Category Archives: Technology

Dear Enterprise Software Vendor: Should You Fire Your PR and Marketing?

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, as a few non-isolated incidents opened up a whole new line of questioning.)

In response to a post by eCornell (which is/was here), THE REVELATOR wrote this comment (which is/was here) which is repeated here in its entirety in case it gets deleted, since anytime we tried to have a serious conversation around sales, marketing, public relations, and/or Gen-AI with Big X firms and/or (mid-sized) consultancies and analyst firms, they have quickly deleted our comments, and sometimes their entire posts rather than enter into a real conversation on the subject (and now we have developed an implicit distrust any corporate account and keep copies of everything):

NOTE: The following post was inspired by a comment by Paul Rogers

Despite feeling like someone walking the hallowed halls of Cornell University wearing a “Yeah, Harvard University” t-shirt, sometimes you have to say things that need to be said – which is the purpose of sharing this article.

Ask ChatGPT the following two questions:

? What is the role of the Public Relations professional?
? What is the role of the Marketing professional?

Do you see any mention of end client or customer success as a priority? Whose best interests are PR and marketing professionals focused on? What does the answer to these questions tell you?

Corporate communication has always been about putting a positive spin on business and the brand. It reminds me of the 1986 Richard Gere movie Power – if not a great movie, it is certainly interesting and engaging. Denzel Washington’s role as public relations expert Arnold Billings is worth the price of admission alone.

Unfortunately, beyond the company they represent, are PR and marketing people doing more harm than good?

Thoughts?

To which the doctor responded (which is/was here)

Well, SI, which has repeatedly told companies in our space to fire their PR firms going back to 2008: Blogger Relations, firmly believes that PR firms are doing more harm than good because

  1. you are NOT selling enterprise software to consumers and
  2. it’s not “image”, it’s “solution”!

As for marketing, corporate marketing can be good if it exists to educate and explain, but when was the last time that happened on a regular basis in our space? Over a decade ago … now it’s all AI-this, orchestrate-that, and whatever the bullcr@p of the day is. It’s all buzz, no honey. All show, no substance. All confusion, no clarity. (It’s bad enough that Trump has brought back the Land of Confusion with his populist politics that have taken by storm the first world over, we don’t need it in our workplace!)

So, right now, I’d say at least 6/7, if not 9/10, marketers are doing more harm than good and should be fired with their PR brethren.

There are over 666 companies in our space, and way too many pandering any type of solution you can think of. While we need at least 3-5 in each industry group – market size – geo region – module focus you can think of for competition, we don’t need 30+. Most are not going to survive, especially when most of these don’t have solid solutions built from years of experience that solve real customer problems (as opposed to just offering some shiny new tech that looks good but doesn’t solve the majority of pain points in real organizations).

This means that companies need to focus less on marketing and selling and more on:

  • market research, especially listening to what the real pain points are of the customers they want to sell to (and they need to focus in on a customer group here, you can’t be everything to everyone in our space and any company that thinks it can is the first company you should walk away from)
  • solution (not product) development — not shiny new tech, tried-and-true tech that works
  • market education, explaining what they do, how they do it, and why it solves real pain points after building a solution that solves the pain points they identified in their research

Which means, especially if money is tight, they should forget the marketers and instead focus on hiring researchers and educators. People are getting tired of the 80%+ tech project failure rates. They’d welcome some real insight and real focus on real solutions. If only the market would wake up and realize this!

Advanced Supplier Management 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 (or should be) available in leading best of-breed systems. And we’re continuing with Supplier Management.

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 Management 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 Procurement success. (This article sort of corresponds with AI in Supplier Management Tomorrow Part I and Part II that were published in May, 2019 on Spend Matters.)

TODAY

Auto Profile Updates with Smart Information Selection

In our last article, we noted that in first, and many second, generation Supplier Management solutions, a supplier was always forced to create a profile by scratch, filling out a bevy of pre-defined form fields — even if they had all of that data in a well formed (metadata rich) xml or csv file. That’s why yesterday’s Supplier Management solutions contained functionality to auto-complete profiles wherever this data was easily available in standard formats.

