The inconvenient truth about contract lifecycle management

To sign a contract Image credit

One of the tasks that befalls a CTO is the endless process of keeping up to date with new market trends. You need to stay abreast of changes in applications, new technology and review where systems or technologies have failed, to avoid making the same mistakes. To do this effectively, you need to keep track of what market analysts like Gartner, IDC, 451 Research and others are discussing. While also keeping an eye on what your competition, and others within your technology arena, are doing or advocating.

I started Seal Software with a clear vision, to fundamentally change how contracts are managed. I knew of the struggles and failures with contract lifecycle management (CLM). However, the issues I saw eight years ago have not gone away — unfortunately, they have only grown. Most CLM systems were developed and deployed with the same mindset. Namely, if an organisation templatized or standardised their contracts base, and understood how they interacted with external parties; they should be able to track, manage and report on all contractual terms. However, this approach did not work then, and it does not work today.

The issues with many CLM solutions

Events over the last few weeks, involving three different streams of technical opinion and discussion, have caught my attention.

  • First, we have the old view that having a CLM system with a different “universal” schema will enable CLM to succeed. This has already been proven to be ineffective.
  • Second, you have the new “Smart Contracts” view. This is the idea that placing templates and workflow, within a blockchain, will fundamentally remove all the issues present within the existing CLM system, using a “universal” schema.
  • Finally, you have the “inconvenient truth” about CLM, which is why, in its current format, it will always fall short. This is the view that drove my business partner and I to create a platform with artificial intelligence at its heart.


The problem is that CLM generally only deals with one side of the equation—your own contracts. As noted by the recent videos from Pramata, CLM could fall short on over 60 percent of the agreements you have within your system. This is because a large percentage of those agreements are on third-party paper. Unfortunately, those agreements are, in many cases, the most valuable (in terms of revenue), and the largest (in terms of company size). This is generally because the companies you are ‘selling to’ will be larger, or command a stronger negotiating position. Therefore, you will be contracting on third-party paper, that your CLM system is not configured to deal with.

Some companies have recognised the importance of this shift. For instance, we work extensively with Ariba to provide analytics and information extraction to augment the system when third-party paper is used. If you look at how the current, and next generations, will interact with systems, it’s clear that the momentum is going with systems that learn and predict. This is because manually typing into a rigid system database, “universal schema,” or otherwise, is just not going to generate the results you need.

Not all AI is equal

We are so used to “tagging” pictures within Facebook or other systems, and then after a short time, expect those systems to “auto tag” friends for us. Why should enterprise CLM systems be any different? Many companies have also recognised this trend. However, not all AI is equal. This is particularly clear within the CLM market. Players are looking to adjust the systems to cope with the changing requirements and CLM’s failure to truly deliver.

In an ever-changing environment, companies rebrand and attempt to adapt old technology to meet the new world. They acquire vendors that have the allure of “new technology,” or just add the current market buzzwords. The truth is when a startup sells out, it’s usually because they cannot gain traction on their own. This is now abundantly true within the artifical intelligence (A.I)  and CLM space

What this means for CLM

Information extraction is not new—Zonal OCR and clustering have been used within Enterprise content management (ECM)and CLM systems for many years. However, that functionality is now being rebranded as AI, by companies jostling to find their space within the new information management age. Asgard, an AI VC, has recently suggested that only 60 percent of more than 400 European AI startups, who claim to be AI firms, actually are.

Before anyone selects an AI vendor, they need to ask themselves, or the vendor, the following questions:

  • Does the system rely on clustering of documents based on templates to know what extractions to apply?
  • Does the system perform Zonal, or as some might call it “hot-spot” information extraction?
  • Does the system need to have a set of similar documents to be able to cluster them, and it does not perform well when the document formats are more random?


Any system that uses Zonal, or requires similar documents for the extraction to work, is not an AI solution. It is just a re-badged OCR Zonal and cluster system, with rules or regular expression. Which, as I mentioned, has been used within ECM digital mailrooms for many years. These systems cannot make predictions on unseen information, nor cope with documents that do not fall within a set format. So, much like the CLM templates, these systems are only good for standard, and generally less-valuable, contracts.

The inconvenient truth of CLM

A critical part of any CLM, or even contracts intelligence solution, is not just the extraction accuracy. It’s the speed and agility, at which a business user can gain insight. One of the reasons CLM solutions fail to provide full value for third-party paper, is that they are unable to quickly adapt to changing reporting or information extraction requirements.

Ask any prospective contracts analysis or discovery vendor: How long, after a Monday morning data breach, it would take them to process and locate all the contracts – containing a set clause that has not previously been captured? Or within that clause, a specific data point – such as the agreed upon notification period to affected vendors, that must be met. Then ask them to sort that data in the order of shortest time to longest?

This might appear simple, but it is not. It involves both artificial intelligence (A.I) I and natural language processing (NLP) combined. As information needs to be detected in random formats (machine learning (ML)); with varying degrees of changes (ML). It also requires information normalization (NLP). Oh, and all that analysis needs to be performed within less than 24 hours. So, if you are considering a CLM system, you must ask a very important question: Will this system allow me to find critical information, myself, and within the time frame, I need it to? If the answer is, yes, you need to ask whether your vendor can prove it, live.

We come back to the inconvenient truth of CLM. Existing solutions do not adequately address the issue of third party paper. Neither are they unable to create a scalable system, that manages contracts and all the data they contain, no matter whose paper they are on. This is why Seal is brought into accounts, very often to work with CLM, and to create a system that manages all a business’s contracts. Find out how we can help you.

Seal Software Logo (Image credit Seal Software)

Kevin Gidney is co-founder of Seal Software. Kevin has held various senior technical positions within Legato, EMC, Kazeon, Iptor and Open Text. His roles have included management, solutions architecture and technical pre-sales, with a background in electronics and computer engineering, applied to both software and hardware solutions.

 Seal Software Contract Discovery and Analytics helps companies maximize revenue opportunities and reduce expenses and costs associated with contractual documents, systems and processes.


The inconvenient truth about contract lifecycle management was last modified: by

Post Comment