How Do You Turbo-Charge Negotiations?

Not that long ago, THE REVELATOR asked some questions around negotiations and how you could turbo-charge them for a better outcome. Of course, the doctor answered because this is becoming an even more important topic given the state of world, and technology, affairs, but its also one that needs a repeat discussion because this topic comes up a lot and the doctor fears that everyone is missing at least one key point.

What does it mean to “turbocharge” negotiations?

How about “what should it mean”. Everyone has their own answer for “what does it mean”, and most aren’t that useful.

Turbocharge should mean to back up with facts (based on organizational data) and market data relevant to every aspect of the negotiation, to go in knowing both what value there is in it for you as well as your BATNA, and the value that it is in for your counterpart, and a best guess at their BATNA.

Without all this insight, you don’t know if you even have a leg to stand on, or how to reach common ground to carry the negotiation forward. Data insight goes much, much, further than a carrot or a stick ever will.

What do you define as being a successful negotiation outcome?

A successful negotiation outcome is a win-win. It’s not a zero-sum win-lose game like a certain world famous infamous author thinks it is … unless, or course, both parties have the exact same collection of goals which they would rank and weight the exact same way, which is astronomically rare. Thus, since the vast majority of the time both parties have their own unique definition of winning, which can have some overlap, both parties can win.

What are your thoughts about AI and the negotiation process?

When it comes to AI and Negotiation, the answer is no, No, NO, ??, ??! Since it’s not true artificial intelligence, you should never, ever, ever let it negotiate as that is letting the system make a decision, vs recommending a decision, which even IBM told all its employees 46 years ago that this is something you should NEVER, EVER do!

To what degree do experience and expertise impact negotiations?

Experience and Expertise both help, but reality is that the results depend more on the differential between the two parties at the table than any scale you might come up with to measure your own. So you want both, but you should always expect to be outmatched, which is why data, facts, and insight are so critical. If you can find that common ground and give up at least some of what the other party truly wants, you have a much better chance of getting something you want and coming out okay.

Bonus Questions

“Are women better at negotiation than men?”

That would, of course, depend on your definition of negotiation and success. I’m inclined to say yes, but if your definition of success is to be a complete a-hole pre, during, and post, well, I’ve seen way more men who excel at that.

“Is AI better at bluffing than humans?”

Regular humans, or sociopaths? Since AI has no feelings, and doesn’t understand truth from lies, depending on how you define bluffing, it can be absolutely great at it … or not.

“Is AI “Genderless”

Not the right question. We know tech is genderless.

The right question is the following: Is AI trained genderless? Usually not as its usually trained on results that were predominantly created and input by men (who make up 75% of STEM). So it’s not genderless and, sometimes, it is very, very biased.

FINAL QUESTION

Is it the technology or how you use it?

It is most definitely how you use the technology, not the technology itself. Heck, you can get good results with a carrot if you are in negotiations with a bunny. 😉

And that technology must be used to get you the data and insights you need to have a good human to human negotiation. No more, no less. Because, at the end of the day, that’s the only way you can turbocharge a negotiation for success!

The Best Way to Survive the AI-Powered Apocalypse? Go Old School!

If you’ve been following along, you know that a great purge is coming on two fronts. All the pundits agree on that! On the first front, a large number of vendors are going bye bye, as we’ve been telling you since our first post on the Marketplace Madness. On the second front, they took ‘er jobs. Except it’s not they, it’s AI.

So doesn’t this mean that if you want to survive the days ahead that you should find the most advanced AI provider that isn’t going to get purged in the near future, adopt the tech, replace as much staff as you can with AI, find a way to survive the hardship, and come out ahead when everyone decides that what they have to do?

Well, for the vast majority of the analysts and pundits, it is exactly what you should do — and do it right now. It’s AI overload all the time. And just when most hype cycles start to die down, this one gets a second wind of hurricane proportions.

But, in fact, it’s the last thing you should do. In fact, you should implement a Gen-AI ban and Agentric AI ban immediately, and identify classic ML-powered AI augmented intelligence tech that can supercharge your team, acquire it, and train your team on that immediately. Because you can get the same results as any Agentric AI can get if you employ the right classic ML-powered human-driven AI technology with the right algorithms, analytics, optimization, etc. Sure, a human might be a little bit slower than an algorithm that can work 24/7/365 without a break, but human who is appropriately skilled and trained will make up for this with something the AI doesn’t have, true intelligence.

