With technology it’s not an either/or

Two opposing ideas or concepts can both be true.  We accept this in our real lives, whether it’s something as deep and meaningful as understanding that a wedding is both happy and sad as a parent of one of the participants or as common as the knowledge of what is considered healthy food which will prolong your life but also knowing that doughnuts taste really good and you want one right now.   But this is not a blog on cognitive dissonance, but one about contract lifecycle management, legal tech and the related. So what do dichotomies and the like have to do with that?  Well I think that one of the challenges with adoption and proper use of technology in the legal and contract space is a forced binary understanding of the use of technology which in turn limits what can be when done right vs what has always been.   When discussing these topics with the resistant, you often hear “I don’t believe in AI because it can’t do ‘X’” or “’Y’ activity is too complicated so I don’t see how any tech could help with that.”  Now, I am not here to tell you that technology is the answer to all problems – far from it.   But I think that technology is part of most, if not all solutions in legal and contracts now and we should be framing the discussion and thinking along those lines and not and either/or binary situation.   To do this, perhaps let’s consider the noble platypus.  

The platypus is a mammal indigenous to Australia and looks like a cross between a duck and beaver.   And not to go too deep into your zoology, but the platypus is one of the only mammals that lays eggs instead of giving birth to live young (a monotreme to get technical).  It’s not one thing or another, but a perfectly evolved creature that incorporates two things we think of as traditionally different and opposite.   And I haven’t even mentioned that the platypus is also venomous like a snake.  

Bringing this back to contracts and legal technology, I think that we should be inspired by the noble platypus and think of our solutions as not one thing or another, but both things, perfectly evolved for the environment – a perfect evolution of human expertise, judgment and interaction coupled with the far superior processing, knowledge management and data knowledge.   A combination of human and machine – a cyborg if you will.   Now I don’t mean this as an individual as that gets us back to the false choice of a binary view that something is “this” and “that” or “this plus that”.  I think both those views lead to overlaps in activities and roles.  Also, I don’t think we’re ready for cybernetically enhanced lawyers – at least I’m not!  Rather, I prefer to think of it as “that with this when needed and necessary.” 

Let me give you some examples of what a “cyborg environment” should look like.

3rd Party paper review

I wrote about self-service recently and beyond pizza-based analogies, the focus was really on starting from your own template or clause library.   But the killer item that CLM and AI tools keep trying to sort and the issue which many a reluctant team uses to kill the acquisition of a technology is third party paper review.  The argument usually goes, “Well, tool ‘X’ works really well if we are using our paper, but as you know we never get to do that, so how will tool ‘X’ work with third party paper?”   I love this argument because so many in-house teams use it that if you play out the alleged statistics no one gets to use their paper, which must mean that some omniscient fourth party is secretly creating all the first drafts.  Cue the conspiracy music and wiki-wormholes.  That aside, there are a number of CLM platforms and spot-solutions that do a pretty good job with third party paper – provided there are articulated standard positions by the party doing the review.   If you don’t have that, then no tool or human will do a consistently good job – but that’s a topic for another blog.   NDAs – probably high 90% hit rate, professional services/MSA somewhere in low 80% - as industry averages.  No tool is perfect – but do you know who also isn’t perfect – human beings.  The alternative to using a technology is to use a person who may or may not have a better hit rate.   For simple, repeatable documents, the reality is that many technologies do it as well, if not better than humans and, sorry to say, do it faster if not instantly. And the old adage “Time is Money” is true.   So from an overall solution, ecosystem perspective that values speed, efficiency and accuracy there is the 100% human version which has been around since stylus and papyrus or the technologically-enhanced merger of preliminary technology driven review plus human oversight (a cyborg) that – when configured, trained and implemented correctly – results in less reviewer time, more focus on high-value/strategic questions and quicker contracting or speed to value.  No – it’s not perfect, but neither are people nor let’s not pretend they are. 

Obligation management

I know, obligation management is not exciting.  I suppose a breach of contract is more exciting.  Or perhaps the real risk of material breach, reputational risk and regulatory fines get your attention.  So please humour me when I suggest that managing and adhering to the obligations you or your vendor (or your vendor’s extended supply chain) signed up for are important.  Working under that “assumption” (sigh), we can all agree that it can be a challenging and time intensive task.  To paraphrase Orwell, all obligations are equal, but some are more equal.  So how do companies attack this? Or, better yet, how should a cyborg system attack this?   Glad you asked.  Companies appear to be embracing and understanding the need for this activity and I think we should thank AI and CLM tools.  15 years ago, to get this from word processing to a spreadsheet, was control F, control C, control V ad infinitum.  Trust me – motivating a team to do this was … a challenge.  Nowadays, we have really good AI tools and platforms with embedded AI that can ingest documents and extract all the “must”, “shall”, “will” and other permutations of requirement language.   But what they can’t do – without guidance – is determine which is more equal.   And this is where the human part of the cyborg can and should shine.   If you are just using the AI, you are probably looking at too much.   If you are just using humans, you are probably six weeks behind on getting the obligations together in a manageable manner.  But a system that has rules based upon what is valuable to the company, reviews the obligations before programmed and then can evaluate “non-compliance” is powerful. 

Performance & Financial Management

Performance and financial management are the quintessential areas where we see that contracts are touched by everyone but often owned by no one.  We negotiate (for too long) the SLAs, reporting criteria and mechanisms around pricing, which are written in Word and monitored in systems that only understand numbers and no one seems to ask, “well – those two systems really don’t seem to communicate, how are we translating that?”  What then happens is a vendor will present a report that looks really good, has nice words, graphics, and even has a RAG reporting model.  It must be accurate, right?   Well, just like history is written by the victors, reports are written by those with the most data.  This often leads to my favourite term – the “watermelon effect” which shows all green on the outside, but if you dig in … a lot of red.  So how does the cyborg system avoid being on the backfoot?   It starts with obligation management (see above) but then also parsing and then connecting financial obligations with your ERP system – a job for the robot.  But then, when information on reports, misses and hits come in, there needs to be judgment and understanding of the relationship before a company decides to enforce a right or push a penalty.   Some things are simple – how many licenses are used, what is the core cost.   Others may need a human’s touch – do we need more hours of work, an SLA missed in month three or extreme event.   This is where we need to marry the tech (data) with the judgment and a cyborg solution is much better than one or the other.

I have always thought that it’s good to combine topics, ideas and approaches – whether it be CLM solutions, fusion cuisine or unique Australian mammals.   Perhaps I’ll write more about that my memoir – Conte: One Man’s Efforts to be the Anthony Bourdain of CLM.  But until then, I hope we can avoid the binary and embrace the mix of use cases.