But the biggest problem remained — supplier profile maintenance. A supplier profile is only accurate the second a supplier hits confirm/complete. Then, their main contact changed. They changed their mailing address. They moved HQ. They offered a new product. They dropped an old one. And so on. And, of course, they never maintained their profile, and you never verified it until you went to call, mail, or order and that person wasn’t there, the mail got returned, or the order was rejected (because the supplier no longer made the product). Then, you went to the website, found the new main line, called, navigated to the right person, got the right info, and maybe remembered to update the system.

So, as errors were discovered, some critical ones would be corrected, but most would remain unchanged or unnoticed and over the years errors — including information on critical insurance, regulatory approvals, and other key business requirements that put the organization at high risk if not verified — continued to pile up. After a few years, the record becomes more wrong than right. Not good.

So today’s solutions make use of the fact that information typically gets updated somewhere, even if not in the application. They monitor the supplier’s website for changes in contact information, invoices for address and product information, state and country registries for business information, and so on and when changes are detected, automatically update the supplier profile if the changes can be independently verified (through a third party authority, to prevent hacks or fraud from changing the system) or present the new data for approval to the relationship manager. All this takes is simple website and data source monitoring, scraping, reg-ex based pattern matching, and automated workflows. For complex information, a bit of semantic processing. Nothing beyond classical, proven, tried-and-true AI is needed.

Market Based Supplier Intelligence

Today’s supplier management platforms can integrate with multiple marketplaces, communities, partners, GPOs, and specialized compliance, sustainability, and risk data platforms, use rule-based transformations to harmonize all the data, and use built-in algorithms to extract intelligence at a market level.

Your company data gives you one view into a supplier; your vendor-based community, which is usually limited to similar companies in your industry that the vendor was able to sell, gives you another view; but the market gives you yet another view yet. Mathematically, one data point doesn’t tell you anything. If only nine other customers use the vendor and share their data through community intelligence, that gives you 10 data points, which gives you some data on the supplier’s performance and their performance for you relative to others, but 10 data points is not statistically significant. But if 30, 50, 100 data points can be collected from the market, that gives you deep insight with deep statistical significance.

On top of the data, and a few powerful cores (few, not a few thousand), all these platforms need is basic statistical calculations, trend analysis, classical machine learning, semantic processing, and sentiment analysis … all of which have been market ready for over a decade.

Real Time Relationship Monitoring

Relationships are more than just performing to a contract. They are about building a working arrangement that is beneficial to both parties. One where both are willing to admit problems, collaboratively explore potential solutions, and work together to achieve them. One where, when there are no problems, both are willing to find ways to improve.

As a result, relationship monitoring is more than just supplier performance monitoring. Especially since the relationship can be bad even when the performance is (still) (surprisingly) good, and the relationship can be (reported as) good when the performance is bad.

However, if you turn that semantic and sentiment analysis that was typically done on market data and public comments on internal communications, you can start to build up a picture of the overall viewpoint and sentiment on the relationship from both sides, what successes or issues are contributing to that, and if the situation is improving or deteriorating over time (by trending the number of spikes in communication with sentiment that is overly positive or negative). It’s not foolproof, as both sides could adopt strict, formal, communication no matter what, but since people are human, they tend to get hotheaded and lose tempers (and let the words fly) if they are really upset or jubilant when they are really happy (and let the praise fly), and while minor changes in relationship sentiment might not be caught (within tolerance), major changes will. Moreover, you’re not going to get rigid, controlled, strict, formal communication until threats of a lawsuit fly, but then it’s too late!

Automated Resolution Plan Creation, Monitoring, and Adjustment

Not only can supplier management platforms automatically detect issues (by rapid increases or decreases in trends or metrics), they can also correlate them to included resolution plan templates, automatically instantiate them and customize them to the issue in question, walk the supplier relationship manager through the resolution process, monitor progress, and automatically adjust the plan, and timeline, as needed as new information, good or bad, comes in.

Each default template can be correlated to a particular metric, trend, or sentiment driven situation, so selecting it is just a lookup. Instantiation is just filling in the blank with the appropriate category, product, service, and metric information, through reg-ex matching and search and replace. Robotic Process Automation (RPA) walks both sides through the process. Monitoring alerts either side when something is updated or not completed on time through more RPA. And adjustments can be made to trend lines based on average timelines on similar projects and current trends at each milestone.