You see, the thing about Gen-AI and Agentric AI is that it works great until it doesn’t. As per our recent post, Gen-AI is full of problems. In a recent post, we noted that, Gen-AI can:

  • get you sued
  • increase the chance you will be hacked
  • result in Million/Billion-Plus processing errors
  • shut down your organization’s systems for days
  • help your employees commit fraud

And those are the good side effects from its hallucinations. There are much worse side effects that can happen. If you refer back to our posts on the valid uses for Gen AI and the valid uses for Gen AI in Procurement

  • the embedded biases, that you might not even be aware of, could result in decisions diametrically opposed to what you are expecting
  • when it computes two options that are equally likely to generate the same end result for the company relative to the KPI it is using, there’s no guarantee it will select the right option — and there’s always a right option, especially if one option for cost savings is a longer term contract so the supplier can upgrade equipment and the other option is forcing the supplier to cut an already razor thin margin 50%
  • the hallucinations eventually become real, as the systems get so advanced that they not only create super realistic evidence to back up their recommendations, but take over your entire systems in the background so that you don’t know that a web request to verify a claim is actually still being processed by the AI that is now running in the background
  • it starts negotiations and cutting contracts you haven’t even authorized yet
  • it becomes you … and you get blamed for all its mistakes

In other words, ignore the Gen-AI and Agentric-AI technologies that are not the miracle cures they are promised to be. The miracle cures are the last generation ML-based AI technology that was just about to transform your operations under the expert fingers of your leading practitioners, not some probabilistic monstrosity that requires an entire data center to run to generate an output no one verify using a system no one understands. Hone your chops on those and you’ll get the results you need, without having to deal with unexpected, possibly catastrophic, failures along the way.

After all, when we told you about all of the great advancements that were coming in Source To Pay in our classic series (indexed here), none of it required Gen-AI to achieve!

Your Upteenth Reminder That Every Dollar Saved By Procurement Goes Straight to the Bottom Line!

… while 10 cents from every additional sale might make it, if you’re lucky!

A week or so ago, Joël Collin-Demers said COVID was the instigating event that pushed Procurement front and center in a comment to yet another post about the tariff crisis (to which, as I keep saying, the only solution is BTCHaaS), when it was really the (fist) elevating event in over a decade.

The first event that really put ProcureTech on the map was the 2008 financial crisis. This is because companies had to stop the bleeding, fast, and charged Procurement to get ‘er done. But once the markets settled, and the provider base stabilized, and companies willing to spend the money they needed to implement proper tech and get more efficient did so, Procurement kind of faded into the background again. That’s because, when markets rise, and sales rise, the C-Suite focusses entirely on revenue, almost to the point of irrationality, because the faster that revenue rises, the higher the valuation, and the more money they can make on the markets and trades.

However, the 2008 financial crisis is why the M&A and PE activity started to ramp up in ProcureTech in the early teens, because of the importance placed on cost cutting as a result of the 2008 financial crisis. And why, if something else had happened sooner, Procurement would have risen up the organizational chart faster, instead of falling back into obscurity at many organizations who returned undue focus to Sales and Marketing.

This, of course, belies the sad, sorry, state of affairs of North American business that still sees marketing and sales as the key to growth in a shrinking economy (and yes, with birth rates declining in almost all first world countries, it is a shrinking economy) when the real key is cost management. Remember your business 101 equation: Profit = Revenue – Expenses.

This says that every dollar of revenue you add is eaten up by the total cost to acquire that dollar — the total cost of that good or service, which is usually at least 90 cents of that dollar.

However, every dollar of expense you cut is gone in its entirety. Every dollar saved goes straight to the bottom line.

Thus, Procurement is 10 times as valuable as sales! But yet, the marketing madmen will try to hide that from you to protect their multi-million budgets!

So if you want to survive the crisis of the day, whatever that crisis may be, it’s not sales, it’s not marketing, it’s not finance, it’s not executive leadership or vision, it’s Procurement. Plain and simple. Maximize every dollar spent while eliminating those that don’t need to be.

Unless, of course, you are a ProcureTech vendor, in which case, as per a previous post, skip the fairy dust and buzzwords, focuses on your customers pain, and put together some educational materials (marketing and training) that will help them ease the bleeding. If you’ve forgotten how to do that, or never learned, there are those of us who can help you!

With Great Data Comes Great Opportunity!

In fact, it can quadruple your ROI from a major suite.

Not long ago, Stephany Lapierre posted that your team may only be realizing <50% of the ROI from your Ariba or Coupa investment, to which, of course, my response was:

50% of value on average? WOW!

Let’s break some things down.

A suite will typically cost 4X a leaner mid-market offering which is often enough even for an enterprise just starting it’s Best in Class journey (that will take at least 8 years, as per Hackett group research in the 2000s).

Moreover, even if the enterprise can make full use of the suite it buys for 4X, at least 80% of the “opportunity” comes from just having a good process, technology, baseline capability and automation behind it. That says you’re paying 4X to squeeze an additional 20% worth of opportunity in the best case.

