Category Archives: Technology

Valid Uses for Gen-AI!

the doctor has been told he’s too hard on Gen-AI. He doesn’t think he’s hard enough, but there are those who keep insisting that Gen-AI has some valid uses. And they’re right, it has some. Not the uses that you need it for, but actual uses nonetheless.

So today, in a rare moment of weakness, he’s going to acknowledge those uses. Soak it in. He may never do so again.

1. Ensure your insurance / bank only covers and lends to people you like.
One of the great things about Gen-AI is that almost all models are biased, and it’s really easy to train them to be as biased as you want. Only want your health insurance to accept only young people between 25 and 40 with no family history or indicators of any illness whatsoever? No problem. Don’t want your bank to approve a loan to anyone who isn’t an all American Christian white? No problem. Race-Biased Gen-AI to the rescue!

2. Have it make up a new story for your child who constantly wants new stories every night.
Train it on thousands of stories kid suitable and it will make up a new story every night (with a high probability of most those stories being safe and suitable — chances are only a few will scare them into therapy). Your kid will be happy (at least until they get scared into therapy) and your brain will get the rest it needs at night (so it can start worrying about how it’s going to pay for that therapy). Put those constant hallucinations to use. It’s your own personal Scheherazade, with just a little bit of Grimm and occasionally a bit of King (Stephen).

3. Incite the mob.
Need a mob behind you to get your cause front page on the headlines? Incite a mob to cover your theft attempt at a corporate headquarters above a luxury department store? Maybe even help you overthrow a capitol? No sweat! Program that Gen-AI to be as hateful and incitory as possible and have it pump out fake news propaganda 24/7 until you have the mob you need on your side and there you go!

4. Scam the Scammers. (Or at least keep them busy and out of your inbox.)
Most scammers will keep trying as long as someone is responding to them (and eating up their time). Guess what AI has a lot of — GPU time. Most models have 10,000 (or more) GPUs at their disposal. That’s a lot of scammers an AI can tie up for you. (Especially if they can’t differentiate easy pickings Grandpa Joe from a very agreeable but completely broke GrandpAI Joe.)

5. Take down a rival’s network.
Simply train in some sleeper behaviour for a few months into the future, and once the competition is done with their tests and trust it … poof … down goes their network.

And if you want to be truly evil, you can always use Gen-AI to

6. Ensure your terror campaign is as lethal as possible.
We’ve read the stories of how even recent tests of self-driving systems decided to ignore the shadows of what were actually people RIGHT in front of them and drive into those shadows at full speed. A few minor alterations and instead of avoiding people-like figures and shadows, it will be the murderous trolley that tries to kill as many as possible. And who says you have to limit it to trolleys? Use it to program bomb-bearing drones and it will seek out the densest crowd possible. And so on. And yes, we went to a very dark place, but just where do you think AI is taking us? There are currently NO bright outcomes. Ponder that before you go singing its praises.

Of course, if you just want to be a little chaotic around the house, and only take that first step down the dark path, just hook up it’s hallucinatory outputs to a random direction generator and use it to:

7. Power your Roomba.
Your pets will think it’s truly possessed!

So there you go — 7 valid uses of Gen-AI. You decide how many of them you want to use.

Enterprises have a Data Problem. And they will until they accept they need to do E-MDM, and it will cost them!

insideBIGDATA recently published an article on The Impact of Data Analytics Integration Mismatch on Business Technology Advancements which did a rather good job on highlighting all of the problems with bad integrations (which happen every day [and just result in you contributing to the half a TRILLION dollars that will be wasted on SaaS Spend this year and the one TRILLION that will be wasted on IT Services]), and an okay job of advising you how to prevent them. But the problem is much larger than the article lets on, and we need to discuss that.

