Category Archives: Technology

Need a Good Solution? Make sure you ask the right questions off the bat … not just the hard ones!

It’s only been a few months since our last post on the topic of vendor selection where we flat out said that if you want a good solution from a good vendor, you need to start off by asking the hard questions off the bat , and gave you the 1-2-3 punch you should start with, but it seems that some vendors have come up with new tricks and you now need a 1-2 pre-qualification phase before the 1-2-3 knockout combo before you can decide if a vendor’s solution is worthy of your consideration.

Now that we have the richer enterprise vendors deploying fully AI-agents to make their standard pitches, create their demos, and, in some cases, even handle their fund-raising and sales cycles, you don’t know if you’re even talking to a human! And you need to talk to a human. An “AI” will only tell you what it is programmed to say and only feed you what it thinks you want to hear, and we all know how that is a recipe for disaster.

Thus, the first question you need to ask in the pre-qualification one-two punch is:

1. “Please tell me whether or not you are an AI construct, knowing that this conversation may be recorded and that if a falsehood is spoken, it may be used against your employer in a court of law, especially if the intent of such falsehood was to deceive us. Also, we retain the right to ask you to prove your response at any time.”

If you get a “yes” response, you must immediately disconnect and eliminate the vendor from your consideration. If they won’t even let you talk to a lowly pre-sales person in a third world country, what chance will you ever have of speaking to a real support person if something goes wrong?

If you get past this question, then the next question is:

2. “Is your offering built around, or just, someone else’s LLM/Gen-AI/AGI (including, but NOT limited to, ChatGPT, Claude, Azure, etc.) offering in a new wrapper? Again, this conversation may be recorded and we retain the right to use everything you say against your employer in a court of law, especially if the intent of the falsehood was to deceive us. Also, we reserve the right to an independent audit of your solution at any time upon purchase thereof.”

If you get a “yes” response, you again must immediately disconnect and eliminate the vendor from your consideration.

i. As SI has repeatedly informed, you there are only a few valid uses for Gen-AI on its own, and even fewer valid uses for Gen-AI in Procurement.

ii. Why should you pay a steep markup to a third party for a shiny wrapper when you can just license the source at a fraction of the cost?

Now, if you have confirmed you are talking to a real vendor rep offering you a real solution built by the vendor PRIMARILY on their own stack (and not just a third party’s in a shiny wrapper) that does something useful for at least some Procurement departments, then you hit-them with the one-two-three punch we gave you last fall:

3. Can, and will, you show me (not tell me) live … preferably on use cases or data I give you on the spot?

Again, the most critical point is you don’t want a canned demo, you want a live display showing you that their solution will do what you need it to do. (Not necessarily the way you envisioned, your process might not be the best or the most technologically friendly, but in a way that will solve your problem.)

4. Once you show me the core use cases, can, and will, you explain the breadth of use cases you developed your solution for and how they are specific to my business?

You want a vendor who can do more than answer a specific question when asked, and tell you the standard script on what their product does. You want a vendor that knows the real world problems that businesses have and who tirelessly works to build a solution to solve those real-world problems.

5. Once we tell you the extent of your solution we feel is appropriate, can you talk us through what the implementation and integration to our environment would require without bringing in a paid third party “expert” consultant? And how long will that take?

It may be a great solution during the demo, but the reality is that it is only a great solution for you if your team adopts it, which will only happen if it works on the technology platform and in the technology ecosystem they are forced to work in. It needs to seamlessly get the required data in from other applications, make it easy for the users to do their tasks, and then push out the needed alterations and decisions to other systems in the ecosystem. An app that stands alone will never get used and will fail even before the implementation starts.

If you get through these 5 questions, you have a real vendor with a real solution which will solve your problems to some degree, and one who should definitely be on the RFP shortlist, if not fast-tracked to negotiation if they solve a critical problem in a way that just works for you.

Myth-busting 2025 2015 Procurement Predictions and Trends! Part 8

Introduction

In our first instalment, we noted that the ambitious started pumping out 2025 prediction and trend articles in late November / early December, wanting to be ahead of the pack, even though there is rarely much value in these articles. First of all, and we say this with 25 years of experience in this space, the more they proclaim things will change … Secondly, the predictions all revolve around the same topics we’ve been talking about for almost two decades. In fact, if you dug up a Procurement predictions article for 2015, there’s a good chance 9 of the top 10 topic areas would be the same. (And see the links in our first article for two “future” series with about 3 dozen trends that are more or less as relevant now as they were then.)

