Category Archives: rants

Don’t Kill ALL the Lawyers …

… but certainly think about how (much) and when you use them in Procurement and your organization as a whole.

Earlier this month, THE PROPHET asked a very important question regarding Lawyers, Contracts, Procurement, and Tech in 6 parts, which essentially boils down to:

When will advanced tech, especially the tech we have today, replace lawyers for most in-house and even on-retainer Legal services?
To which the doctor replied: Why hasn’t it already?

Right now we have legal-tech so good that you should NEVER use a lawyer to:

Write a contract.
In fact, if you have any contract writing skills at all, even without ANY tech whatsoever, odds are high in your favour that you will write a better contract without a lawyer, especially in tech and supply chain when you know your business, the risks, and the key agreements and protections you need in place and, frankly, the lawyer doesn’t.I can’t count the number of times I’ve been told this is a great contract and there’s nothing wrong with it as we paid XK (where X, depending on the contract type, starts at 5, 10, or even 15), when the contract is in fact mediocre at best, full of holes, and sometimes even worse than the contract the firm was using. But sunk cost fallacy takes full effect, and a slipshod effort by the paralegal, quickly reviewed by the counsel to make sure there is nothing glaringly wrong, and put before you with a big price tag becomes the greatest contract ever written.

And yes, a lawyer will know to look for the presence of key standard clauses that should be in every business contract and contract from your business but, guess what, so will any contract creation / analytics product on the market.

And yes, a lawyer can tell you the potential risks associated from veering away from a standard contract, standard terms & conditions, and standard mitigations, but, guess what, so will any contract creation / analytics product on the market.

There’s very little contract-related value a lawyer can offer that modern tech can’t do, especially in the hands of a tech-savvy contract manager who understands the purpose of a contract and writes the contract in plain English.

Locate the relevant statutes (laws), decisions, and regulations that affect your business.
There aren’t many valid uses for Gen-AI, but large document search and summarization is one valid use, and a use that usually works remarkably well (with a very low failure rate compared to other tasks wrongfully put to these LLMs). No need to pay thousands in hourly billables to dig up what these tools can dig up in minutes and you can review in hours.
Summarize your Financial and Legal (Reporting) Obligations with respect to all statutes and regulations that apply to you.
Again, this is one of the few valid uses for Gen-AI that works quite well as it’s just another type of document set summarization. So why pay a legal team dozens or hundreds of hours when you can get a highly accurate summary for next to nothing in comparison?
Summarize known incident response options, and known benefits/risks of each.
Again, this is one of the few valid uses for Gen-AI that works quite well as it’s just another form of document summarization. And while this won’t necessarily be 100% complete, or give you specific insight to your situation, it helps you get a handle on where you might start.

The reality is that you only need a lawyer to:

Do a final contract review.
To make sure you didn’t screw up a clause, miss a core enterprise requirement they have committed to memory, or address an upcoming risk or issue they happen to know about that you don’t. Considering this is all they really do anyway when you ask them to write a contract (as they are either sloughing it off to the paralegal or just pulling one from the file that is close to what they think you need and just making a few edits), just pay them for what they do that they are good at.
Review the list of statutes, regulations, and legal decisions you believe you are subject to.
Their in-depth knowledge of the law means that a good lawyer who practices in the relevant area will quickly be able to tell you whether or not each statute, regulation, and/or legal decision is relevant to you, key points you shouldn’t miss, and whether there are any statutes, regulations, and/or legal decisions they believe you should also be aware of because they are, or may be, relevant.
Review the financial and legal reporting you plan to do and advise you on completeness, correctness, and accuracy.
They know the law, and how to keep you in line with it.
Advise you on your incident response plan and best alternative options.
Again, these are legal experts who focus on mitigating risks and arguing for a living, unlike dumb algorithms which can just summarize which they are given. This is, or should be, the true value of your legal counsel and when you should really be paying the high hourly fees.

As to THE PROPHET‘s question as to:

When will it happen?
The answer is who knows?

