Now, I’m going to upset a lot of people with this, but I don’t care because the linked article below is literally the best article I ever read on why you should NOT hire F6ckW@ds from Bg X Consulting Firms who claim to be analytics and AI experts when they don’t actually know

• the difference between a mathematical formula to calculate the center of gravity of a falling object and to calculate the median spend in a category
• proper software architecture
• proper compute resource allocation
• your business
• the difference between real ML technology, RPA and a few formulas, and the current Gen-“AI” where the “AI” stands for artificial idiocy

because

• you’ll spend 3 years and millions of dollars to implement something that should take 3 to 6 months
• you’ll spend hundreds of thousands on big vendor software licenses you don’t need
• you’ll spend hundreds of thousands on compute power you don’t need

After all, these guys and gals get paid by the hour and the commission on the resell license is a percentage of the total price they sucker you into buying it for. So, the longer the project takes and the more licenses and compute power they sell …

Read the linked article. Twice. And then tape it up to your fridge. The situation described in the article is NOT the exception. As a former CTO and 25 year consultant/analyst, I know this is the norm!

Now, if you’re wondering how to tell who is a F6ckW@d and who’s not when it comes to analytics and AI at the Big X? It’s easy. (If they are there for more than a year or two) THEY All ARE! The real talent in tech and analytics don’t stay long before they move on to specialist firms where they are listened to and allowed to do it right. Tech/Analytics people take pride in their work (and not their title), and you need to remember this. Good tech/analytics people don’t contradict managers because they want to be important, they contradict because they want the job done right, and the reality is that …

Some of this stuff is literally so complicated that you need degrees in mathematics and computer science and sometimes a decade of experience to get it right! (It took the doctor two advanced degrees and building advanced analytics and optimization systems for multiple leading companies in the 2000s.)

In other words, it’s okay if you don’t really get it as a manager. Just find those one or two people who do who you can trust, pay them well, and let them do what they need to make your department look good (be it hire internally, choose a consulting firm you never heard of, hire former colleagues on short-term contracts, etc.). They’ll get the job done right and be quite happy to let you take all the credit IF you give them regular raises and a bonus any time they do particularly well. Just put your ego aside and let the people who get it make the tech/analytics decisions, and everyone will win!

A recent article over on The Sourcing Journal indicated that one should Fail Fast and Fail Forward When Implementing AI into Workflows. WTF? Why fail at all? Especially since if you’re using AI where you are expecting a high risk of failure, there’s no reason to expect that you’ll only fail once, or that you can actually fail forward.

Now, if we were talking traditional ML, where it’s just a matter of continually expanding and refining the model and training data, tweaking the parameters, and starting small, then fail fast, fail forward, get it working, use the spice weasel, knock it up another notch, and continue until you have automation across the platform in appropriate places, it would be good advice.

But when we are talking full fledged Gen-AI (which is the article’s focus) based on massively large and entirely unpredictable LLMs or super-sized DNNs, you can fail fast, but, with absolutely no way to control the models, you can’t fail forward. So while fail fast and fail forward is a good motto in general for technology, process digitization, and automation, as long as you take things step by step and control the risk, it’s not appropriate at all when we are talking about AI!

Not that long ago, we penned Procurement Automation: Good. Automated Procurement: Bad because organizations that embrace the right digital technology do much better than their peers, but organizations that go all in and put too much trust in unproven technology without human oversight (while trying to run before they’ve learned how to walk) or good data (and then make worse decisions than having no technology at all, as recently determined by Gartner) are making a huge gamble while forgetting that it is the house who always wins. (And in this case the house is the technology provider that is charging you a lot of money for the technology that eventually fails and costs you time, money, and in the worst case, your job and/or business. But we digress.)

And while this blog is a Sourcing, Procurement, and related Supply Chain Technology blog, it was very happy to see a recent release from the Hackett Group, as advertised in a recent press release on yahoo! Finance / BusinessWire, that noted that while HR (and Humans are VERY important to successful Procurement Operations) operating costs increased significantly in 2023, Digital World Class organizations continued to spend significantly less than their peers while delivering more resiliency, employee productivity, and greater business value with less staff than their peers. The Hackett Group concluded that increased spend on technology plays a key part in driving the superior performance.

Other key metrics that Hackett pointed out is that companies with at least one business services function operating at Digital World Class levels see a five-year average performance premium over their industry medians -– an 80% improvement in net margin; 24% higher earnings before interest, taxes, depreciation and amortization; 89% greater return on equity; and 44% higher total shareholder return. (So imagine how good your organization would be doing if you were world class in Procurement and HR, and ensured that your organization always acquired, trained, retained, and promoted the best of the best.)

