Category Archives: Technology

That’s Right, You Do NOT need AI for Automation!

In our last article, we stated that our space was full of Overpriced “AI” you don’t need in source-to-pay, and one of our three examples was “Sourcing Automation” in Sourcing. To be clear, we’re not saying you don’t need automation — the whole point of software has always been efficiency through automation — we’re saying you don’t need “AI” automation.

The reason we’re doubling down into this topic is that we know there are a number of vendors pushing AI Automation and while automation is very good, AI is just not needed. But we know you’re going to get pushback if you echo the doctor‘s viewpoint here, so we’re going to double down into the details and explain why no AI is needed for great automation.

In our last post, we noted that, at its simplest, it’s the ability to auto-source a (set of) product(s) or service(s) once the need has been identified or the request approved. It’s useful, but you don’t need AI to accomplish this, just good-old rule-based (workflow) automation. After all, it’s just

  1. instantiating a new RFP (which can be done if you have a template tied to the product/service types)
  2. distributing it to known, approved suppliers (which is easily done if you have supplier management that tracks approval status and associated products/services)
  3. collecting the bids (automated submission management through a portal or provided spreadsheet for upload)
  4. selecting the lowest bids and marking it as an approved award (simple analytics)
  5. assembling the contracts (with templates, it’s just sucking in the supplier details, product details, and bids using tag-based search and replace)
  6. push it into the e-Signature portal (via the API)
  7. alert the buyer when the contract is ready for signature (via alerting)

1 You just need templates, and good providers have had those for a long time. And “AI” is not going to invent one you can trust.*1 It’s not too hard to tag your (provider’s) existing templates to all of the products and services you buy, and you only have to do it once.

2 When you onboard a supplier, you should tag it as approved, associate it with the products and services it is approved for, look up its risk and environmental scores, and track its performance over time. If it’s performance drops, it can automatically be suspended from consideration for new projects using old-fashioned business rules that will prevent it from being included in events it shouldn’t be. Thus, approved supplier management isn’t that hard to do and simple saved searches find all the suppliers that should be automatically invited to an event.

3 RFP and e-Auction software has been around for 25 years, so don’t let anyone ever tell you that you need AI.

4 If you’re trying to administer an award subject to constraints or goals, that’s good old fashioned strategic sourcing decision optimization. That’s not AI. MILP using classic tableau and interior point algorithms works just fine in predefined scenarios that suck in the organizational constraints … that leading SSDO (Strategic Sourcing Decision Optimization) providers were building over two decades ago.

5 Contract templates should be prescribed by Legal Counsel, not by software flipping random bits using layered statistical algorithms in combinations no one truly understands. The vendor will provide you with templates, but you should be the one reviewing them to make sure they are too your liking. This includes the standard clauses and variation by geography, industry, or risk you want to address.

6 Software integration happened for decades before AI.

7 Alerts have been standard software capability for decades, no AI needed.

If the right data is captured, and the right rules are written, standard workflow-driven software systems can be fully automated without any AI. The only thing preventing them from going from one step to the next is the human verification checkbox being completed. You can turn that off and they will work just fine. So, again, don’t be fooled that you need AI for Sourcing Automation, because you don’t. And with rules-based systems, you’re guaranteed you won’t get the odd, unpredictable result, every 10th sourcing project (because AI is only statistically effective, which means, eventually, it will always fail).

*1 Sure “Generative AI” can generate one. But there’s no guarantee it won’t be hot garbage.

Yes Mid-Markets, 120K is More Than Enough for Source-to-Pay!

the doctor is sure that by now you have certain (mega-)suite vendors whispering in your ear that you really need their full 1 Million+ (annual subscription) S2P solution to maximize efficiency and savings (and that the doctor was crazy*0 when he told you that you should be able to get a sufficient Source-to-Pay solution for 120K a year), which, while possibly true stated that way, you don’t need to spend nearly that much to maximize your ROI.

But how do you maximize ROI without necessarily maximizing savings and/or efficiency? Simple! The same way you optimize profit by optimizing COGS vs. increasing volume. Just like every $1 of savings goes straight to the bottom line vs only $0.10 of revenue, every dollar you don’t spend on a technology solution goes straight to the bottom line vs. only squeezing out an extra 1% on savings.*1

But the best way to see this is to, gasp, do some math! Let’s take three mid-markets at 250M, 500M, and 750M. We’ll use industry averages for COGS (with 33% salaries & contractors; 2% utilities; 5% rental; and 20% amortization/depreciation) and assume 40% external spend. Depending on the industry, external costs can go to 50% or more, but not much in the Mid-Market (MM). We’ll assume an average 5% savings potential and 80% spend addressability over 3 years (as some existing contracts will be long term and not addressable in the short term, and some tail spend will just be too small / one time to ever bother with). We’ll assume that a base solution can achieve 80% of that savings potential, or 4% over three years (if there is sufficient manpower to address all the relevant categories [semi]-strategically).