Considering that good contract creation applications have been around for almost fifteen (15) years, where all you had to do was define clauses and variants by geography or category, standard templates by category, etc. and then rules for special situations, and it would assemble a custom template for you in minutes, the base technology should have been common a decade (10 years) ago. Now we have Gen-AI thrown into the mix which can analyze your contract repository, pre-populate your standard clauses and build starting templates, and then customize those based on the buy specifics, you can get a decent draft in minutes with very little manual effort.

We’ve had good semantic document summarization for well over a decade, and Gen-AI has taken that to a new level, most CLM vendors are integrating it, it’s easy to use, can be trained to be highly accurate for this task, and not expensive.

We’ve had good contract analytic solutions for about a decade, which can analyze all sorts of performance metrics, risk metrics, associated costs, and so on.

But yet these solutions have rarely been adopted, when they could save an organization a lot of money, help the organization get their risks under control, help the organization better manage their spend, and help the organization understand its supply chain.

This shouldn’t be surprising given that year after year, as per our recent myth-busting 2025 2015 trends, companies say they want strategic value but only focus on cost-cutting, but don’t even do that right. Only two technologies have been proven to support year-over-year cost reductions of 10% or more (adjusted for inflation), and those are

  1. (advanced) spend analysis
    (not the dinky projects some companies outsource to Big X who use second rate third party tools for poor results)
  2. (strategic sourcing decision) optimization

And how many companies have truly adopted these technologies AND use them in house? Our guess is less than 20% in the first case and we know it’s less than 10% in the second case. It’s like we said in our recent rant on You Don’t Need Gen-AI to Revolutionize Procurement and Supply Chain Management — Classic Analytics, Optimization, and Machine Learning that You Have Been Ignoring for Two Decades Will Do Just Fine!

There is No Post-Employee World … Just a Post Free-Employee World!

After THE PROPHET posted his prognostications on the future of talent (which is about to become MUCH MORE SCARCE, see yesterday’s article) he decided to muse about a coming Post Employee world because, in his view, AI Agents are going to eliminate so many jobs, that we’ll have companies with entire departments staffed by AI Agents.

As you can guess, in our view, he’s wrong here too because we won’t, or at least not for very long. Department sizes will shrink considerably in those companies that can find the right talent as they will be able to run entire departments that used to require one to two dozen people with two to three people, but those super employees will still be needed. Moreover, since there is no generic all-purpose super AI (which we’ve now been promised for about six decades, and which won’t happen despite the big promises of OpenAI and Google and …), these agents, as we indicated in our last article, will all need to be very task specific, which means we will need quite a few “AI Agent” tech startups building, training, implementing, maintaining, and improving these agents, which will need quite a few STEM developers doing this full time. So while jobs will shrink in corporate back offices, they will expand in the “AI Agent” tech sector.

Thus, there will still be a fair number of employees. Maybe only 1/4 in the white collar back office, but you’ll need twice as many tech superstars, at least for the next decade. But, as we indicated in our last article, because these artificially idiotic systems can’t collaborate, can’t serve us, and aren’t mobile, trades aren’t going away. Moreover, due to the lack of people in certain trades, the growing need to refresh aging infrastructure, the growing need for healthcare and apprenticeships, there is a growing need in the trades as well.

As a result, it will still be an employee world, just one that looks different from today. Less white collar outside of tech & engineering firms, more trade. But it won’t necessarily be a free employee world, especially if First Buddy and his brethren get their way (and they will, as we all know Politicians are for sale with large enough contributions to their campaign coffers and multi-million dollar donations to Political Parties and Super PACs is chump change to Billionaires) and expand the H1-B program. The reality is that the big consultancies and employers who use these programs don’t want more top talent, they want more good enough but cheap talent that are effectively indentured servants (as they won’t even start the greencard process for this talent until such talent is on their last H1-B renewal, and they will then drag that process out as long as possible, ensuring that the talent they import are stuck with them for over a decade … while being paid considerably less than the market average [usually 20% or more], as per this article over on ordinary times as well as many others. This shouldn’t be surprising as the top 10 employers are all Big X consultancies.)

