Category Archives: Technology

Advanced Supplier Management YESTERDAY — 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 application, 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 (as we don’t really have true appercipient [cognitive] intelligence or autonomous intelligence, and we’d need at least autonomous intelligence to really call a system artificially intelligent — the doctor described the levels in a 2020 Spend Matters article on how Artificial intelligence levels show AI is not created equal. Do you know what the vendor is selling?) that have been available for years (if you looked for, and found, the right best-of-breed systems [many of which are the hidden gems in the Mega Map]). And we’re going to continue with Supplier Management. (Find our series on Advanced Procurement — No Gen-AI Needed! Yesterday, Today, and Tomorrow; our series on Advanced Sourcing — No Gen-AI Needed! Yesterday, Today, and Tomorrow; and our series on Advanced Supplier Discovery — No Gen-AI Needed! Yesterday, Today, and Tomorrow through the embedded links.)

Unlike prior series, we’re going to mention some of the traditional, 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, might not match one-to-one with what the doctor chronicled five years ago because, like time, tech marches on.)

Today we move on to AI-Enhanced Supplier Management that was available yesterday (and, in fact, for at least the past 5 years if you go back and read the doctor’s original series, which will provide a lot more detail on each capability we’re discussing). (This article sort of corresponds with AI in Supplier Management Today Part I and Part II that were published in April, 2019.)

YESTERDAY

Auto-Fill Onboarding

While early 1st and 2st generation supplier management platforms required a supplier to create a full profile from scratch and enter all of their information, third generation platforms, which define expected formats for each field and have contextual awareness, can pull in the data from third party profiles, market databases, supplier forms, and even csv or xml exports of a supplier’s profile from another site.

Using classical semantic parsing, pattern matching, flexible reg-ex rules based data format validations, and any available meta data, even yesterday’s platforms could auto-fill the majority of a supplier profile form if the data was available in textual format for parsing.

Basic Community Intelligence

As per our coverage of supplier discovery, the reality is that this “AI” like functionality doesn’t require any “AI” at all. Community Intelligence just requires the amalgamation of data across customers, which is easy to do with multi-tenant SaaS as long as the customer agrees to sharing their reviews and insights (which could be part of the contract), and the supplier is made aware (which is part of the waiver to participate in customer events) of what is being shared.

It’s just math for averages, time series for trend series on those averages over time (of quality ratings, performance ratings, OTD ratings, etc.), and consolidation of tagged reviews. The only AI that would be needed is semantic processing if the platform provided a sentiment analysis across the community.

Real Time Performance Monitoring

As written five years ago, the last thing you want is to find out without warning that your primary supplier for a critical component in your new engine, control system, or IoT platform is bankrupt and no more shipments are coming; that a recent shipment has a 10% defect rate that is 10 times the acceptable, contracted, level; or that the custom factory redesign you just contracted for is going to take an extra six months when it should be 80% done.

Also, as written five years ago, none of this needs to be the case. There’s no reason a good platform could not alert you to leading indicators correlated with bankruptcy. Or a pattern of (slightly) late deliveries that is getting worse over time. That defect rates, even if within tolerance levels, have been increasing rapidly in recent shipments. Or that the last three key project milestones haven’t been met and the project is tracking to at least three months late.

With regards to early detection of bankruptcy, pull in financial risk scores monthly from your financial risk provider, look for downward trends (simple math), and monitor for alerts. Use the community intelligence identified above to identify late deliveries. Alternatively, if that’s not available, and it’s a big supplier with multiple customers in your country, monitor the public port data for its shipments … if they used to be every two months, but are now every three or four months, with an average volume per shipment that’s going down, that’s an indicator of trouble. With regards to your needs, track all of the rejected shipments at the warehouse, the returns, and keep a running tab on defect rate over time, again looking for trends in the wrong direction in terms of defects per shipment or returns per month.

There is so much you can do with just math. So do it!

Automated Issue Identification

As per our article five years ago, if the supplier management platform is integrated with organizational Sourcing, Procurement, and/or ERP systems, then the platform can automatically import objective supplier metric data as well as subjective supplier performance data from individuals across the organization that interact with the supplier.

Building on real time performance monitoring, the platform can monitor a whole host of metrics, trend them over time, identify drops that can signify issues, and alert the buyer if a dangerous drop is detected. Again, it’s just math.

Automated Risk Identification

The automated issue identification capabilities of a properly implemented and integrated supplier management platform are great, but as we have hinted above, the best platforms can also detect potential risks using leading indicators spit out by cross-organization metrics, trends, reports, and sentiment.

Remember, in addition to metric data, it can also take advantage of the community intelligence to identify early risk indicators. It can track the overall trend of promotion (against pre-existing tags) of a supplier for specific capabilities and the overall tone and sentiment of comments, and then compare that to the overall trend of anonymized price and performance data, and so on to detect when the performance or rating of a supplier is improving or declining, and, possibly, even how fast a rating might be declining which could indicate not just potential problems but risk.

Now integrate this to third party intelligence platforms with financial, CSR, operational, etc. risk and you start getting 360-degree risk profiles — and super early warning indicators since you never know where they are going to come from (the risk assessors, the community intelligence, or your own metrics). It’s all metrics, trends, and thresholds. Math. Good ol’ math.

Automated Resource Assignment

The best platforms support corrective action management, new product development, and supplier development initiatives. Each of these typically require project plans that require resources to support them, Always human resources and sometimes even physical organizational assets or IP assets (including software licenses).

If the platform is connected into a project management platform which has all of the information on organizational resources, and the organization’s asset management software, since the platform will know what skills are needed for the project, as well as what assets the supplier needs, it’s just a matter of best-match mapping. A great supplier management platform could do that through simple match computations and allocation tracking. When there are conflicts, it’s just a simple optimization problem for the best match.

SUMMARY

Now, we realize this was very brief, but again, that’s because this is not new tech, that was available long before Gen-AI, which should be native in the majority (if not the entirety) to any true best-of-breed Supplier Management platform, that is easy to understand — and that was described in detail in the doctor’s 2019 articles for those who wish to dive deeper. The whole point was to explain how traditional ML methods enable all of this, with ease, it just takes human intelligence (HI!) to define and code it.

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?