But first, let’s summarize the major impacts outlined in the article (which you should click to and read before continuing on in this article):

  • Higher Operational Expenses
  • Poor Business Outcomes
  • Delayed Decision Making
  • Competitive Disadvantages
  • Missed Business Opportunities

And then add the following critical impacts (which is not a complete list by any stretch of the imagination) when your supplier, product, and supply chain data isn’t up to snuff:

  • Fines for failing to comply with filings and appropriate trade restrictions
  • Product seizures when products violate certain regulations (like ROHS, WEEE, etc.)
  • Lost Funds and Liabilities when incomplete/compromised data results in payments to the wrong/fraudulent entities
  • Massive disruption risks when you don’t get notifications of major supply chain incidents when the right locations and suppliers are not being monitored (multiple tiers down in your supply chain)
  • Massive lawsuits when data isn’t properly encrypted and secured and personal data gets compromised in a cyberattack

You need good data. You need secure data. You need actionable data. And you won’t have any of that without the right integration.

The article says to ensure good integration you should:

  • mitigate low-quality data before integration (since cleansing and enrichment might not even be possible)
  • adopt uniformity and standardized data formats and structures across systems
  • phase out outdated technology

which is all fine and dandy, but misses the core of the problem:

Data is bad (often very, very bad), because the organizations don’t have an enterprise data management strategy. That’s the first step. Furthermore this E-MDM strategy needs to define:

  1. the master schema with all of the core data objects (records) that need to be shared organizational wide
  2. the common data format (for ids, names, keys, etc.) (that every system will need to map to)
  3. the master data encoding standard

With a properly defined schema, there is less of a need to adopt uniformity across data formats and structures across the enterprise systems (which will not always be possible if an organization needs to maintain outdated technology either because a former manager entered into a 10 year agreement just to be rid of the problem or it would be too expensive to migrate to another system at the present time) or to phase out outdated technology (which, if it’s the ERP or AP, will likely not be possible) since the organization just needs to ensure that all data exchanges are in the common data format and use the master data encoding standard.

Moreover, once you have the E-MDM strategy, it’s easy to flush out the HR-MDM, Supplier/SupplyChain-MDM, and Finance-MDM strategies and get them right.

As THE PROPHET has said, data will be your best friend in procurement and supply chain in 2024 if you give it a chance.

Or, you can cover your eyes and ears and sing the same old tune that you’ve been singing since your organization acquired its first computer and built it’s first “database”:

Well …
I have a little data
I store it on my drive
And when it’s old and flawed
The data I’ll archive

Oh, data, data, data
I store it on my drive
And when it’s old and flawed
The data I’ll archive

It has nonstandard fields
The records short and lank
When I try to read it
The blocks all come back blank

I have a little data
I store it on my drive
And when it’s old and flawed
The data I’ll archive

My data is so ancient
Drive sectors start to rot
I try to read my data
The effort comes to naught

Oh, data, data, data
I store it on my drive
And when it’s old and flawed
The data I’ll archive

Let the Bloodbath Continue!

Note the Sourcing Innovation Editorial Disclaimers and note this is a very opinionated rant!  Your mileage will vary!  (And not about any firm in particular.)

In a recent LinkedIn post, THE PROPHET tells us there is a Consulting Bloodbath starting, especially in the Big 5 (and their strategy firms). All the doctor can say to this is Good Riddance! and It would be even better if they battled it out Gladiator style! (After all, it’s been 28 years since American Gladiators ended, time for a rebrand and a relaunch with a little bit of MXC, which ended 17 years ago.) But we’re getting ahead of ourselves here …

Basically, according to THE PROPHET, firms are worried about the economy and growth headwinds ahead (this is also why investors have yanked money from equities and lessor-rated debt in recent weeks), and this includes tech/dev teams within consulting firms. In some cases lucky consultants are put on the bench and told they have six or nine months to find their next gig, and in others (and maybe the doctor is reading a bit between the lines here) they received their pink slips faster than they could say please Jack Robinson.