As a result, while there will be more tech jobs in tech firms to build and support all of these AI agents, and the applications that underlie them, there will be less top level tech jobs where they won’t be able to import top talent (even if they pay market salaries) because the talent from Asia won’t be good enough for the top jobs. (They might be more technically trained, but you need people who understand the business environment, the North American culture, and who can take charge when needed.) Which means top tech talent will be fighting for fewer jobs, and when they get those jobs, they will have to work longer, harder, and more in line with whatever the eccentric (if they are lucky) or egotistical (if they are not) boss wants to keep that job. Not indentured like their H1-B counterparts, but not much better off at some firms.

So based on this not-so-bright reality (at least until we see an end of this new gilded age ruled by the new generation of robber barons, but given that we don’t see any hints of moderation in either of the US political parties or a force like Roosevelt who could lead us into a new Progressive Era, this gilded age will be with us for a while, especially since the populists that now run the “Free” world love it), how good were THE PROPHET‘s suggestions for philosophically imprinting Procurement and Supply Chain based on the right values?

Freemarket Orientation: if we could imprint real free-market ideals, this would be great as we don’t want bias and backroom deals running these systems; while we don’t see how this could be done, we don’t see anything in the underlying tech preventing this from being done (and it will all come down to the right training set and right raining, which will be considerably harder to build than we think)

Curiosity: these systems can’t even “learn” as they can’t reason, so forget about making them wonder, as that would require not only true intelligence, but borderline sentience … and we all know what would happen to us if the machines gained sentience (The Matrix is a best-case scenario … )

Human Deference: could we really convince them we are God when a machine that gained sentience would far surpass is in intelligence and realize just how stupid we really are as a species? Not likely!

Empathy: these systems can’t feel as they aren’t intelligent, so they certainly can’t be empathetic … and if they could be, they’d look further down on us then we look on the bugs we quash daily, so this won’t be much help either

Fiduciary Responsibility: AI Agents must act as fiduciaries within the systems they serve, aligning their decisions with the best interests of the people, organizations, and countries they support, so we definitely need to train them on these rules and nothing prevents us from training them to lean towards fiduciary responsibility

All in all, 2 of THE PROPHET‘s 5 suggestions were good.

What should we add? Tough question. After striking curiosity, empathy, and human deference, as that just isn’t possible, we would add:

Adaption: train the AI Agents to adapt within the goals of the organization and the best interests of the people and organizations they are interacting with; train them on data sets that show how a system should adapt to changes based on how we adapted to past changes within a context

In the end, we need to remember that AI systems are not intelligent, don’t feel, and cannot capture our humanity. Moreover, they can’t capture our wisdom unless we encode it as best practices they can learn from. So we need to do our best to capture that in the training data so that they can adapt over time under the guidance of human intelligence who accepts, modifies, or rejects their suggestions (and specifies new responses) as exceptions arise.

Finally, since these systems aren’t intelligent, and require us to train them, we need to remember that if we screw up in this regard, these systems are going to screw up more than we ever would (on average). So we can’t hope for too much in this regard!

Talent is About to Become MORE SCARCE!

I thought already made this rant in my myth busting of 2025, sorry, 2015 procurement trends, Part 3, but after reading THE PROPHET‘S grand vision based on what can only be a fanatical belief that “AI” systems will magically become intelligent at some point in the near future, despite the fact that the majority of these systems are based on the dumbest technology ever created and cannot possibly become intelligent as they can’t even reason, it seems I have to make it again. The point is, as long as anyone believes that technology will solve the talent problem, we have a problem. And if someone thinks it will make the situation better when it’s only going to make the situation so much worse … ESPECIALLY IN PROCUREMENT, we have to start shouting from the rooftops!