 

Size 250M 500M 750M
Addressability (80% of 40%) 80M 160M 240M
Savings Potential @ 4% 3.2M 6.4M 9.6M
3 Year Cost 360K 360K 360K
ROI 8.8 17.6 26.4
Savings Potential @ 5% 4M 8M 12M
3 Year Cost 3M 3M 3M
ROI 1.4 2.7 4.0

 

Now, what type of ROI would you like to see if you are a 250M MM? A 1.4X ROI or a 8.8X ROI? the doctor knows what type of ROI he’d like to see! Also, if the mega-suite provider cuts the price in half, it only doubles the ROI to 3.2X. Barely acceptable, and you need the manpower to identify the full savings potential and everything to go perfectly to realize it. (What’s the probability that this will hold true continuously for three [3] years? Zero Percent. 0%)

Unless you have a (very) large category over 10M (where the savings potential on that category is 500K), the reality is that the 80% solution you will get by an average across-the-board solution / self-assembled platform-powered BoB suite will provide you an ROI that far outshines what the oversized, overpriced solutions will do for you as a mid-sized business. (Those suites are only needed for 1B+ enterprises where there are 50M to 100M+ categories where an extra 1% makes a huge difference.)

the doctor loves sourcing optimization, but it typically won’t find that much savings beyond what you can find with good spend analysis on RFP data in a category < 5M. (It might take a few hours of spend analysis, but you will get 80% of the savings with intelligence. If the vendor includes an affordable optimization module (2K/month; likely with model size caps), then you should use it on every category, if just to get a baseline, as you will get a good ROI from the module with continuous use, but if they want 10K/month and you are a 250M business, you likely won’t get enough of a return, especially since most of your categories aren’t that large or complex. Note that if you are a 1B+ multi-national enterprise, the story is the exact opposite. You absolutely need it and in your well managed categories, you won’t identify enough savings without it.)

For most categories, all you need to do in sourcing is 3-5 bids, side by side unit cost and total landed cost (TLC) comparisons, supplier award selection with RFP (spend) analysis, contract cutting to capture the price, configured POs in the eProcurement system to capture the contracted price, and line-item match on the invoice to the PO to make sure you’re paying what you should be. This is two-decade old tech now, but more than sufficient, when properly implemented and enforced, to capture 80% of the “savings” (or cost avoidance) in a category. Procurement savings come more from the proper implementation of a process than from technology that enables that process. What technology does is make it easy to do the process efficiently and effectively because it can guide you through the process, prevent you from missing steps or making mistakes, provide you the insight you need to make the best decisions, and even train you on best practices you aren’t familiar with. And allow you to repeat the process many more times on many more categories in a much shorter timeframe than if you were trying to do it all by hand.

Plus, the technology will allow you to do more with less, so you can minimize the need to expand the Procurement team as the company grows. Remember, good people cost $$$. In fact, a fully burdened high-end resource will cost as much as you pay for the tech, if you are paying the right price. This means that the tech will not only provide you an ROI on measurable cost reductions, but a measurable cost avoidance as you grow as you will not need to add as many people to a Procurement department that will become more efficient over time (as more and more tactical tasks get automated, freeing up the team to focus on value-add tasks). (Remember, tech never replaces the people you need, it just makes them many times more efficient so that you only need one or two high performing individuals for a function vs ten for one that is poorly managed; allowing you to add those ten resources elsewhere to produce more product or grow the business further. However, remember that Procurement does more than one function, so you may still need those 10 people for contract management, supplier development, additional strategic sourcing events, etc. but you won’t need them processing paperwork.)

So don’t overpay for S2P tech. You absolutely need S2P tech, but overpriced tech won’t get you the ROI!

*0 they may be right, I may be crazy … but it just may be a lunatic you’re looking for …

*1 An extra savings of 10% on a maximum savings of 10% leads to a maximum additional savings of 1% overall on a single category. In inflationary times, which we are now back to, you’ll never find more than 10% slack in the TCO of any category. In fact, you’ll do good to find 5%, which means going from average capability to advanced capability will only shave an extra 0.5% off of the total category spend on average.