In our last instalment, we continued our review of the 10 core predictions (and variants) that came out of our initial review of 71 “predictions” and “trends” across the first eight articles we found, in an effort to demonstrate that most of these aren’t ground-shattering, new, or, if they actually are, not going to happen because the more they proclaim things will change …

In this instalment, we’re again continuing to work our way up the list from the bottom to the top and continuing with “Data”.

Data

There were 4 predictions across the eight articles which basically revolved around “data-driven decision making” with some sideline focus on the need for “data governance”. As with almost every “prediction” and “trend” in this series, this is yet another prediction that makes headlines every year, no more important this year than the last as no Procurement tech works without good data (although some work even worse with bad data), and unlikely to get more attention now that a certain analyst firm has latched onto a new buzzword to hide the importance of good data. Before we discuss further, as is our custom, we will list the four predictions.

  • Data-Driven Decision Making
  • Data-Driven Decision Making
  • Data-Driven Decision Making Will Become More Critical
  • Data Governance and Data-Driven Decision Making

All strategic decisions should be, and more importantly, should have been, data driven for the last four decades in any organization (given that the first IBM PC hit the market in 1981, making computer-based data analysis affordable for any mid-sized or larger organization. And while it wasn’t possible to give every office worker a computer and internet access until about 25 years ago, limiting “data analysis” decision support to only the most important strategic decisions, once everyone had a computer and internet access, every strategic decision should have been supported by data to some extent).

And with the emergence of web-based data services, it’s never been easier to get data. Moreover, most organizations are swimming in data. In fact, some organizations have so much data that the problem is not the lack of data, but the lack of good, appropriate, data. In most organizations, there are drives bursting with data, where the quality ranges from reasonably good to completely wrong, and if you use that wrong data, you’ll have a wrong analysis and make wrong inferences. Also, not all data is appropriate for all types of analysis, so there’s no guarantee the feeds you have are the right ones. Moreover, most users in most organizations don’t know how to judge the quality of the data, or how to do a proper cleansing and correction if the data quality is poor.

Good decisions only come from a proper analysis on good data, so while there will continue to be pushes for data-driven decision making, because that’s the age we are in, there needs to be a continued push for good data! But that will only occur if an organization has good data governance, which is what the majority of these predictions and trends are missing.

The organization needs to ensure that, before any data is stored, there are processes in place to make sure that any data stored in an organization’s system is correct, complete, in a standardized format, and linked to any associated records using unique ids. That no record is stored unless these requirements are met. And that all records are verified on at least an annual basis to ensure they are still complete and correct. In particular, any time a record is updated, the data should be (automatically) verified again, and any time a record is touched for use, critical data should be verified. A lot of this can be automated if the organization has identified masters for all types of data and trusted external feeds for new data verifications and annual rechecks. And if it’s not, the organization can’t really do data-backed decision making because that relies on good data.

What Should Happen? (But Won’t!)

E-MDMA. The adoption of an Enterprise Master Data Management Administration strategy. Since data is so fundamental to good decisions across the organization, enterprises should not only be proactively managing their data but managing it in a manner that ensures it is actively maintained, highly accurate, and available to use by any system that needs it. This requires identifying, for each piece of data, a (master) system of record, verification rules, (third party) data sources for corroboration and verification, and access rules. All boring stuff … that has to be done enterprise wide … but absolutely necessary for data-based decision making. Especially if you want to use AI.

Now, we know it sounds very boring, but it’s critical. But we also know that no one will want to do it. So don’t call it Enterprise Master Data Management Administration … just call it E-MDMA and tell your employees its going to bring ecstasy to their job. Let them think its a new drug, and maybe they’ll buy in.

Seven down, three to go.

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!

Myth-busting 2025 2015 Procurement Predictions and Trends! Part 2

Introduction

In our last instalment, we noted that the ambitious started pumping out 2025 prediction and trend articles in late November / early December, wanting to be ahead of the pack, even though there is rarely much value in these articles. First of all, and we say this with 25 years of experience in this space, the more they proclaim things will change … Secondly, the predictions all revolve around the same topics we’ve been talking about for almost two decades. In fact, if you dug up a Procurement predictions article for 2015, there’s a good chance 9 of the top 10 topic areas would be the same.