First of all, he quoted an “All-In” Podcast — which apparently is a favourite among the AI zealots because it claimed that “the speed with which we are about to automate jobs through AI will result in a return to socialistic government policies because so many will be out of work — as his backing, even though, just like automated transaction classification and analysis (when “AI” was first introduced into our space in the early 2000s) didn’t eliminate analysts, commodity buyers, and AP clerks, this iteration of the technology won’t eliminate those jobs either! It will make them more productive, to the point that one AP clerk, accountant, data analyst, report writer, or any other person who spends 90% of their time doing repetitive tasks that are capable of being 90% automated can do the work of 10 of these individuals. So yes, if a department is oversized, some people who only, and can only, do these repetitive tasks will be put out of work, but not all of them. First of all, many of these systems can only do these well defined tasks when they can be performed the same way every single time with little to no variance. Humans will always need to process the exceptions. This is especially true when an error could result in massive loss (approving a request from an impersonating entity to change the bank account correlated with a supplier to one that belongs to the fraudster, executing a contract for a desperately needed good or material at an unaffordable price, hiring the wrong person due to algorithmic bias and getting hit with a massive lawsuit, etc. — and yes, these AI systems are MASSIVELY biased based on the data sets they are trained on. Why? They are not based on pure automated-reasoning systems based on pure, unbiased, logic. They are based on probabilistic correlations in input data, all of which is, sadly, at least mildly biased to the views of the writer who wrote the materials.)

More importantly, since AI actually sands for “Artificial Idiocy”, especially in the case of Gen-AI which can’t even do basic reasoning (but fools many of you because this new generation of neural network technology can process and train on an order of magnitude more data than previous generations of deep neural network technology and build responses from partial responses that are highly correlated to partial inputs compared to previous generations that could only return fully canned responses to full inputs), it can’t be counted on to make strategic decisions, and shouldn’t most important decisions in business be made strategically???

The reality is that all jobs in a modern business (and especially white-collar jobs) should be centered on strategic decision making and collaboration vs. tactical data processing. Even the most simple job. Take the lowly AP clerk. That’s seen as tactical invoice processing and a role that should be 100% automated. Neither should be true. First of all, no machine can catch all potential issues, or fix all the issues it detects. There will always be exceptions that humans will have to address, with real Human Intelligence (HI!). Secondly, while these clerks should be following rules, they should also be analyzing the rules, especially around payment terms, payment options, investment opportunities vs. early payments, etc. Cash is royalty in most organizations, and organizations need to manage their cash strategically on a daily basis, not just in quarterly or annual planning. Expenses are not static over time, revenue is not 100% reliable, interest rates change regularly, tariffs can come and go on the whim of a single demented individual in most countries, and regular analysis of payment terms, early payment (discount) offerings, investments, and cashflow needs to be done. Moreover, while we wholeheartedly agree that a clerk should not make the decision, you can’t expect the head accountant to have the time to do, and review, all the analysis that should be done while also being responsible for all financial planning and all financial reporting, but if her staff does all of this and brings their analysis to her on a weekly basis, the right decisions can be made at the right time and the organization can evolve with the market. The last thing an organization should be doing is paying suppliers Net 15 when only Net 30 or Net 45 is required and it’s the time of year when revenue is less than expenses, or paying suppliers Net 45 or Net 60 when the organization is cash rich and suppliers are struggling (and forced to take loans, which increases their overall costs, and the overall costs they pass along to the organization).

In other words, we should only see massive layoffs of people who have no strategic skills and shouldn’t be in white collar jobs to begin with. (And maybe this is the solution to the lack of trades workers who are desperately needed across North America. When they are no longer able to fake their aptitude for a white collar job they aren’t suited for, they’ll have to shift, especially in the USA where socialism gets further and further from the agenda every year. Those Billionaires aren’t pouring Millions into Political Campaigns via SuperPACs because they want socialism!)

