Category Archives: Technology

When Should You Use Big X?

I’m often hard on Big X (in general), specifically when it relates to analytics or AI, because I regularly get insight into high project costs with low chance of return, see too many failure stats (where they get the lion’s share of the projects), and know that they struggle to attract the best people in those cutting edge technologies (as there is too much demand across the market, and too few STEM graduates, who want to go to the Big Tech Powerhouses like Alphabet or Meta or Microsoft, or, in AI, Open-AI or to a wild-west startup).

However, I’ve always noted that

  • my opinions are restricted solely to these areas (and not in general, and not even tech in general),
  • I believe they are often the best choices for many other projects, and
  • sometimes they are the best choice. You just have to do your homework. (Some of these Big X have recently acquired smaller providers that built a team of experts in analytics and/or AI and now have some of the best experts in the world!)

But since I just wrote a very critical opinion piece on the (marketing) direction of specialist suite software vendors (when I’m normally just praising best of breed vendors), I’m going to turn the tables and write a very positive piece on when you should use Big X. Even if it may seem sometimes that I’m against them, especially if I’m on a rant (and you have to remember, SI is a blog for information and entertainment purposes, not a paid analyst site!), I’m not. For the most part, especially if engaged properly, you can get a lot of value from a Big X, and, furthermore, sometimes get value you can’t get any other way!

(I’m just critical of non-value, and jumping all in on a new technology / digitization project with a Big X without blindly investigating both the project and the Big X’s proposal is usually not the right way to get value. And yes, in this situation the blame for any failure should fall on the company that didn’t do its homework and not the Big X.)

So here are three ways to get value out of a Big X, guaranteed and risk free if you approach it right:

  • Strategy: especially
    • Corporate: corporate strategists don’t work for companies, and the best don’t work for small niche consultancies, they work for Big X companies where they do these types of projects for big clients all the time; and only the Big X have enough of an archive across their talent to build up patterns that work and project real industry trends
    • Marketing/Sales: there are a lot of niche marketing shops for small scale consumer marketing, and a few for global marketing, but a lot of these shops are for executing a plan, not all can create one, especially if it needs to be co-created and tie into a corporate strategy
    • Operations: operational excellence is most often found in two places: business schools and Big X. ‘Nuff said.
    • Global Accounting/Tax Efficiency: what small, or even mid-sized, firm is going to have global knowledge of current and upcoming accounting and tax regulations and rates and can advise you on how to expand in a tax efficient manner?
    • Supply Chain Design: it takes a lot of people to have a lot of knowledge of all of the countries a multi-national will need to source from in its extended supply chain and sell to; this knowledge will rarely be found elsewhere (some of it from data subscriptions, but you still need access to human expertise on every country you are considering)
  • Process Redesign: the next step after operational strategy is process redesign, and these firms have deep insight into best practices across the board, detailed playbooks for conducting these projects, and deep insights into what is working and not;
  • Gap Analysis: if you don’t need a total operational process redesign, but just improvements, or technology to fill the gaps, they can just do a gap analysis and find weaknesses, make recommendations, and help you outline technology needs, and even help you create a good RFP to send out to potential vendors (however, unless you do your homework and provide them with good, deep documentation up front on your processes and systems, they will have to do a lot of manual labour to build that picture and that will result in an expensive RFP; but again, most of the cost will be on you and not them)