The bit about tech/dev teams makes the doctor happy because,

  • these are not tech firms, and they are selling modern analytics/automation/AI solutions they often have no business selling (and no real capability to deliver at even an average level unless they recently acquired a firm that does — remember what they initially got big doing, that is what they do better than anyone else)
  • they are not structured for proper SaaS development and deployment and are NOT SaaS enterprises
  • most of the “talent” they are using are not “top” talent, and if if they are “top” of their class when they are hired, they still need mentorship and experience to become “top” talent, mentorship and experience they are NOT going to get a lot of at a Big X until they start climbing the ranks (as there are too many hires each year for one-on-one mentorships to be practical, it’s usually one mentor per team)
  • the Big X cost structures are too high for mass market penetration; only the F500 / G3000 can afford them, but they still shouldn’t be using them automatically because overpaying for anything that can be commoditized by a SaaS or servifes vendor doesn’t deliver the value they need in inflationary times where supply chains are breaking daily (and instead the Big X should be used for where they deliver the best value — see when should you use a Big X)

And before you chastise me from apparently taking pleasure in people getting fired, think it through! If you do you will realize

  • the true “top” talent is going to end up at appropriate SaaS/Tech companies (or SaaS+IP powered niche automated services consultancies where their true talent/drive really is) where they can get the mentorship they need to grow and reach their full potential (and possibly rejoin a Big X later, either by choice or through acquisition0 because
  • Big X being forced to pull out of (chasing) inappropriate custom SaaS/tech deals/engagements will open up the market back up for those companies that are well positioned, who can start growing and pick up this top talent, and, moreover, give Big X a chance to focus on where they offer the greatest value, can easily guarantee a return on a high dollar investment, satisfy the customer on the first project, and get repeat business for life (see when should you use Big X)
  • the “talent” that is not ready for the tech market will either go back to school or find their true calling (before going down a path where they will eventually get overwhelmed, be unhappy, or both; we can’t have the next generation burn-out in first world countries where a very significant portion of the aging population will not be of working age in the very near future)

Plus, shift happens! (How many of us have been restructured, rightsized, or outsized from a job by financiers and lawyers who think they can run a complex enterprise from a balance sheet or understand advanced technology and engineering when they can barely gas up the Jaguars and Mercedes they drive to work everyday?*) Furthermore, given that the average life expectancy at a job these days is 4 years, this talent might as well learn about, and get used to it, now when parts of the economy will be rebounding (and they have opportunity ahead of them), versus getting their @ss3s unceremoniously throw to the curb next time the market drops.

And if, for some reason, a Big X Consultancy (which did not start in tech but in accounting/tax, operations, strategy, etc.) is where they belong, then let them prove it in a battle royale! Forget about sitting on the bench waiting and hoping to get invited to a sales call where they can sell a project to work on, put them in the Arena! When a Fortune 500/Global 3000 needs a consultancy, force them to make their selection in the arena where the consultant leads will battle it out modern gladiator style! Not just a Dragon’s Den pitch, they have to battle it out to even get the opportunity to pitch — prove they’ll do whatever it takes to deliver value at the hourly rates their employer is charging!  (Yes, we’re kind of joking here, but if it is where they belong, they should have no problem proving their worth!)

Thoughts?

 

* If the apocalypse is nigh it is largely because some rich benefactors, not even involved in the day to day running of the company, and likely never involved with the company at all, looked at their spreadsheet models and forced the engineers who actually know how to build things out of the C-suite, allowed Gen-AI to tell them how to do technical jobs, and then elected populist pinheads as Prime Ministers and Presidents to tell them balance-sheet management is okay. And let’s not forget that, as per the OECD PISA data, statistically most of them shouldn’t even be able to do high school math competently!

Firms that Rely on Logo Maps and Analyst 2*2s for Tech Selection are NOT Appropriate for Tech Selection!

In our last article, where we described in detail the many, many reasons why logo maps (including the Sourcing Innovation Mega Map on Source to Pay+ with 666 Unique Clickable Vendor Logos which were verified to be valid as of 2024 April 13), we not only reiterated how these maps are mostly useless but explained that your mileage will vary widely between a map created by an analyst who’s likely seen 1/3 to 1/2 of the vendors in depth and a(n) (former) implementation consultant or (want-to-be) influencer from a CPO background who has no in-depth technology education or experience (beyond the systems he used).

Those who read between the lines would have seen this post coming — not only are they not appropriate for tech selection, any firm that relies solely on them or analyst firm 2*2s (which are great if you are searching for some holy smoke to keep the beast of procurement technology at bay) is also inappropriate for tech selection projects.