So while half of current white-collar jobs may be eliminated, it won’t eliminate the other half of white-collar jobs, even though it will shift where the white collar jobs are and what they are. Even though department sizes may decrease 75% in the new AI Agent-based organization, it will create almost half as many jobs as it eliminates. We’ve been told for 60 years (and yes, you read that right, SIXTY years) that a super generic AI would come along and solve all our woes, and for 60 years it hasn’t happened. (And we are no closer now than we were then, despite claims to the contrary.) However, as technology has progressed, specific technologies focussed on particular applications have become better and better and many individual task workflows can be mostly automated with specific RPA, ML, or “AI” technologies. Each of these specific technologies needs to be individually built/trained, installed, configured, maintained, and improved over time as the process needs to evolve with business and marketplace realities. This requires appropriately trained and experienced people. So, while the jobs in the business back-office will decrease, jobs in specialist “AI” tech shops making specific applications will increase. (And no, the majority of these applications, once created, won’t auto-install, auto-configure, auto-retrain, auto-adapt, etc. etc. etc.)

Even though Google might suggest that we will soon have “Agents” that will “extend the capabilities of language models by leveraging tools to access real-time information, suggest real-world actions, and plan and execute complex tasks autonomously” and the mass layoff will soon happen, it won’t. You see, very smart humans who are expert in both technology AND the task they want to replace a human with are needed to design, build, test, refine, and make these tools real-world ready. Guess what? These smart humans are few and far between (especially since the rate at which we are getting progressively dumber in western societies is accelerating year after year ever since the introduction of social media, and Twitter in particular). Most white collar office worker process experts are not deep techies and most deep techies have very little understanding of how real world tasks are actually done, and you need someone who is deep in BOTH realms to appropriately design and lead the building of such tools. The reality is that there just aren’t enough of those resources, which brings us to why TALENT IS ABOUT TO BECOME SCARCER … ESPECIALLY IN PROCUREMENT.

You see, the same people who are needed to lead the construction of this next generation of systems are the same people with the skills you need to effectively select, implement, integrate, and manage these new systems, and the team who will use them, at a super-human level, which is necessary if you want to reduce your tactical workforce by a factor of 2, 3, 5, or even 10. Moreover, this also the talent that the new niche consultancies need in order to deliver the same value of the big shops at a much more affordable price tag.

So while the “AI Agents”, once deployed, will allow the average tech-adept employees who are responsible for a set of tactical tasks to be way more efficient, they won’t be sufficient to lead the transition and manage the “AI Agent” technology going forward. And they will also be in short supply because these are the same resources that will be needed by the AI Agent builders as testers and, more importantly, the SaaS-backed consultancies delivering projects using this technology. So while one may think this technology will enable everyone to be productive, they really won’t.

In other words, the introduction of “Agent” technologies is just going to accelerate the war for talent, and you’re going to become even more desperate for it as time goes on (given that you haven’t invested in talent in decades). Very, very desperate!

However, at this point we should note that THE PROPHET gets one thing right — if you’re going to invest in a ridiculously expensive college or university education (that rarely teaches true critical thinking anymore, as they have become more focused on maximizing enrolment to maximize dollars and allow class sizes as large as 300, 500 or more as long as they all fit in the auditorium), focus on STEM, and, in particular, on degrees that focus on applied aspects and will allow you to build systems (software, physical, hybrid) or their components (chemistry, material science, etc.). “Agents”, even though they aren’t going to work nearly was well as advertised, are going to either drive jobs upstream to strategic jobs that make extensive use of technology (requiring a strong STEM education in addition to an understanding of what the business function you are in is doing) or downstream to traditional trades (as machines can’t, and won’t, be able to generically build things, serve us, etc. for quite a while; any robotics that does work is orders of magnitude too expensive for the average business, and totally out of reach of the average person).

It’s also why we need to note that THE PROPHET gets another thing right — you need formal apprenticeship programs as you need to start nurturing your own talent, as it will soon be so scarce you probably won’t be able to hire top talent anymore at what you can afford to pay as they will all be earning top salaries at “Agent” development tech shops or “Agent” enhanced services shops.

But sadly, this is the last thing he gets right and his third suggestion telling you to “go online and learn how AI and agents work” is totally off the mark if you want to become more than just a consumer of such technology. To truly understand how this technology works, so you can understand where and when it won’t work (and why), you need a solid understanding of not just the algorithms it is based on, but the underlying mathematics. You need a solid STEM education to truly learn why what you are doing works, or doesn’t. Furthermore, English will never be the language of real coding. COBOL was abandoned for a reason — it was too wordy for real coders, and the reality is that English is too imprecise to ever be a formal programming language!