And, of course, used right,

  • Technology: not all technology is bleeding edge, and there are only a few categories at any given time where there just isn’t enough talent to go around, which is why our only concern is advanced analytics and AI projects (especially where you don’t know what you need*)
    • Enterprise Product Selection: all Big X, which serve big companies, have deep expertise in tried-and-true enterprise applications; moreover, they tend to have partnerships that give them deeper insights than other providers, which can be very beneficial in your selection process;
    • Select Emerging Product Selection: many Big X are investing in technology players that are up and coming that they see as next generation enterprise solutions; they have deep insight into the products they are investing in (but, as you can expect, limited insight beyond publicly available information into solutions their competitors are investing in)
    • Enterprise Product Implementation: they not only have deep insight into enterprise products, especially in the back-office (ERP, Finance, AP, etc.) and supply chain (planning and logistics), but deep implementation experience in the platforms as well; plus they have detailed step-by-step guides that even the most junior hire can follow and succeed
    • Partner Product Implementation: they know their partners well, especially since they usually won’t take on a partner unless it has a training program to train the implementors or a support team for the implementors
    • Appropriate, Well Defined, AI & Analytics Projects: we rant here all the time because most companies ultimately get this wrong (most Big X, most Suites, and definitely most clients); the reality is that the technology has progressed so far so fast that there are very few that understand just what is out there, where it is and is not good, and how it applies to different market/company situations; the reality is that with all of the recent dynamic shifts in markets, supply chains, and demand, and sometimes a complete lack of consistent, historical, data to base on analysis on, standard methodologies don’t always work anymore; you need experts, there are not enough to go around relative to explosive market demand, and you need the right expert for your problem; and even if the right expert exists at the provider you select, you will only get the right expert if YOU define the problem appropriately;
      this being said, with many Big X companies now acquiring specialist AI and analytics firms, as well as world class experts, there are a number of projects they are very well suited for — but you have to define the right project and make sure it’s right for the firms you invite

… and for most of these areas, you’ll struggle to find more than one or two niche companies that can deliver the same value, if you can find any at all! (And they only have the manpower for so many big projects compared to a Big X.)

In other words, we aren’t against Big X, and in fact recommend them regularly (just like suites we also pick on in our opinion pieces) we are against automatically using them for advanced technology projects that aren’t well defined, where they may not have the right expertise available for your problem at hand.

And we’re tired of the high failure rates, so if you don’t know what you need, stick to what they know well and always deliver on to your satisfaction until you know what you want!

* The reason for high tech failure rates usually boils down to two fundamentals. 1) Lack of Preparation and 2), as THE REVELATOR would say, an equation-based technology led platform approach vs. an agent-based human led solution approach. Preparation is key. If you don’t know what you need, and specify it clearly, you can’t expect the provider to know what you need, make the right interpretations, put the right proposal together, and assign the right people when you accept it. (And this goes for all providers, not just Big X.) This means that the Big X proposal writer is forced to make assumptions and then make a plan and assign resources based on those assumptions. And if they are wrong, because you did not provide the right clarification either in your request or your acceptance of the proposal, the project will not deliver the results you expect with the indicated time and effort, leading either to cost overruns, time overruns, or, if you’re not on the ball, complete failure. And, if the Big X did their best to understand your needs, it’s your fault, not theirs, but it’s still another failure.)

Top 10 Ways to be Labelled as a (Procure)Tech Noise / TroubleMaker!

For those of you who want to be a noise maker, trouble maker, Debbie Downer, complainer, etc. etc. etc., the doctor can confidently tell you that these are ten proven ways to accomplish that goal! Enjoy!

10. Point out that Tech Failure Rates have reached an all-time high of 88%! (Bain)

(As it is, in Procurement, We Don’t Get No Respect. We’ll get even less if 9 of every 10 projects fail! They’d fail less if … )

09. State that that RFPs for Tech should be Affordable!
(They are a critical first step in proper vendor selection once your need has been identified, and skipping this step has always proven disastrous. And then, after you select the vendor, the next step is to kick of Project Assurance, so the implementation doesn’t go off the rails.)

08. Go further and suggest that Big X SHOULD NOT be used for analytics and AI!
(The reality is, as we’ve stated again and again, limited tech talent is generally NOT interested in consulting — they want to work with the big powerful mega-corps [Meta, Alphabet, etc.] or join the wild west start-up frontier. Those not good enough get scooped up by the consultancies to try and fill the bench they need to staff the projects they sell. Doesn’t matter how good the outdated playbook is if you’re starting with the B-Team if you’re big, and rich, enough to afford it … or the C-Team if you’re not. Also, as we’ve said before, this doesn’t mean you shouldn’t use Big X for strategy, internationalization advice, etc. or the roots where they started where they have, and attract, the best people — just that, like every business decision, you have to be smart about where, and how, you engage to get your ROI. In fact, there are a whole slew of areas we generally recommend Big X for, and sometimes ONLY recommend Big X for, and these are covered in When Should You Use Big X?)

