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.

AI proponents, who keep telling us it stands for Artificial Intelligence which does not exist, keep pushing the benefits of AI while sweeping all of the detriments under the rug so you’ll sign that multi-year deal now (and they’ll have the money to keep researching the technology in the hopes that their continued efforts will prevent the bad from happening again). (And while the tech will get better and the success rates will improve, the very nature of the technology they are deploying is such that it is impossible to prevent the bad because the technology is not intelligent and not deterministic, as per our many previous posts on the subject. The best will eventually get success rates up to 99%, but that’s still a 1% failure rate and, in Procurement, it only takes one catastrophic failure to wipe out all of the successes made in the rest of the year. It only takes one bad decision that shuts down a multi-million production line, results in class action lawsuits for the release of unsafe products in to the market, or results in seizures and destruction of millions of dollars of inventory when the products violate import restrictions, etc. to deliver masses losses.)

And the benefits they push typically fall into one of the seven patterns of AI that the AI proponents keep telling us AI will deliver. To help you better classify the false promises, we’ve decided to cover the seven patterns, example promises, and realities you will encounter if you implement current iterations of technology that employ Artificial Idiocy.

HyperPersonalization
Promise: The system will adapt and evolve over time so that when you log in, you see exactly what you need to see, in priority order for you.
Reality: The system comes with a set of widgets, and all the AI does is reorder the alerts/notifications/tasks/reporting views in each widget based on a priority weighting where the weights are recalculated based on recency in access so, at the end of the day, it just keeps showing you what you just looked at and truly important alerts, because you haven’t regularly looked at them, are at the bottom of the widget and off the screen since you never scroll down inside the widget. Classic rule based systems work better.

Recognition
Promise: 100% effective automated invoice processing, routing, and approval
Reality: it only recognizes invoices from suppliers who invoice regularly in a format that never changes; and only if the line item descriptions are never abbreviated; and only matches properly if the PO number is included; 10%+ of invoices have to be manually processed, and abbreviation errors cause misclassifications of units that sometimes don’t get caught until after payment is issued

Conversation and Human Interaction
Promise: natural language interaction, including voice to text
Reality: due to the ambiguous nature of the English language, the number of follow on questions the AI has to ask just to produce a simple report requires the user to spend five minutes giving clarifications and specifying parameters that could be point-and-click selected in 30 seconds; efficiency is flushed down the virtual toilet

Predictive Analytics & Decisions
Promise: what the price will be when, and why
Reality: works really well 95% of the time, with price accuracy often within 2% to 3% and demand predictions (outside F&B and other highly unpredictable industries) within 3% to 5% at the macro/rollup level, but when a trader illegally tries to run up the market with excessive trades, and prices start to skyrocket and the algorithm doesn’t know this is unusual/short-term, it may predict extreme price increases at contract expiry in 6 months, and automatically early renew the contract for you at rates 30% to 130% higher than it should (before the costs become unaffordable)

Goal-Driven Systems
Promise: Sustainable Buys with Cost Savings
Reality: Unsustainable buys as cost is overweighted and over-prioritized in all situations; ’nuff said

Autonomous Systems
Promise: They will procure automatically and do better than humans
Reality: They procure automatically and occasionally do better than humans, usually do on par, and occasionally make such disastrous decisions that the company does well to avoid bankruptcy …

Patterns & Anomalies
Promise: They will detect unusual spending patterns and detect the best opportunities for savings and the most likely instances of fraud
Reality: Unusual spending patterns don’t mean savings, and usual spending patterns don’t mean absence of fraud, and you get all kinds of “priority alerts” with no savings opportunities while the largest opportunities go unidentified and collusion frauds are never detected

At the end of the day, as we’ve said again and again, Procurement Automation: Good, (AI) Automated Procurement: Bad. Only you, dear reader, are intelligent and, thus, only you should do the thinking and only use technology for what it’s good at, the thunking.