90% or more.
Some of the reduction can be attributed to the software itself. With a cloud-based contract management system, communication between stakeholders becomes streamlined, and contracts can be automatically routed to the right person for approval.
But data also plays a role. With CLM, companies can not only measure total turnaround time but also how long contracts linger at each stage. Bottlenecks quickly become apparent, which companies can then address. Is it our legal team that is slow to respond? Maybe it's opposing counsel that is causing the delay? Systematic delay can be addressed accordingly and progress can be tracked over time to ensure efforts are having an impact.
The contract value is another important data point to start measuring at this stage. Contract metadata like how much the contract is worth has historically been locked in legalese that was impossible for software to process. This meant manual extraction of contract value out of the paper document and into other systems—wasting time and creating errors. The contract value was even more difficult to measure when factors like rebates and performance-based discounts were factored in.
Now, though, contract management software can automatically extract contract value and other metadata. With this information, high-value contracts can get automatically routed for review; executives gain instant insights into the company's most important contractual relationships, and risk is surfaced.
What to measure: Deviations from standard terms, deviations from contract management policies, and contract obligation performance.
What you'll get out of it: Reduced litigation and disputes.
When your CLM is fully deployed, you can start drilling down into clause-level analysis.
Advances in artificial intelligence (AI) enable contract management software to read and identify contracts at scale. This means that the software can, for example, review thousands of contracts and analyze which indemnity clauses are being used. Actions can then be taken to remedy situations in which an indemnity clause active in a contract violates risk management policies.
AI can also automatically identify and surface obligations contained in contracts. With a holistic view of all obligations, companies can measure how well they are doing at fulfilling their requirements, and how well their contract counterparties are doing at fulfilling theirs. This active audit function of contract obligations both reduces risk and maximizes contract value.
A data-driven approach to enterprise contract management won't happen overnight. But with the right software and an incremental approach to collect data and information about contracts and business relationships, companies can quickly realize new insights and the huge value digitized contracts carry.
If you would like to learn more about Icertis' approach to contract management, contact us today to schedule your free 1:1 consultation.