07. Dare to suggest it may be the end of an era for an early ProcureTech suite!

(Is The Third Act the Final Act?) Let’s ignore the fact that there has been more consolidation and failure in this space over the last two decades than anyone realizes, and that the seven suites appear to be sailing the seven seas without a sextant [foreshadowing?]. See SI’s classic Vendor Day Reprise and count how many of those companies are still around as-is. These were representative of the cream-of-the-crop when they were covered. The rate of disappearance is actually higher across the board!)

06. Note that Gen-AI is way overhyped.

(Unless you want suicidal people committing suicide in suicidal self-driving cars, for example. See valid uses for Gen-AI. And note that one of the big analyst firms pushing it in its hype cycle also noted that that it’s failure rate is 85%! [Source])

05. Remind people that intake & orchestrate is not new!

(With intake in ProcureTech tracing its beginnings back 24 years and orchestrate tracing it’s way back over 50 years as it’s just the fancy new name for middleware, which was a term coined in the 60s and implemented in the late 60s/early 70s with RPC being one of the earliest examples. See Point 11 for more hard truths.)

04. Rail against 2*2 vendor maps, and logo maps, as vendor selection tools!

(They are NOT Appropriate for Tech Selection. At most, they can be used to identify vendors to shortlist — but you still need to create a proper RFP! Remembering that:)

03. FREE RFPS are NOT free!

(How many times do we have to tell you There Are NO Free RFPs? Too many, since vendors will NOT get the message!)

02. State that there is no demonstrable ROI for attendees and vendors at big (Procure)Tech events.

(We need better events. A great experience is not business ROI!)

01. Mathematically argue that no business is worth more than a 10X multiple at investment time.

(‘Nuff said. Deeper dive in linked article.)

Now, I don’t know about you, but if wanting

  • (10) tech project success,
  • (09) affordable RFPs for all Procurement departments that need them,
  • (08) value for your consulting dollar,
  • (07) a true picture of the ProcureTech space and where the best cost/value ratio is for all buying organizations (not just G3000s),
  • (06) real AI powered by real HI that delivers real value,
  • (05) solutions that do what they should with (true) open APIs,
  • (04) real solution guides,
  • (03) valuable RFP advice,
  • (02) valuable events for all (not just organizers and consultants), and
  • (01) fair investments across the board for underfunded ProcureTech companies

means being a troublemaker, then make me the leader of the troublemakers! I’ve had enough of platform failures, enough of marketing soundbites, enough of one-way sales, enough of vendor marketing packaged as analysis and advice, and enough BS. Without procurement, there is no business. And, like Rodney Dangerfield, who unfortunately never got it in his lifetime, we deserve a little respect.

Procurement deserves better!

P.S. If you lead a provider organization that wants to do better, please feel free to reach out!

Advanced Supplier Discovery Tomorrow — No Gen-AI Needed!

Back in late 2018 and early 2019, before the GENizah Artificial Idiocy craze began, the doctor did a sequence of AI Series (totalling 22 articles) on Spend Matters on AI in X Today, Tomorrow, and The Day After Tomorrow for Procurement, Sourcing, Sourcing Optimization, Supplier Discovery, and Supplier Management. All of which was implemented, about to be implemented, capable of being implemented, and most definitely not doable with, Gen-AI.

To make it abundantly clear that you don’t need Gen-AI for any advanced back-office (fin)tech, and that, in fact, you should never even consider it for advanced tech in these categories (because it cannot reason, cannot guarantee consistency, and confidence on the quality of its outputs can’t even measured), we’re going to talk about all the advanced features enabled by Assisted and Augmented Intelligence that are (or soon will be) in development (now) and you will see in leading best of breed platforms over the next few years.