Your results with such firms will be about the same as the bigger firms with “consulting partner” status with all the (same) big players, as they will ultimately just recommend the same ten firms for your Tech RFP over and over again, whether or not they are the right firms (and solutions) to meet your needs.

In order to effectively select a set of potential solutions for a client, you need to, at the very least:

  • understand the processes the client needs to support and the gaps they have
  • understand the solution types needed to support the processes, and the client’s gaps in particular
  • understand the client’s current technology landscape and Technology IQ, including what is replaceable and what is not (since, gosh darn it, some clients are going to hold onto that ERP they overpaid for until you dodge their six-gun pistols and pry the contract from their cold, dead hands)
  • understand the client’s unique situation based on vertical/industry, market size, and geography/culture
  • understand what global vendors support the processes, fill the gaps, synch with the tech stack, and can, possibly through third party integrations/partners, address the client’s unique requirements

This is a tall order. So tall in fact that, despite the growing demand for technology transformation and digitization across the Procurement landscape, outside of a few niche vendors that primarily focus on specific industries and specific solution types, the vast majority of procurement transformation shops aren’t able to fulfill it. Most will

  • have the processes down pat, they are consultants after all!
  • have a decent understanding of the common/core solution types, as they smart ones will actually read the expository articles written by the analysts (that they have access to anyway*)

Some, who employ technology and industry-specific professionals, will be able to build a decent understanding of

  • the client’s technology landscape and technology quotient
  • the unique requirements to look for/enable based on vertical/industry, organizational size, and geography

But few, if any will be able to:

  • identify even a handful of relevant global vendors that take into account the first four requirements

This is because, as pointed out in our last few articles:

  • the space is much bigger than they think, with
    • more types of product offerings,
    • considerably more vendors then they think exist, and
    • considerably more than they can process
  • they don’t have the deep technical background or technical understanding to differentiate between two vendors that speak the same and present applications that look the same in a 60 minute demo, but differ greatly in underlying power, extensibility, integration capability, etc. where you need a deep technical background and/or competitor understanding to tease it out (as well as a deep understanding of Procurement and the competitive [solution] market place)
  • they don’t have a process to do a proper technical assessment, diligence, or tech analysis …
  • and they certainly don’t know how to do a deep assessment by module/area to truly differentiate two solutions to qualify them as suitable for selection if they submit the best RFP

As a result, many consultancies will just do their in-depth process analysis, write up functional requirements based on that, and toss it over the wall to the solution providers to figure out, selecting from their partners if they feel there is enough overlap, then from the upper right in the analyst maps they paid for, and, finally, from the logo maps from their most trusted source. And, as we’ve explained, this doesn’t cut it and is why many sourcing / procurement software selection projects fail to live up to client expectations. Because, and we can’t say this enough, the most you can use logo maps / analyst 2*2s for is vendor discovery. Not validation for your projects!

Now, while the doctor has yet to receive an answer to his transformation process inquiries from any consultancy/service provider that fully satisfies him (he is demanding, after all), he is happy to say that, recently, a few# providers have acknowledged that transformation is going to require getting a lot more intelligent in tech and updating their processes and methodologies to recognize that, while it’s still The Wild West, it won’t be tamed by hope and grit alone — you’ll need the right tools to conquer it (and, FYI, those tools aren’t Gen-AI, they are good old-fashioned predictable, dependable steam- and gunpowder-powered tech solutions in the hands of us old and busted masters; the new hotness has nothing on us).

Secure Download the PDF!  (or, use HTTP) [HTML]
(5.3M; Note that the Free Adobe Reader might choke on it; Preview on Mac or a Pro PDF application on Windows will work just fine)

 

* this is your regular reminder that Sourcing Innovation has never had a paywall and never will for baseline vendor coverage or expository posts; should SI choose to offer books, in-depth [comparative/market] intelligence, or similar IP services, for example, it may in the future sell this non-blog content, but every blog post will remain paywall free — almost 6,000 and counting …

# and we mean few, he can currently count them on his fingers on one hand, thumb not required

Digitalization alone is NOT a cure all to our problems!