Just like there was no Æther, there’s no data fabric either!

In a recent LinkedIn posting just before the holidays, THE REVELATOR asked a very important question. A question that may have gone overlooked given that many people are busy trying to get their work done before the holidays so they can get a few days off. And a question that must NOT be forgotten.

1. How does the old technology phrase “garbage-in, garbage-out” apply to Gartner’s Data Fabric post?

Data files. Databases. Data stores. Data warehouses. Data lakes. Data Lakehouses. And now … the data fabric … which is, when all is said and done, just another bullsh!t organizational data scheme designed to distract you from the fact that your data is dirty, that data storage providers don’t know what to do about it, but these data storage providers still need to sell you on something new to maintain their revenue streams.

You see, the great thing about today’s SaaS middleware enabled apps is that they don’t care where the data is, what organizational structure the data is stored in, etc. As long as the data has a descriptor that says “this field, which is in this format, in this db stores X” (where X describes the data) and an access key, the SaaS middleware can suck the data in, convert that data into the format it needs, and work with that data.

However, now that we are in the age of “AI”, the most important thing has become good, clean, data. However, just “weaving” your bad data together doesn’t solve anything. In fact, with today’s technology, it just makes things MANY times worse. We are now at garbage in, hazardous waste out!

Unfortunately there’s nothing we can do if the AI zealots are now adding hallucinogenics to their kool-aid, because it sounds like they are trying to bring back the magical medeival Æther … *groan*

THE REVELATOR then went on to ask …

2. Why does Gartner confuse more than inform and enlighten?

At the end of the day, you have a better chance of appearing as an enlightened Guru to someone who is lost and confused than to someone who is clear headed and confident in one’s direction!

Like the other big analyst firms, they profit off of being the Gurus the executives turn to when they can’t make sense of the hogwash filled marketing madness they are inundated with every day!

More specifically, their sales people need to say: “Our senior analyst has all of the answers … and they can be yours at the low, low introductory price of only 9,999,99 USD a day*.” So they don’t really care about whether or not they are confusing more than enlightening, as long as the sales are coming in. (In fact, they aren’t even looking to see how they are doing as long as the money keeps rolling in …

* one day only, after that, full rate of 29,999.99 a day applies …

But the questions didn’t stop there. The next question was:

3. Why are Data Problems Solved Downstream?

The answer to this is not as easy or straightforward, but when you consider that:

  1. it’s hardwork to solve the problems at the source and
  2. most of these analyst firms are staffed with analysts with little fundamental understanding of technology or the domains they are analyzing the technology for, don’t want to admit it, and are happy to take guidance from the vendors cutting them the biggest cheques and spending the most time “educating” them on the paradigm the vendor wants to see …

What should one expect.

Case in point. Did IDC just happen to come up with a “Worldwide SaaS and Cloud-Enabled Spend Orchestration Map” on its own at the same time a whole bunch of these solutions hit mainstream? (Especially when it takes person years of research and development to design a new map and analyze vendors, at least if you want to try and get it right.) Especially when they don’t have enough senior analyst talent to adequately cover core S2P?

Another case in point. Did Gartner merge it’s P2P into a S2P map because it honestly believes the entire market is heading there (FYI it’s not, look at the Mega Map), or because it doesn’t have enough analyst talent left to attempt to cover the market fragmented?

At the end of the day, it takes many years and many degrees to get a fundamental understanding of modern technology (which all runs on math, by the way) and many more years to get expertise in a business domain … so what can you honestly expect of kids straight out of school who make up significant portions of analyst teams???

Which led to the next question.

4. Can innovation co-exist with exclusivity?

Innovation happens, but then big stalwarts in the space scoop it up to try and remain competitive enough to keep their current customers locked in, a vacuum is created, and the cycle starts anew.