Unlike prior series, we’re identifying the sound, ML/AI technologies that are, or can, be used to implement the advanced capabilities that are currently emerging, and will soon be found, in Source to Pay technologies that are truly AI-enhanced. (Which, FYI, may not match one-to-one with what the doctor chronicled five years ago because, like time, tech marches on.)

Today we continue with AI-Enhanced Supplier Discovery that is in development “today” (and expected to be in development by now when the first series was penned five years ago) and will soon be a staple in best of breed platforms (and may be found emerging in development beta versions of some platforms). (This article sort of corresponds with AI in Supplier Discovery The Day After Tomorrow that was published in March, 2019 on Spend Matters.)

TOMORROW

Intelligent Supplier Discovery

How is this different than deep capability match that is available today? Because it sure sounds like the ability to match to a very detailed request is “intelligent”. Compared to most search capabilities in most platforms, it is. But there’s more to selecting a supplier, especially one with whom you need a long term relationship, than just tech specs and certification checks. There are also performance considerations, innovation ability (hard to measure), culture, and other, softer factors.

First of all, you need a platform that can predict the ability of a given supplier to innovate and, more importantly, innovate for you based upon your specific needs. To do this, you need to chart the “innovation history” of a supplier (how many innovations per year, typical gap between innovations), compare the “innovation history” to other suppliers in the industry and category, use a predictive curve fitting or other ML algorithm to predict it’s rate (vs. the average). This is a lot of semantic processing to identify innovations and approximate dates, a lot of trend analysis to find the right predictive algorithms, and a lot of calculation. And then you need the ability to refine the innovation rate by category for a multi-category supplier so the trend line matches your need, and not your competitor’s.

Secondly, you need to be able to parse the “reviews” not just for sentiment, but positive or negative interpretations of specific, relevant “soft factors” like communication, working culture, etc. and compute appropriate ratios or bands that can be compared and be considered in super search / match criteria that is relevant to your organization. Next generation targeted sentiment analysis on factors identified on deep semantic analysis. No Gen-AI needed, just domain specific refinements of traditional approaches (trained on highly vetted, validated data sets).

Predictive Smart Search

For a company in direct manufacturing, electronics, pharma, or another industry where advanced innovation at a fairly rapid pace is required not just for growth, but continued market share retention, identifying the right suppliers is critical. This requires a very deep search, and for specific projects, potentially dozens of requirements and validations that need to be done before a supplier can be invited to an event.

So many in fact that, even if a buyer could identify all of these up front, building the search criteria to capture them all could be difficult. Next generation platforms will learn from each search entered into a platform for a product, category, supplier, etc. and extract the typical criteria, the frequency, and the preferences by organization and user.

Based on this data, when a buyer, new product development specialist, etc. starts a search for a new supplier in a category and/or for a product, the platform will predict which factors are relevant to the user, recommend those factors and factors, and intelligently build the right search and tolerances for the user. And then retrieve the best suppliers, ranked with match percentages.

None of this requires Gen-AI. Frequency is just frequency mapping by product, category, and supplier. Matches are matches as per deep search. Auto query creation is rules based automation. Soft factors are identified by semantic and sentiment analysis. And so on. It just requires a lot of Human Intelligence (HI!) to put it all together.

Is That All, Folks?

Probably not. The more data that is collected, the more analysis that can be done, and the more matching and prediction that can be done across people, products, services, and solutions. And the more “intelligence” (which CAN NOT be generated by Gen-AI) that can be put forward beyond your search before you invite a supplier to an event. But it’s the next step, and we’re going to stop here because we are going to refresh our series on Supplier Management as well.