Hackett found that a key aspect of Digital World Class Organizations in HR, just like Procurement, was a greater use of technology (to the tune of 60% more likely to have and use the full capability of Human Capital Management applications).

There are a lot of great applications that a leading HR organization can employ that go beyond the specific applications mentioned of:

• Human Capital Management
• Time Sheet Management (for hourly employees / contractors)
• Health (& Welfare) management

and, as Hackett points out, include the use of emerging technologies such as:

• smart automation (not automated Gen AI applications)
• advanced analytics
• collaborative tools

For example, a good HR department will employ platforms that:

• will use smart automation to onboard employees, ensure they get paid on a regular basis, ensure that their expense claims are properly routed and evaluated on a timely basis (and OCR use to reduce receipt processing), ensure that all information they enter on health/disability/etc. claims is auto-routed to the right third party systems (and not lost/transcribed wrong), etc.
• will use advanced analytics to analyze its highest contractor/third party costs, determine what functions should maybe be brought (more) in-house, analyze it’s biggest employee benefit plan costs, optimize those costs (without reducing benefits), etc.
• use collaborative tools for onboarding, training, and continued professional development, especially for remote learning and self-study

Just like a good Procurement department will employ platforms that

• use smart automation to onboard suppliers, automatically distribute and collect RFPs, verify data that can be verified by a third party, do automated sanity checks, do initial analysis for presentation to a HUMAN, automatically generate POs from carts/contract schedules, automatically match, to the extent possible, invoices to POs, etc.
• use advanced analytics to identify not only the greatest costs but the greatest opportunities available to the organization based on PPV (purchase price variance), market opportunities, consolidation, demand management, substitution, etc.
• use collaborative tools to involve all stakeholders and make sure processes are automated to the extent possible

Because modern technology is far superior for tactical processing (thunking) than we are as humans. However, the leaders understand machines, while they can augment our intelligence with finely tuned applications, cannot think and leave the final decisions to the humans. Technology is applied appropriately for maximum success.

As Hackett says, the bottom line is that Digital World Class HR organizations are better at enabling their companies to succeed. They have streamlined the day-to-day transactional elements of their operations, and through systematic use of global business services and process automation have freed up an additional 12% of their teams’ efforts to focus on value-added activities. Now, they can more effectively focus on attracting, retaining, developing and engaging employees. The right digitalization helps people, and that’s why the right digitalization helps Procurement.

Gartner, which promotes the use of Generative AI in customer service, even though it did place Generative AI on the Peak of Inflated Expectations on the Hype Cycle for Emerging Technologies, just inadvertently made the best case for never, ever, ever using AI anywhere in the supply chain, including Source-to-Pay, and we love it!

In a press release on their newsroom in late September, where Gartner Says 80% of Supply Chain Not Accounted for in Current Digital Decision Models, the subheading clearly stated that Digital-to-Reality Gap Shows Current Technology Use Fails to Improve Outcomes for Supply Chain Decision Makers.

As a result of this “digital-to-reality” gap, Gartner’s research, based on an analysis of 600 survey responses of supply chain decision makers, not only found that current use of digital models to analyze trade-offs made no meaningful impact on the rate of good decision outcomes but actually found that slightly more bad decisions were made with the use of digital tradeoff analysis than without and marginally increased the percentage of bad decision outcomes. Moreover, More than half of supply chain leaders reliant on digital technology to make a recent strategic decision told us that they felt they would have landed on better decision outcomes without the use of their models, and our analysis suggests that they are correct.

In other words, if source-to-pay and supply-chain decision makers cannot even make decisions when relying on traditional, focussed, machine learning and modelling technology, there’s no chance an unpredictable probabilistic incarnation of Artificial Idiocy that randomly changes its output by the millisecond is going to make good decisions. And the reason is the same — just like traditional (guided) (machine learning) models require good data and a digital representation that covers the majority (if not the entirety) of the process and relevant variables, so do Generative AI models and, in just about every organization on the planet, this necessary digital representation DOES NOT EXIST!

As a result, applying AI without the data it needs to have even a snowball’s chance in h3ll to make a decision is pretty much guaranteed to lead you to worse decisions than you, or any other intelligent human with a decent understanding of the situation, will make without the use of any technology whatsoever.

You don’t need AI, you need end to end process modelling, data collection, data enrichment, data validation, and the ability to use those end-to-end digital tools, interpret the data and recommendations, and make good decisions off of that. And since, with the current rate of digitization, it’s unlikely the majority of organizations will go from 20% supply chain digitization to 80% supply chain digitization (which is the minimum level of digitization you should have before even considering any AI, even for inconsequential decisions) by the end of the next decade, you should not even have AI for decision making on your future roadmap before the next decade rolls around.

the doctor doesn’t say this often, but thank you, Gartner. (Because it really is the case that stupid is as stupid does.)