Don’t think that these inflationary times are going away anytime soon. Supply chains are at their shakiest thanks to both the pandemic and the repercussions thereof, the rapid increase in climate change which has led to a rapid increase in natural disasters, the increased geopolitical destabilization around the globe, and the rebelling workforce, many of whom have gone from living barely above the actual poverty line (relative to where they live) to below it. Now add that to the flat and recessionary economic conditions in most major GDP players, and we won’t be seeing good times ahead for quite a while.

Don’t Trust an Analyst Firm to Score UX and Implementation Time!

A post late last month on LinkedIn started off as follows:

“If you’ve ever read any research papers or solution maps on procurement tech, you’ve probably figured out a couple of things.

1. It’s confusing and overly complex
2. It doesn’t cover the basic, most obvious-of-the-obvious fundamentals that everyone needs to consider.

These are:

– User interface and user experience (UI/UX)
– Ease and speed of implementation

Why don’t they do this?

Honestly, I don’t know the answer.

The cynic in me says it’s because their biggest paymasters have a horrible UI/UX and require a very complex and lengthy implementation.”

This really bothered me, not because UX and implementation time aren’t super important, they are, and they are among the biggest determinants of adoption (which is critical to success), but because anyone would think an analyst firm should address this.

The reality is that no proper analyst will attempt to score these because they are completely subjective! As a result:

  1. There is no objective, function-based/capability-based scale that could be scored consistently by any knowledgeable analyst on the subject and
  2. What is a great experience to one person, with a certain expectation of tech based upon prior experience and knowledge of their function, can be complete CR@P to another person.

Now, some firms do bury such subjective evaluations on UX and implementation time in their 2*2s where they squish an average of 6 subjective ratings into a dimension, but that is why those maps are complete garbage! (See: Dear Analyst Firms: Please stop mangling maps, inventing award categories, and evaluating what you don’t understand!) So no self-respecting analyst should do it. As an example, one analyst might like solutions with absolute minimalist design, with everything hidden and everything automated against pre-built rules (that may, or may not, be right for your organization and may result in an automated sourcing solution placing a Million dollar order with payment up front for a significant early payment discount to a supplier that subsequently files for bankruptcy and doesn’t deliver your goods) while a second might like full user control through a multi-screen multi-step interface for what could be a one-screen and one-step function and a third might like to see as much capability and information as possible squished into every screen and long for the days of text-based green-screens where you weren’t distracted by graphics and animations and design. Each of these analyst would score the same UX completely different! On a 10 point scale, for a given UX design, three analysts in the same firm could give scores of 1, 5, and 10, averaged to 5 … and how is that useful? It’s not!

(And while analysts can define scales of maturity for the technology the UX is based on, just because a vendor is using the latest technology, that doesn’t mean their UX is any good. New technology can be just as horrendously misused as old technology.)

The same goes for implementation time. An analyst that mainly focuses on simple sourcing/procurement where you should just be able to flick a SaaS switch and go would think that an implementation time of more than a week is abysmal, but an analyst that primarily analyzes CLM and SMDM would call BS on anything less than six weeks and expect three months for an implementation time. This is because, for CLM, you have to find all the contracts, feed them in, run them through AI for automated meta-data extraction, do manual review, and set up new processes while for SMDM you have to integrate half a dozen systems, do data integrations, cleansing, and enrichment through cross-referencing with third party sources, create golden records, do manual spot-check reviews, and push the data back . Implementation time is dependent on the solution, the architecture, what it does, what data it needs, what systems it needs to be integrated with, what support there is for data extraction and loading in those legacy systems, etc. Implementation time needs to be judged against the minimum amount of time to do it effectively, which is also customer dependent. Expecting an analyst to understand all the potential client situations is ridiculous. Expecting them to craft an “average customer situation”, base an implementation time on this, and score a set of random vendors accordingly is even more ridiculous.

The factors ARE absolutely vital, but they need to be judged by the buying organization as part of the review cycle, AFTER they’ve verified that the vendor can offer a solution that will meet

  • their current, most pressing, needs as an organization,
  • their evolving needs as they will need to get other problems under control, and
  • do so with a solution that is technically sound and complete with respect to the two requirements above while also being capable of scaling up and evolving over time (as well as capable of being plugged into an appropriate platform-based ecosystem through a fully Open API).

A good analyst an guide you on ways to judge this and what you might want to consider, but that’s it … you have to be the final judge, not them.