SUMMARY

Now, we realize some of these descriptions are dense, but that’s because our primary goal is to demonstrate that one can use the more advanced ML and AI technologies that already exist, harmonized with corporate, market and community data, to create even smarter Supplier Discovery applications than most people (and last generation suites) realize, without any need (or use) for Gen-AI, that the organization can rely upon to reduce time, tactical data processing, and risk while increasing supplier intelligence and overall organizational performance. It just requires smart vendors who hire very smart people who use their human intelligence (HI!) to full potential to create brilliant Supplier Discovery applications that buyers can rely on with confidence no matter what category or organization size, always knowing that the application will know when a human has to be involved, and why!

Two and a Half Decades of Project Failure

  • 2024 Bain: 88% of business transformations fail to achieve their original ambitions (Source)
  • 2023 HBR: Some estimates place the failure rate as high as 80%.
  • 2023 Gartner: states that 85% of AI projects fail. As well, 87% of R&D projects never get to the production phase.
  • 2023 EY: 2/3 of senior leaders have experienced at least one underperforming [digital] transformations in the last 5 years (Source)
  • 2020 Standish Group: 66% of technology projects end in partial or total failure (based on the analysis of 50,000 projects globally). 31% of US IT projects were canceled outright and the performance of 53% ‘was so worrying that they were challenged.’ (Source)
  • 2020 McKinsey: 17% of large IT projects go so badly that they threaten the very existence of the company (Source)
  • 2020 BCG: 70% of digital transformation efforts fall short of meeting targets (Source)
  • 2020 KPMG: 70% of organizations have suffered at least one project failure in the prior 12 months (Source)
  • 2019 Everest Research Group: 78% of enterprises fail in their digital transformation initiatives (Source)
  • 2018 PWC: 75% of digital transformations fail to generate returns that exceed the original investment (Source)
  • 2018 Standish Group: only 29% of IT project implementations are successful, and 19 percent are considered utter failures (Source)
  • 2017 Gartner: 75% of all ERP projects fail (Source)
  • 2016 Innotas: 55 percent had a project fail in the last 12 months (Source)
  • 2015 Genpact: more than 66% of digital transformations fail to meet expectations (Source)
  • 2013 Innotas: 50 percent had a project fail in the last 12 months (Source)
  • 2012 McKinsey: large IT projects run 45 percent over budget and 7 percent over time, while delivering 56 percent less value than predicted (Source)
  • 2011 HBR: average project cost overrun is 27%, 1/6 projects is a black swan with a cost overrun of 200% or more Source
  • 2011 Forrester: 70% failure rate of change management initiatives (Source)
  • 2010 Deloitte: only 37% of projects delivered the functionality on time and budget meaning that 63% of projects failed to some degree (if not entirely) (Source)
  • 2009 Standish Group: failure in 68% of projects is probable (because success in 68% of projects is “improbable”) Source
  • 2001 Standish Group: 52.7% of projects will cost 189% of their original estimates and 31.1% of projects will be canceled before they ever get completed (Source)
  • 2001 Robbins-Gioia Survey: 51% viewed their ERP implementations as unsuccessful while 46% did not feel the organization understood how to use the system (Source)
  • 2001 Conference Board Survey: 40% of the projects failed to achieve their business results within one year of going live those that did achieve benefits had to wait (at least) six months longer than expected (Source)
  • 1999 Gartner: 75% of e-business projects will fail to meet the business objectives through 2002 (Source)

Is it just me, or is it the case that:

  • many of the firms who have been chronicling project failures for over two decades are also
  • many of the firms that have been guiding IT projects for over two decades?

Advanced Supplier Discovery Today — No Gen-AI Needed!

Back in late 2018 and early 2019, before the GENizah Artificial Idiocy craze began, the doctor did a sequence of AI Series (totalling 22 articles) on Spend Matters on AI in X Today, Tomorrow, and The Day After Tomorrow for Procurement, Sourcing, Sourcing Optimization, Supplier Discovery, and Supplier Management. All of which was implemented, about to be implemented, capable of being implemented, and most definitely not doable with, Gen-AI.