Automated Risk Mitigation Strategy Identification

It’s one thing to detect risk, which is pretty easy along many dimensions when you have a lot of data at your disposal, and relatively straightforward to predict the likelihood of some risk events, but it’s a lot harder to determine which mitigation strategies should be employed when it looks like a risk is going to materialize.

But that doesn’t mean it can’t be done, or isn’t doable by the best of platforms. Just like a platform can come equipped with issue resolution plan templates, it can also come equip with standard risk mitigation strategies, which are essentially action plans to be automatically customized with the specific category, product/service, logistics, and supply line details. This is just pattern matching and semantic contextual awareness.

When all of this is combined with (near) real time monitoring across data sources, that are continually looking for relevant news sources, changes in metrics / prices / trends, etc, it’s like magic (although it isn’t). The platform detects risks, finds the most appropriate mitigations, and present it to the relationship manager. An all it uses is math, traditional machine learning, and traditional semantic/sentiment analysis. And, of course, a lot of up-front human intelligence (HI!) in the creation of this solution.

Automatic Real-Time Resource Re-Alignment

Corrective action plans and risk mitigation plans have something very important in common — people. People who create them, approve them, execute them, and monitor them. This requires resources to be constantly assigned, monitored, replaced as soon as they are unavailable or needed on more pressing assignments, and reassigned as the issue is resolved or the mitigation complete.

And while it will often be difficult for a project manager, or even a resource manager, to determine when to remove an organization’s best problem solver from a critical corrective action project to address a less critical risk mitigation project, or vice versa, even when the manager can’t think of someone else who could address the less critical risk mitigation project effectively, even when there is another moderately experienced problem solver that could step into the critical project, the software will be able to compute when that should happen if the organization defines the rules as to when that will happen based on hard metrics.

For example, if you define assignments to correlate resources to the projects with the highest cost (should the issue persist or the risk mitigate), and you define the cost of an issue based on its expected impact if unsolved, and the cost of a risk as its expected impact if unaddressed (using a fixed cost or a formula if those 10,000 processors don’t arrive and you have 10,000 vehicles you can’t complete), and you associate a seniority with each resource, it’s simply rank ordered matching.

If there aren’t enough resources for all problems, you can apply simple optimization to maximize the impact of your most senior resources. And, again, there is no Gen-AI needed!

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 years, have been in development for a few years and discussed as “the future of” Procurement tech before Gen-AI hit the scene, and all of these capabilities are pretty straight-forward to understand. Moreover, if you want to dive deeper, the baseline requirements for most of these capabilities were described in depth in the doctor’s May 2019 articles on Spend Matters. The primary purpose of this article, as with the last, is to explain how more sophisticated versions of traditional ML methodologies could be implemented in unison with human intelligence (HI!) to create smarter Supplier Management applications that buyers can rely on with confidence.

A Company Should Never Build Its Own Enterprise Software Systems …

… and the fact that, apparently, hundreds are now thinking about building their own custom ProcureTech systems (as highlighted by a DPW Kearney presentation and referenced in THE REVELATOR‘s remote dispatch) demonstrates that the current marketing and methodologies being employed by software vendors, consultancies, and analyst firms are abysmal failures (and reinforces my statement that PROCUREMENT STINKS and that we have to clean it up)!

(The only partial exception is if a software company is building a piece of tech for a company of its size and industry, i.e. tech sourcing for startups, then it should use its own software and eat its own dog food, but it should not build any modules it doesn’t intend to sell.)

Why?

1. A company that is NOT a software company is NOT an expert in building products.

Moreover,

1b. A company that has not been validated to be best in class across the board in a function (and no such company exists, FYI) is NOT an expert in what it should be doing process wise, only on what it is doing process-wise.

As a result, it might not even be in the best position to design digital workflows, and especially not automated ones. Furthermore, even if it could do better than most on the digital workflows and automation, that doesn’t mean it can identify the right technologies for those workflows or the right stacks to build them on. And it certainly doesn’t have the chops to ensure the technology is secure, respects privacy rules, uses safe third party tech, keeps it up to date, etc.

