Category Archives: Technology

70 Years Ago Today Was the Beginning of an Era …

When the first “networked” television broadcasts took place as KDKA-TB in Pittsburgh, Pennsylvania goes on the air connecting east-coast and mid-west programming. And then,

12 Years Ago Today the End of that Era Began …

when Netflix announced it will launch streaming video services. Who needs cable TV when you can watch all the shows on your laptop, iPad, and even cell phone on the go?

Regardless of what you think, that’s a pretty fast rate of advancement. Eras used to last centuries. Now they barely last decades. Can your supply chain keep up?

AI in Procurement Today

As per yesterday’s post, there is no true AI in procurement, at least with respect to the traditional definition of AI as artificial intelligence, but there is AI out there if you interpret AI as assisted intelligence, and some of it is pretty good.

What is there? If you check the doctor‘s 2-part in-depth piece over on Spend Matters on AI in Procurement Today (Part I and Part II) [membership required], you’ll see there are six areas where at least on one or two providers add a lot of value. They are:

  • True Automation
  • Smart Auto-Reorder of MRO / retail stock
  • Enhanced Mobile Support
  • Guided (and sometimes Guilted) Buying
  • M-Way Match And Error Prevention
  • Smart (Automatic) Approvals

And, in some cases, a system will integrate its automation, m-way match, and smart approvals to determine when an invoice with a small fluctuation can be automatically paid and when it can’t. For example, when an invoice comes in for services at a rate 10% higher than the last invoice, most m-way match systems would block it and bubble it up to the lead buyer / requisitioner. But a smarter system with integrated checks, behavioural analysis, and a history of override decisions might do the following:

  • check the PO and see it referenced a master contract with an evergreen clause where the original term had expired and the supplier had the right to increase rates up to 15%
  • check the user’s past overrides and see that they generally approve rate increases of 10% or less
  • check the user’s approval authority and see that they have the ability to make that approval
  • calculate the probability of automatic approval by the buyer and if it’s 90% or greater, queue the invoice for automatic payment, with a notification to the user that they may want to explicitly renegotiate the contract as the next invoice from the supplier might be at a 15% increase

Now, this is not going to help you in all cases, but every time you waste time investigating an overage you can’t do anything about, it’s a waste of time and, thus, any assisted intelligence solution that can prevent a waste of your time is valuable.

For more details on what the best systems can do today, if you have a Pro membership, the doctor strongly encourages you to check out AI in Procurement Today (Part I and Part II) and find out what your Procurement system should be doing for you.

When A Vendor is Selling (Cognitive) AI, What Are You Really Buying?

AI is the buzzword, or, more precisely, the buzz acronym. Just about every enterprise vendor is claiming they have AI, even if all they have is RPA (and even if what they have is pushing the definition of RPA). However, whether your vendor has AI or not (and the answer is that they probably don’t, as most of the best vendors just have ML, possibly enabled by AR, but probably not), it is coming, and if you don’t adopt (at least) the (precursor) technology available today, your Sourcing and Procurement organization may be left in the dust.

And by now you are probably firmly bamboozled, so let’s set the record straight, starting at the bottom of the AI technology ladder.

At the bottom of the technology ladder we have RPA, short for robotic process automation, which is generally used to automate what would otherwise be very manual processes, usually by way of a rules-based workflow engine.

On the next rung we have ML, short for machine learning, which applies (usually improvements on, or variations of) open-source or standard algorithms that can extract a model from a set of inputs to produce the associated outputs with high probability. The better platforms use machine learning to tune, if not define, the rules used by the workflow engines embedded in the platforms.

Sometimes the mix of ML and RPA is so good that for certain, focussed, applications that the platforms almost seems intelligent, and this is often what passes for AI these days. But it’s not real artificial intelligence, it’s assisted intelligence as it helps you do a better job, but your intelligence is still required to identify the right recommendations and approve the right actions.

The next rung up is AR, automated reasoning, which can take a set of assumptions, encodings of logical rules and predictive models, and compute derivations that can surpass even a human expert most of the time for very well (and narrowly) defined applications or problems. It’s basically the modern equivalent of an expert system that can compute millions of inter-related logical inferences until new realizations are discovered.

The next rung up is the version of AI that exists today, augmented intelligence, which expertly integrates RPA, ML, and AR to produce applications that more-or-less mimic what an expert would do the majority (but not all of) the time. And that allows an organization to automate some low-value tasks that would otherwise require manual effort as they were generally identified as strategic, but not always worth the effort.