To make it abundantly clear that you don’t need Gen-AI for any advanced enterprise back-office (fin)tech, and that, in fact, you should never even consider it for advanced tech in these categories (because it cannot reason, cannot guarantee consistency, and confidence on the quality of its outputs can’t even be measured), we’re going to talk about all the advanced features enabled by Assisted and Augmented Intelligence that were (about to be) in development five years ago and are now available in leading best of-breed systems. And we’re continuing with Supplier Discovery.

Unlike prior series, we’re identifying the sound, ML/AI technologies that are, or can, be used to implement the advanced capabilities that are currently found, or will soon be found, in Source to Pay technologies that are truly AI-enhanced. (Which, FYI, may not match one-to-one with what the doctor chronicled five years ago because, like time, tech marches on.)

Today we continue with AI-Enhanced Supplier Discovery that was in development “yesterday” when we wrote our first series five years ago but is now available in mature best of breed platforms for your Supplier Discovery success. (This article sort of corresponds with AI in Supplier Discovery Tomorrow that was published in March, 2019 on Spend Matters.)

TODAY

Deep Capability Match

As noted in our original posting, if you want a custom produced FPGA, an industrial strength power converter that can handle feeds from your wind farms and water wheels, or a new state-of-the-art surround sound system, you don’t want just any supplier. This is especially true if all they do is produce a fixed set of products, use production technology that is not appropriate for the design you want, have a record of sourcing inferior raw materials, or don’t have the right quality processes in place.

So, when we last tackled this subject five years ago, the new/leading supplier discovery platforms were working on deep capability match that could take a set of requirements for a product, or even a bill of materials, and find matching suppliers for the parts.

Especially since all this needed was deep capability identification and tagging across categories, products, and services that included production process, certifications, materials, etc. Which means that deep capability match was essentially just a super smart search capability across not just a few, but dozens of requirements — as long as the data was properly structured and indexed.

This requires the ability to crawl websites and extract all text and documents, OCR those documents to text, and then semantically process for the relevant information along the recorded dimensions. This just required classical semantic processing which uses ontologies, semantic networks, and custom trained (neural) networks for POS/concept identification when classical processing is not sure. Tech that has now been around and ready for production use for over 15 years. The big challenge was the magnitude of data that needed to be processed and indexed, which is not a problem anymore given the processing power of racks, the size of modern data centres (which require 10X to 100X the processing power for the Gen-AI trainwrecks that don’t deliver), and modern distributed processing algorithms and technology.

And, of course the ability to do rapid semantically aware reg-ex (across similar key words / phrases) for anything not indexed, or indexable in a standard taxonomy.

Resource Capability Match

Sometimes you need very specialized services. As we noted five years ago, for new product design, you need an engineering resource who has designed similar products and is familiar with the new production technologies and components that are on the market. For software implementation, you need a team who has installed the current software in a similar environment that has the same ERPs, OSs, data sources, etc. For utility installation, you need engineers with the right skills and certifications. And so on.

This is essentially just a variant of deep capability match, except you are matching on the services capabilities and the individual’s resumes. Getting here was just determining everything that was relevant for a service, processing large amounts of data, tagging and indexing it appropriately, and supporting very deep multi-faceted searches, using the same semantic technology as described above, but tuned for different service (instead of product) domains.

That’s All For Now, Folks!

Again, focus on supplier discovery was, and still is, limited as there were, and still are, only a few vendors doing it. The good news is that we’re starting to see the technology predicted for “tomorrow” five years ago starting to emerge in these platforms as well.

SUMMARY

Now, we realize some of these descriptions, like yesterday’s, are also quite brief, but again, that’s because this is not entirely new tech, as the beginnings have been around for a few years, have been in development and discussed as “the future of” Supplier Discovery tech before Gen-AI hit the scene, and all of these capabilities are pretty straight-forward to understand. And, if you want to dive deeper, the baseline requirements for most of these capabilities were described in depth in the doctor‘s March 2019 articles on Spend Matters. The primary purpose of this article, as with the last, was to explain how more sophisticated versions of traditional ML and AI methodologies could be implemented in unison with human intelligence (HI!) to create smarter Supplier Discovery applications that buyers could rely on with confidence.