In order to get these chops, they would have to hire a full IT team, which is very expensive, and would soar the lifetime solution cost to 10X or 20X what a solution offered by a modern SaaS provider with a multi-tenant stack would cost. With a significant percentage of that cost Up Front!

There only other option is to farm it out to a consultancy, and who are they going to choose? Probably a Big X. (You know, usually the exact opposite of who you should be hiring for niche / new technology, as pointed out in our post on When Should You Use Big X. )

2. Building takes years, and by the time it’s built, it’s out of date.

Companies need solutions now, not solutions in two, three, or five years … solutions that, by the time they are rolled out, are already outdated.

Moreover, by the time it’s rolled out, your other organizational systems are going to have changed, and then you have to integrate all the other systems to your custom solution on your own as your vendors won’t have out of the box connection options to your custom system, and that’s additional work and expense before you can get your outdated system rolled out.

3. It’s not about the product, it’s about the process.

Which means what a company really wants is a solution that embodies standard best practices with configuration options that allow the buyer to tweak it to their specific needs. As well as a solution that integrates well with their current ecosystem and provides the people who need it access when they need it as well as visibility when they need it. (In today’s verbiage, intake and orchestration, which is not new … intake was fully there in Coupa 1.0 [with respect to what the platform could do] in 2006 and Ariba was working on e-Forms back in 2000.)

3b. Furthermore, it’s about the data that drives that process!

And the most important requirement is the ability to export all system data in a standard format at any time! (Which is what almost everyone overlooks, because systems come and go, but as long as you have the data, it doesn’t matter.)

The Conclusion

And when you look at this, and you look at who’s out there, you have to wonder why more than a few companies couldn’t find at least 3 to 5 potentially good options for any ProcureTech need. The only reason, as far as I can tell, is they can’t identify them. (Because they are out there for almost every need in almost every organization in almost every industry across almost every region. As someone who has reviewed over 500 solutions in depth over two decades, the doctor says this with certainty.)

So why can’t these companies identify good vendors? As has been repeatedly commented on LinkedIn by the doctor and THE REVELATOR: there’s too much noise!

Marketing has devolved into a constant stream of one-way buzzword soundbites … which we tried to demystify a bit in the linked article earlier this year, but AI-backed, orchestration, autonomous, smart, etc. is all meaningless, and people know it.

In order to make a proper technology identification, a buyer needs to identify technology that solves the problems they are facing every day — which means they need focussed, educational, messaging that tells them about real problems the vendor’s technology was designed to solve. (Something we used to have a lot of in this space, but something I haven’t seen much of since the big M&A mania in the latter part of the 2010s where ProcureTech became the new FinTech, big enterprise investors moved in, and it became all about sell, sell, sell [and not solve, solve, solve].)

Now, this is something we should be seeing from analyst firms, but we don’t. We just see the same old maps with the same old enterprise vendors which hand over six, if not seven, figures a year to the firm to be included in their quadrants, waves, and marketscapes. Maps which are meaningless when each axis has like six different, usually subjectively scored, dimensions meshed into one.

And of course consultancies aren’t helping because the mid-markets exist by specializing in implementing smaller suites and best-of-breeds and living off of implementation referrals to the solutions they’ve learned to implement; while the Big X will quite happily tell you to build your own because they expect they will be the ones to design and build it, charging you tens of millions for something you might be able license for 1 Million a year, implement for 250K, and integrate for 500K if you are happy with an 80% to 90% solution (and then use the Big X for staff augmentation or custom extensions at a lower up-front cost, saving money for more valuable services later when you should use the Big X (and not cheap out on a low-cost consultancy without the experience).  (It would be very poor business for the Big X to turn you away!)

But at the end of the day, while it’s still buyer beware and a buyer should do his homework before engaging any vendor, we can’t help but think that a share of the fault lies with the majority of vendors who have abandoned solution first, education first methodologies and allowed revenue operations to switch to a marketing and sales first approach with no questions, please.

the doctor can’t wait for the coming rapture where THE REVELATOR has predicted that 75% of ProcureTech vendors won’t survive unscathed, because only two groups of vendors are going to survive — the really big suites with enough customers to keep going on current install base, and the new vendors who are smart enough to go back to basics, solve real problems, and lead with real solutions. Those are the vendors that SI has focussed on since it began, and the vendors that you need!

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.)