If it existed, the next rung would be the AI that is touted, true artificial intelligence, which does not exist today. (And that’s a good thing, because if there was true AI, would the C-Suite need you? Yes. But would they realize it? Probably not.)

But the final rung, and where everyone wants to get to, is cognitive. AI technology that is not only intelligent, and that can make great decisions unassisted every time, but make the decisions the best human buyer for every situation would make considering all hard and soft variables.

And that’s the technology ladder you are dealing with, and now you know that where you are is likely not where you want to be. But don’t fret, things are getting better. Stay tuned!

RPA: Robotic Process Automation or Redistributed Process Automation

RPA, ML, and AI is all the rage these days, with RPA being the most mature technology. But just because it’s a mature technology, that doesn’t mean it’s a mature technology offering from the vendor you are considering, as powerful as they are purporting it to be, or even as general purpose as you might expect (especially if it relies heavily on ML or AI techniques).

In fact, like early spend classification technology, which was usually 60% auto-class and 35% behind-the-scenes manual-class by the hundred interns in the backroom (in India, Poland, or another outsource locale with a relatively high percentage of English-as-a-second-language speakers), a lot of the RPA technology being promoted today is in fact supported by, if not done by, humans behind the scenes.

This is especially true when natural language processing is involved, and doubly true when interpretation is involved. And it even comes in to play with something as simple as calendar scheduling. For example “book me an appointment with John Russell” next Wednesday is not often straight forward. John Russell the person, or John Russell the company? And the next calendar Wednesday, or the calendar Wednesday in the next week? (English speakers typically refer to the next calendar Wednesday as this Wednesday and the Wednesday in the following week as next Wednesday, but certain European cultures always refer to the next calendar Wednesday as next Wednesday.)

So imagine how much human intervention is required behind the scenes if you want to do document analysis or contract interpretation! Quite a bit. When it comes to document and contract processing, it’s one thing to break it up into sections and annotate what’s in the document, it’s another thing to interpret what each section means, and yet another to determine whether or not its enforceable, or even allowable, against a regulation or law.

Advanced RPA / ML systems can analyze a document or contract and break it up into relevant sections, identify the constituent components of each section (party, address, obligation, description, explanation, etc.), and make it easy to determine whether or not a section, entity, or value is contained within. With sufficient ontological definitions, training, and tweaking, these systems can get highly accurate.

But when it comes to interpretation, that’s different. It’s easy to determine that a document contains the phrase “the receiving party is bound to provide the sending party with a hold payment to be applied against the obligation of the sending party upon transmission”, but harder to figure out precisely what that means if the hold payment, receiving party, sending party, and obligations are specified elsewhere in the document. And then if you want to determine whether or not that obligation is in line with organizational policies or contract law in the jurisdiction of choice, that’s yet another level.

Really good tech might be able to sift the document and make probabilistic guesses as to what the hold payment is, what the obligation is, and maybe even what transmission means (providing to a courier, being received by a local courier, showing up at the recipients door if its a physical good, or confirmed receipt / acknowledgement if an electronic IP deliverable), but chances are it will be wrong a good percentage of the time and require human confirmation. And when it comes to interpretation, frankly, unless a human is reviewing the clause and given the most likely scenario, a random number generator mapped to an outcome table is likely to be just as accurate. (In other words, trusting RPA means you are rolling the bones.)

Thus, any RPA system that performs an advance task is likely not true Robotic Process Automation but in fact Redistributed Process Automation, even if the vendor doesn’t advertise it as such. But if you are curious, there are tells. How long does the system take to perform the task? An hour or two to process a document? Definitely RPA of the second category. Fifteen minutes or more to schedule that appointment? If both sides were using true RPA of the first type, it would take seconds, maybe a few minutes if there was limited bandwidth and email delay. And so on. Look at service times, customer counts, and what’s being heavily promoted. The truth is under the covers.

But redistributed process automation is not necessarily bad. It’s probably the most efficient use of your organization’s time, especially if the vendor has RPA-lite algorithms that can quickly determine what needs to be done by a human and what can be automated. Anything that saves your organization time and money while improving outcomes is a step forward, and as long as the vendor continues to reinvest its profits into system development, the system should get better over time.

But don’t buy RPA with eyes wide shut. Otherwise, you might not get what you are expecting. Or put too much faith into the system.