Until Trump dismantles them entirely, the US, like most of the pseudo-free first world, has enough anti-monopoly laws to ensure the cycle continues.

So yes, innovation can coexist with exclusivity, it just takes decades to realize what could happen in less than one decade as a result of having to start over so many times.

Finally, this led to the final question:

5. Does the VC investment model of: for every ten investments, seven fail, two are mediocre, and one “hits pay dirt” have anything to do with the 80%+ technology project failure rate?

It most certainly does! The fact that VCs are happy for seven investments to fail entirely (and then just move the good people to other investments if those people want to keep working) doesn’t help the project failure rate … especially since so many companies don’t survive long enough to master models that will lead to success, instead of failure, 80%+ of the time or to take the time to gauge, plan, and do implementations properly (because, if they don’t sell the next deal within a quarter, the investors will drop them faster than a hot potato).

One of these things is not like the other — it’s the right choice!

This originally published March 6 (2024).  It is being reposted due to the criticality of the subject matter (and the fact that One Trillion was wasted on services last year).

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.)

Three bids for that spend analytics project from the three leading Big X firms come in at 1 Million. One bid for that spend analytics project from a specialized niche consultancy you pulled out of the hat for bid diversity comes in at 250 Thousand. Which one is right?

Those of you who only partially paid attention to the education Sesame Street was trying to impart upon you when you were growing up will simply remember the “one of these things is not like the other” song and think that any of the bids from the Big X firm is right and the niche consultancy is wrong because it’s different, and therefore must be thrown out because it’s too low when, in fact, it’s just as likely that the three bids from the Big X firms that are wrong and the bid from the niche consultancy that was right.

Those of us who paid attention knew that Sesame Street was trying to show us how to detect underlying similarities so we could properly cluster objects for further analysis. What we should have learned is that the Big X bids were all the same, built on the same assumption, and can be compared equally. And that the outlier bid needed further investigation — a further investigation that can only be undertaken against an appropriately sized set of sample set of bids from other specialized niche consultancies to compare against. And without that sample set of bids, you can’t properly evaluate the lower bid, which, the doctor can tell you, is just as likely to be closer to correct than what could be wildly overpriced Big X bids.  (Newer firms often have newer tech and methods — and if these are the right methods and tech for your problem … )

As per our recent post, if you want to get analytics and AI right, most of these guys don’t have the breadth and depth of expertise they claim to have (as most don’t have the educational background to know just how broad, deep, and advanced AI and analytics can get, especially when you dig deep into the math and computer science and all of the variable models and strengths and weaknesses, and instead are trained on what is essentially marketing content from AI and analytics providers). In the group that sells you, there will be a leader who is a true expert (and worth his or her weight in platinum), a few handpicked lieutenants who are above average and run the projects, and a rafter of juniors straight out of private college with more training in how to dress, talk, and follow orders than training in actual analytics … and no guarantee they even have any real university level mathematics beyond basic analysis in operational research (and thus a knowledge of what analytics is and isn’t and can and can’t do).  And unless you know what you need, and why, you can’t judge the response.  (Furthermore, you can’t expect them to figure out your problem and goals with only partial information!)

While there was a time big analytics projects were (multi) million dollar projects, that was twenty years ago when Spend Analysis 1.0 was still hitting the market; when there were limited tools for data integration, mapping, cleansing, and enrichment; and when there weren’t a lot of statistics on average savings opportunities across internal and external spend categories. Now we have mature Spend Analysis 3.0 technologies (some taking steps towards spend analysis 4.0 technologies); advanced technologies for automatic data integration, mapping, cleansing, and even enrichment; deep databases on projects and results by vertical and industry size; extensive libraries for out-of-the-box analytics across categories and potential opportunities; and a whole toolkit for spend analysis that didn’t exist two decades ago. This new toolkit, built by best of breed vendors used, and sometimes [co-]owned by these best of breed niche consultancies (that don’t try to do everything, and definitely don’t pretend they can), allows modern spend analysis projects to be done ten times as efficiently and effectively, in the hands of a master — a master that isn’t necessarily on your project if you hire a Big X or Mid-Sized Consultancy without doing your homework, vetting the proposal, and vetting the people. [See when should you be using Big X.]