To save you the trouble of reviewing all of these articles, which are going to be 80% the same (as the 71 “predictions” and “trends” from the first 8 articles we reviewed all fell nicely into 10 “core” areas with not a lot of difference between them), we are going to discuss the 10 core predictions (and variants) that you are going to see over and over again, with some of them being the same predictions we have seen over and over again, and, where necessary, contrast the dream with the reality. Just for the heck of it, we’ll do take them in reverse alphabetical order, because, why not?

Technology & Digital Transformation

These predictions basically revolved around the rise of SaaS-based “Digital Transformation” with calls out to agility and integration. More specifically, these were the 10 predictions and trends from the first 8 articles we reviewed.

  • Cloud-Based Solutions
  • Digital Tools
  • Digital Transformation and Technology Adoption
  • Diverse Digital Transformation Technologies will Become the Norm
  • Emphasis on Digital Procurement
  • Integrated Procurement Systems
  • Proliferation of Procurement Technology Platforms
  • Rise of Agile Procurement Processes
  • Rise of Digital Procurement
  • Rise of E-Procurement

As you can see, they all revolve around SaaS/Cloud-based “Digital Transformation”, which should not be a surprise. The SaaS transition started over 15 years ago, and revved up in a big way over the last few years when COVID forced business to put more of their processes online for remote/home access. And even though a lot of big companies are forcing employees to return to work, now that they’ve realized SaaS is easier to maintain (someone else manages the data centre, does the updates, etc.), expense (you’re not paying huge one-time fees up front), replace if needed (provided you make sure, up front, in the contract, that you have the right to export all of your data at any time in an industry standard format), and mitigate you exposure in the case of a cybersecurity breach (as the provider is required to maintain agreed upon security and privacy standards and you can insist they indemnify you for their failures to honour the contract) they are going to continue to replace legacy systems with SaaS as time goes on. It’s just a continuation of what they have been doing for the past four years. Nothing new, exciting, or unexpected here.

And yes, the smarter companies, in addition to ensuring they own their data and have the right to extract all of it at any time, will look for solutions that are easy to integrate into their current/planned SaaS ecosystem as well as those that increase their agility. It’s just good practice to try and future proof your SaaS as much as possible, especially considering poorly managed SaaS subscriptions contributed significantly to 500 Billion in wasted Technology spend last year.

So, yeah, it’s a trend. But did it take any prognostication to notice it? Nope!

What Should Happen? (But Won’t!)

Companies should analyze all of their systems and plan to replace any systems that

  • are not being used / not delivering value
  • don’t integrate with their ecosystem
  • don’t support modern Sourcing & Procurement processes

And we use the word “plan” because:

  • it takes time to identify the right solution, which has to be done by YOU and not a salesperson, and then to implement that solution, which needs to be overseen by YOU and not a third party with no stake and who only cares about how many hours they can bill, and then needs to be adopted by YOU and used regularly
  • the company may be locked into the current system for another one (1) to four (4) years depending on the deal signed by your predecessor, and escaping early may be way too costly, so you might be stuck with it for a few years (and have to focus on finding solutions to make it more usable/valuable)
  • you can only replace one system at a time in any department without causing so much disruption that you risk the business (big bang projects always result in big bangs; just Google “worst supply chain disasters” and you’ll see the vast majority are caused by technology failures, and about half by ERPs alone!)

Once you have the list of systems that you need to acquire / update, prioritize them based on the value a new system can deliver and how soon you could implement / replace a current system, and then begin the to identify, select, implement, integrate, train, and use the first system (on the list) on a daily basis. You should only begin the process of identifying a second system once the first system is being used on a daily basis (and not begin implementation of a second system until you are sure all the bugs are worked out of the first system).

And always remember that your success or failure depends on you. As we have previously noted, digital procurement transformation requires strategy and design … your strategy and your design. After all, you don’t want to be the company that pushes the technology project failure rate over 90%. (It’s 88% now and climbing year-over-year.)

Anyway, that’s one trend myth down, nine to go.

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.)