That’s why, when the doctor co-designed Solution Map when he was a Consulting Analyst for Spend Matters, the Solution Map focussed on scoring the technological foundations, which could be judged on an objective scale based on the evolution of underlying technology over the past two-plus decades and/or the evolution of functionality to address a specific problem over the past two-plus decades. It’s up to you whether you like it or not, think the implementation time frames are good or not, believe the vendor is innovative or not, and are satisfied with the vendor size and maturity, not the analyst. Those are business viewpoints that are business dependent. Analysts should score capabilities and foundations, particularly where buyers are ill-equipped to do so (and this also means that analysts scoring technology MUST be trained technologists with a formal, educational, background in technology — computer science, engineering, etc. — and experience in Software Development or Implementation –and yes, the doctor realizes this is not always the case, and that’s probably why most of the analyst maps are squished dimensions across half-a-dozen subjective factors [as they are not capable of properly evaluating what they are claiming to be subject matter experts in; as a comparison, when you have a journalist or historian or accountant rating modern SaaS platforms that’s the equivalent of having a plumber certify your electrical wiring or a landscaper judging the strength of the framing in your new house — sure, they’re trade pros, but do you really want to judge their opinion that the wiring is NOT going to start an electrical fire and burn your house down or the frame is strong enough for the 3,000 pounds of appliances you intend to put on the 2nd floor? the doctor would hope not!).

The cynic might say they don’t want to embarrass their sponsors, but the realist will realize the analysts can’t effectively judge vendors on this and the smart analysts won’t even try (but will instead guide you on the factors you should consider and look for when evaluating potential solutions on the shortlist they can help you build by giving you a list of vendors that provide the right type of solution and are technically sound, vs. three random vendors from a Google search that don’t even offer the same type of solution).

Have the Analyst Firms Finally Admitted They Don’t Know What They’re Doing?

the doctor recently went on a big rant about the analyst firms and the utter lack of usefulness in the maps they release, the focus they put on what they don’t understand, and the award categories they invent because, even though they have/had some great talent (and should be doing incredible work), what they’ve publicly released has been mostly valueless to the market they’ve been trying to serve (when it wouldn’t be too hard to provide a lot of value based on all the research and work they do). In the doctor‘s view, this is very sad because if they could demonstrate the value they provide, they would be more relevant across the market (and likely get a lot more business from smaller and/or more innovative providers who think that, because of the budgets the big players like Oracle, SAP, and Coupa have, the analysts are always going to recommend those companies anyway).

However, now he’s gone from sad to mad about something he has just heard from a couple of vendors regarding one of the biggest firms, because, if true, it means not only do they not have a clue about what is and is not valuable in tech, but they are unnecessarily creating confusing and obfuscating technology that still may be best in class.

So what have they done now? Well, apparently they are now basing 30% of the score on whether or not the vendor has “AI” in their platform, something which they’ve repeatedly proven they have ZERO ability to score whatsoever! So, either a vendor makes false, grandiose claims (and tries to use Applied Indirection to fool the Analyst Idiot that they have more than Artificial Idiocy in their Application Implementation), or they get scored low even if they have the best technology built on best practices, proven algorithms, and consistent results that give their customers a 5X to 10X ROI.

True AI adds value, but, in the doctor‘s experience,

  • up to 80% of AI claims are Applied Indirection (at best) or Artificial Idiocy (at worst); in fact, some of the “AI” in spend analysis is still the “AI” they used in the early 2000s, and the doctor would rather not spell out that sad, but still true for some vendors, racial slur
  • up to 80% of the rest, or up to 16% of tech that claims AI, is level one Assistive Intelligence; and this is typically just classic RPA (Robotic Process Automation) using human-defined parameter-based rules, and the “AI” is the automatic parameter adjustment based on user overrides … not very intelligent, eh?
  • up to 80% of the rest, or up to 4% of the tech that claims AI, is level 2 Augmented Intelligence, which is the first level of AI where the tech can learn from human feedback and provide better insights and recommendations over time on one or more specific tasks, and the first level of AI that you should even consider as AI
  • up to 80% of the rest, up to 1% of the tech that claims AI, and the highest level modern technology has generally achieved, is level 3, Apperceptive Intelligence, or Cognitive Intelligence, where the systems can not only learn from specific human feedback to recommendations but from general knowledge and intelligence available to it from integrated data sources to mimic the performance of the best human experts over time, even evolving processes, behaviours, and actions within well-defined bounds
  • and then the rest, 0.1% or less, is nearing level 4, Autonomous Intelligence, where the system can learn, evolve, adapt, and maintain itself over time without human intervention … and hopefully execute meaningful, appropriate decisions grounded in best process and fact that considers all of the relevant information available (and not go off of the rails and advise you to commit suicide because you feel bad, Hail Hitler, or sacrifice a trolley full of people and a cross-walk full of pedestrians because there might be a cat in the road — all things AI has already done)