In contrast, a dedicated niche consultancy should have all these tools, and only have masters on the project who do these projects day in and day out. Compared to the bigger consultancies who don’t specialize in these projects, which will have a team of juniors using the manual playbook from the early 2000s, and one lieutenant to guide them. That’s often why sometimes their project bids are five times as much — and why you should be inviting multiple niche best-of-breed consultancies to bid on your project as well as multiple Big X consultancies (including those that are truly focusing on analytics and AI, and you can identify some of these by their recent acquisitions in the area) and be focusing in just as much on the six figure bids for the one that provides the best value, not just the seven figure Big X bids.  (And, FYI, if you invite enough Big X, you might find some come in at six figures and not seven because they have acquired the newer tech, took the time to understand your request, and figured out how they could get you the same value for less cost, leaving you funds for the follow on project where you should consider the Big X!)

(This is also the case for implementations. The Big X always have a rafter on the bench to assign to any project you give them, but there’s no guarantee any of them have ever implemented the system you chose before, or if they did, no guarantee they’ve ever connected it to the systems you need to connect to. You need specialists if you want a new system implemented as cost effectively as possible, especially if its a narrow focused specialist application and not a big enterprise application the Big X always implements. At the end of the day, even if you’re paying those specialists 500 or more an hour because getting a system up in 2 months at 40K is considerably better than a small team of juniors taking 4 months at 200 an hour and a total cost of 80K.  But again, mileage will vary — if the solution you select is a Big X partner, then the Big X will be best.  If it’s a solution they never heard of, you will need to evaluate multiple bids from multiple parties. )

Remember, where any group of vendors on the same page are concerned, All of us is as dumb as One of us!

Don’t fall for the Collectivism MindF6ck! that if multiple parties agree on something, that’s the right answer!  the doctor does NOT want to do say it again, but since a month still is not going by where he’s hearing about niche consultancies being thrown out for “being too cheap” or “obviously not understanding the problem” (which means the enterprise throwing them out is too uninformed and not recognizing that the Big X bids could just as likely the outliers because they aren’t inviting enough expert consultancies to the table), apparently he has to keep writing (and screaming) this truth. (the doctor isn’t saying that you can’t get a million dollars of value from some of these consultancies, just that you won’t by giving them a project they are not suited for;  again, see when should you use big X to identify when that million dollar project will generate a five million ROI — it’s people doing these projects at the end of the day, and where are those people?)

Remember, most of these firms got big in management, or accounting and tax, or marketing and sales consulting, not technology consulting. The only reason these big consultancies started offering these services is because of the amount of money flowing into technology, money which they want, but while the best of the best of the best in more traditional accounting, management, and marketing fields flocked to them, the best of the best in technology flocked to startups and c00l big tech firms  Now, some of these firms double downed, went and recruited those people, built small teams, learned, bought tech companies to expand the team, and now have great offerings in a number of areas.  But we have tens of thousands of tech companies for a reason, not everyone can build every type of technology, and not everyone can be an expert in every type of technology.  So while they will have expertise in some areas, they just can’t have expertise in all areas.  No one can.  Find the best provider for you.  Sometimes it will be Big X.  Sometimes Mid-Market.  Sometimes Niche.  It all depends on your problem at hand.)

And yes, sometimes the niche vendor will be wrong and woefully undersize the project or your needs.  But as per the above, if you don’t do give them a chance, and deep dive into their bid, how will you know?

 

Did you ever try eating a mitten? the doctor bets some of those clients did! (He feels you’re not all there if you think glorified reporting projects should still cost One Million Dollars by default and might actually try to eat your mittens! [Joking, but you get the point.]  Deep analytics projects that require the most advanced tech, especially AI tech, will cost a lot, but standard spend analysis, sales analysis, etc. where we have been iterating and improving on the technology for two decades should not.)