Recently, a customer of ours asked us to help them audit their contracts for “detrimental terms”, i.e., the terms that are not beneficial to their company. Once we find them, they also want us to help the company fix the less-than-ideal terms. Because of our focus on private legal AI and a thorough study of “golden standards” to find deviations and suggest remedies to bring them back into the standard, this task is right up our alley. However, one can’t help but ask: why do companies sign up for bad terms in the first place?

Instead of discussing this phenomenon as a simplified “people make stupid mistakes” narrative, the reasons can be complex. We, as business people, make mistakes often, but more often than not, make conscious concessions. At other times, we may think something is okay for now, but later on, as we continue to evolve, things are no longer okay. Finally, we may also “inherit” contracts, sometimes from teams of people who don’t align with our standards, other times from companies such as acquisition targets. Let’s break these reasons down.

Lack of Awareness

The first reason that we sometimes find not-so-great terms can be a lack of awareness or knowledge. Lawyers are known for those “got ya” moments. When the legal, procurement, or sales team may or may not be aware of the implications of a certain provision, we can and will make mistakes, which we will later regret. A simple example would be even for sophisticated procurement managers and in-house lawyers; we may accidentally sign up for terms such as non-cancellation for a multiple-year contract or auto-renewal for software solutions. Then, when we want to cancel a contract of multiple years, we are shocked to find that we can’t, and we have to pay. Or incidentally, be enrolled for another year of a software solution that we just realize that we no longer need.

The simple fix for this situation would be to develop gold standards and use AI to “crawl” every single contract that we sign to ensure we absolutely make no such silly mistakes. As we all know, AI is much more thorough than humans. So, by deploying AI-driven rule enforcement across the board, we can avoid making silly mistakes such as these.

Result of Conscious Concession

The more common scenario, however, is when we sign up for not-so-great terms intentionally, typically in exchange for something.

As business people, we all know that we must make horse trades with our counterparties. It’s unrealistic to expect bulletproof terms for the entire contract all to our very best advantage unless you have a crazy amount of leverage such as Apple.

More likely than not, we concede on points that are not so important to us, in exchange for terms that are critical to us, which we find more valuable. For example, as an automotive company, we may care a great deal about having a long and robust warranty from our suppliers, while if we are doing okay with cash, we will let not-so-great payment terms slide.

In this case, the “trade-offs” are much harder to quantify with AI. As we know, AI is not the best at assessing trade-offs and making choices. However, we can still deploy a somewhat scientific system to assign weight to each of the provisions and use the AI to score the strength of each of them. Using the previous example, maybe for us, a warranty provision carries the weight of 20 points in a 100 score system, for which we are getting a 9, whereas a payment term provision may only carry a score of 5 for us, and even if we get a 1 for the payment term, we are still better off overall.

Another aspect of agreeing to not-so-great terms consciously is what you are getting for them, in addition to pure legal concessions. Are we getting paid more? If not, shouldn’t we? This is another consideration for business people once the AI identifies the sub-optimal terms.

Things that are No Longer Okay

Signed contracts are static. Business environments are, on the contrary, dynamic.

Take the same payment term example above. When we are sitting on hundreds of millions of dollars in R&D mode, we may not really care about paying our suppliers a bit early. However, when we become a mature company where we need to carefully manage our cash flow, then getting an optimal payment term may become increasingly important.

That’s why for many companies, when we look back at contracts we signed one year, three years, or five years ago, things all of a sudden don’t look so rosy. We may have made concessions in the heat of the moment, which can later come back and bite us. Business, macroeconomic, or other changes may have occurred to make the previously acceptable terms seem detrimental now.

To catch these terms, we must compare our old gold standard with a new one, adjust based on changed circumstances, and audit all the existing contracts systematically, to identify detrimental terms and to figure out a way to fix them.

Inherited from Others (People or Companies)

The last situation where we are stuck with bad terms is when we inherit them.

As general counsels or procurement leaders, I am sure you have joined a company and found the contracts that you are stuck with to be a mess. The previous GC may have a different view about risks than you do. The previous procurement leader may think conceding to the supplier on certain terms is fine when you feel strongly against them. It is what it is; you are here, and you have a different set of standards.

Similarly, for companies that acquire other companies, you may be stuck with not-so-great terms from the company you just acquired. They may be a lot smaller than you are. They may have a different risk tolerance. They may not be as sophisticated or powerful as you are in a negotiation. All of this may lead them to sign up for suboptimal terms.

The worst part of this endeavor is even though one would conduct diligence before the acquisition, you may still be stuck with terms that you may not be aware of. So if this is the case, conducting due diligence by comparing your gold standard to these contracts is the first step. AI can be extremely effective in this exercise. Similarly, once the suboptimal terms are identified, the AI can also make recommendations about fixing them.

In summary, signing up for not-so-great terms may seem odd, but it happens more often than we may imagine. Burying your head in the sand is not the solution. Conducting a proper audit, assisted by the power of AI, is the first step. More importantly, if the AI is fine-tuned with your gold standards, it can also recommend how to fix these agreements. Sounds intriguing? Please reach out to us.