A recent post on LinkedIn said digitalization is an emergency because:

  • Health workers are feeling squeezed
  • People can’t find the housing they need
  • Farmers can’t find enough hours int he day
  • Manufacturing firms can’t find the workers they need

And while digitalization is an emergency for some businesses, DIGITALIZATION WON’T SOLVE ANY OF THESE PROBLEMS, because lack of is not the core issue. Since the poster is living in Montreal and the doctor is living in Halifax, we’ll focus on the source of the problem from a Canadian perspective, but the reality is that the majority of countries with these issues has the same source problems:

1) Today, 20% of Canadians don’t have a family doctor, compared to 2001 when it was only 13%. This is because, from 2001 to 2021 we saw a 23% population increase. In the same time, we saw a 12% decrease in health workers. (And things have only gotten worse since COVID, but StatsCan is always a couple of years behind. The Nurses association says we are short 60,000 nurses alone!)

2) The national vacancy rate is 1.5%! Year-over-year rent increases are 8%! One bedrooms in downtown Halifax are going for 2K a month! Pre COVID, they were 1K. Pricing is out of control across all major Canadian cities.

3) The days are the same length they’ve always been. What farmers can’t find is enough seasonal workers, despite the unemployment rate, because they can’t afford to pay seasonal workers a living wage. (And that’s why our migrant farm workers in the bigger provinces are essentially facing modern slavery conditions, as per a UN report.)

4) Manufacturers can’t find the workers they need because of a lack of SKILLED workers. Everything is going tech, but yet our STEM graduate rates in Canada hover around 22%! That’s 1 in 5. But if you don’t have decent math, computer, and electronic equipment skills, you don’t have the skills a manufacturer needs.

Thus, it doesn’t matter how much digitization you apply or how good the systems are, we still have the fundamental problems that:

1) it takes a doctor a certain amount of time to properly diagnose a patient, a surgeon a certain amount of time to do a surgery, a nurse time to put in the IV, check the vitals, talk to the patient to do a cognitive assessment, etc. THOSE TIMES CAN NOT BE SHORTENED.

2) We need to BUILD more housing. We need to at least DOUBLE the vacancy rate so that the housing is WHERE it is needed in a market that is COMPETITIVE and REMAINS AFFORDABLE.

3) We need to HALT INFLATION, reduce farm taxes, bring back critical subsidies (and not invent new “grocery taxes” to halt greed mongering, the CEOs will just hike prices for consumers in the end), and make farm work a living wage again.

4) We need to promote and train for STEM.

Only then will digitization help because all it can do is

1) minimize the downtime between seeing patients

2) help people find those affordable units faster and submit applications faster and do the background checks faster

3) help farmers minimize their planning and “back office” operational time

4) help manufacturers get the most from the skilled employees they hire (but there IS a limit on productivity increases)

This is because, as the doctor has said time and time again, all computers are good for is thunking, not thinking, and all Gen-AI does is exacerbate problems because you don’t know if the sh!t it is making up (that’s what generative means — make stuff up) is correct or not! As a result, they can be great at automating tactical data processing and bring the following benefits:

1) centralized country-universal health records, integrated systems, and remote home/self care support — if a nurse or doctor can instantly get all the data on your history they need, they can review it all before seeing you, assess with knowledge, and get to a likely correct diagnosis, and treatment faster; also, if they never have to rekey existing data and just add to the record, it’s less time between patients

2) better three-way support for landlords to find tenants and comply with legislation, tenants to find properties that meet their needs and keep landlords honest (with reviews and instant reports to), regulators who can ensure everyone is using the system properly and fairly

3) easy planning, monitoring, management, and farm-tech selection best suited to the needs of the farm workers based on farm size, location, produce/livestock, and workforce

4) better procurement, production planning, (up)skill(ing) maintenance, design, testing, etc. — maximize every hour on engineering stuff, not back-office paper pushing

But none of this solves the core problems we have or helps if the workers aren’t there to begin with!