On average, it takes 2 to 3 years to implement a suite (on a 3 to 5 year deal). So maybe you’re seeing an average of 66% functionality over the contract duration.

As Stephany pointed out, bad data leads to

  • increased supplier discovery and management times
  • invoice processing delays and errors
  • increased risk and decreased performance insight

As well as an

  • inability to take advantage of advanced (spend) analytics
  • inability to build detailed optimization models
  • decreased accuracy in cost modelling and market prediction

This is even more problematic! Why? These are the only technologies found to deliver year-over-year 10%+ savings! (This is where the extra value a suite can offer comes from, but only with good data. Otherwise, at most half of the opportunity will be realized.)

Thus, one can argue an average organization is only getting 66% of 25% of 80% of its investment against peers (based on 2/3rd functionality, the 4X suite cost, and the baseline savings available from a basic mid-market application that instills good process and cost intelligence) and 50% of 20% (as it is able to take advantage of at most half of the advanced functionality offered by the suite due to poor and incomplete data). In other words, at the end of the day, we’d argue an average company is only realizing 23% of the potential value from an opportunity perspective!

However, as one should rightly point out, the true value of a suite is not the value you get on the base, it’s the ROI on that extra spend that allows for 20% more opportunity than a customer can get from lesser peer ProcureTech solutions.

For example, let’s say you are a company with 1B of spend with a 100M opportunity.

If tackling 20M of that opportunity requires advanced analytics, optimization, and extensive end-to-end data, it’s likely that you’ll never see that with an average mid-market solution with limited analytics, no optimization, and only baseline transactional data. If the company paid an extra 1.5M over 3 years for this enhanced functionality, then the ROI on that is 13X, which is definitely worth it.

Moreover, if the suite supports the creation of enhanced automations, you could get more throughput per employee and realize the base 80M with half or one quarter of the workforce, which would lead to a lowering of the HR budget that more than covers the baseline cost.

However, ALL of this requires great data, advanced capability, and the in-house knowledge to use both. This is only the case in the market leaders. As a result, we’d argue that the majority of clients are only realizing about 25% of the suite’s potential — when sometimes the only thing standing in their way of realizing the rest is good data.

Why Your Standard Sourcing Solution Doesn’t Work For Direct

Too many of you have been there. You sign that seven figure deal for that end-to-end Source-to-Pay suite, spend another seven figures and 18 months integrating with the ERP, PLM, AP, BI and existing Legal CR solutions, and then try to source your first NPI project natively only to … fail. Why is that?

They just weren’t built for direct.

And it’s not just something you can add in later. If the platform wasn’t designed from the ground up for direct sourcing, there’s zero chance it will ever do a decent job at it. (And, FYI, the majority of the S2P suites the big analyst firms are drooling over in their annual quadrants and waves started out as simple indirect Sourcing or Procurement tools.) People who don’t understand the nature of software don’t get this, but software has to be constructed like a building. You might hear vendors and techies throw around MVC model, which stands for Model-View-Controller, when they talk about how new and well architected their solution is, but that just means it’s built in a maintainable web-friendly way for what, and only what, it was initially designed to do.

It all comes down to the data model and the software architecture of the controller, and neither can be a black box. The data model has to be designed from the ground up to support bill of materials and direct sourcing and procurement data requirements. The controller has to provide the infrastructure to support the complexity of the application that is required. For those who don’t understand software, I like to put it this way. If you pour the foundation for a two story house, and buy wooden beams for all of your structure and supports, you can’t build a 10 story apartment building. You need a foundation for an apartment building and steel and concrete supports. (Even though you can theoretically build a 10-story structure on a two-story foundation if you have the right steel and supports, it won’t be stable. The slightest tremor on the Richter scale [which might not even be detectable by a human] or a strong wind will send it crashing down.) You need both. And just like you can’t replace the foundation under a building or replace the entire support structure in real life, you can’t do the same in code. You have to rebuild, usually from scratch.

So why weren’t they built for direct? Well, there are a number of reasons (besides they wanted to get a product to market fast and/or just weren’t smart enough to build a direct sourcing solution). They include:

  1. direct material sourcing is hard
  2. substitution is not guaranteed
  3. demand aggregation is not straight forward
  4. delivery time guarantees and on-time arrival is significantly more important

To understand these, and learn about the rest of the reasons the majority of sourcing solutions were not built for direct, dive into Standard Sourcing Technology Solutions Don’t Work for Direct – Part One and Standard Sourcing Technology Solutions Don’t Work for Direct – Part 2 over on Supply Chain Matters.