And even where a platform has semblances of real AI, chances are that the AI (the vendor is now forced to include or arbitrarily be relegated to the dustbin because, apparently, it’s not solutions but buzz-acronymns that matter now) is producing worst results than the best traditional algorithm or methodology on expert curated data sets and dimensions. For example, the vast majority of the market believes AI improves forecasting. It doesn’t. The best AI is still inferior to the best techniques developed in the 70s when applied to the right data dimensions. All the “AI”, which is just fancy, souped-up versions of classical machine learning (using algorithms developed in the 80s and 90s for which we didn’t have enough computing power until recently), does is run all of the data through a model that integrates classification with prediction to filter out the most relevant dimensions and the best curve fitting technique as all these algorithms, at the core, are based on 50+ year old statistics! This means that, at the end of the day, their best case performance is something a human genius figured out 50+ years ago.

But to achieve that best case, the developers have to implement the right AI algorithms, tune them properly, allow them to run long enough to correctly fit (but not over-fit) the training data sets, and monitor those algorithms over time … and to do that they need to be an expert in those algorithms, which they probably aren’t. So, in order to “check a box”, and sell you a product, they are ultimately integrating algorithms that will give you an inferior result (while requiring considerably more computing power that runs up your cloud utilization bill), versus sticking to tried-and-true algorithms and processes that their experts tweaked over years and that their experts can explain and verify at any time.

And this is an almost reasonable example of what a technology vendor might do (as the best predictive algorithms are not untested “AI” but based on classical, tried-and-true, statistical or optimization functions). Most of what the doctor has seen is MUCH worse than this. And the fact that some big analyst firms are now forcing vendors with good tech to integrate underdeveloped, unproven, and often untested AI just to get a rating, make a map, or be recommended is downright stupid.

SHAME ON ANY ANALYST FIRM THAT DOES THIS! Buzzwords are not products, and unproven tech is not value. Analysts should be recommending the best solutions, regarding of the tech they are based on. the doctor is simply appalled!

Seven Easy Mistakes Source-to-Pay Tech Vendors Can Avoid

A few weeks ago we wrote about Five Easy Mistakes Source-to-Pay Tech Buyers Can Avoid in their effort to procure a fit-for-purpose technology solution to help them with their current challenges because the wrong solution can often be worse than having no solution at all.

However, and this is one thing the doctor knows very well, it’s not just buyers that make mistakes. Vendors do too. Lots of them. Lots more than they’ll care to admit, and these mistakes cost them time, money, sleep, and, sometimes, satisfied customers — which is ultimately the most important thing as satisfied customers will renew software subscriptions indefinitely (while unsatisfied customers will try to end the subscription as soon as possible).

Especially newer vendors, and especially those that haven’t built and/or run a company in our space before. And while some mistakes will be unavoidable (innovation doesn’t happen the first time, some things can only be learned the hard way, etc.), most aren’t. (In fact, the vast majority aren’t.) Usually all that is needed is research and insight, which can often be obtained by overworked founders without enough time by engaging the right advisor*.

So, to help these vendors understand overlooked areas where they are likely making mistakes, and where they should at least get an independent review, so that they can bring you better solutions, we’re bringing to them (and you, so you ask the right questions when considering their solution) the most common mistakes the doctor has seen over and over (and over) in his long career as an (independent) industry analyst, blogger, technical solution reviewer, consultant, researcher, CTO, etc.

Lack of market understanding and the real needs of their potential market

The first time the doctor talks to a new company, either for an introduction, review, or due diligence, one of the first things he hears (or will ask if he doesn’t), is why the founders started this company. And the answer he gets the most by far, so much so that he’s lost count of how many times he’s heard it and struggles to point to significant companies where he didn’t, is because XYZ didn’t do this function we needed to be efficient so we figured there was an opportunity. This would be a perfectly logical response if:

  • XYZ was a company/product that was designed to serve the function the founders were trying to address
  • there weren’t already two dozen products out there that addressed the function already and, at the baseline, did the same thing; literally, the same thing

This becomes especially prevalent during every M&A frenzy where a PE firm decides they need a company that does X, like (accounts) payable(s) during the last frenzy (exacerbated by COVID when PE firms realized/decided that business needed to be conducted entirely online, and decided they all need a virtual collaboration and online payment solution in their network). And the doctor doesn’t want to tell you how many times he heard payments company X was started because bill.com or quickbooks didn’t do basic accounts payable functionality or how few (read: almost none) didn’t do any real research which, in just a few hours, would have uncovered two dozen plus companies with payables capability the founders were sure didn’t exist, and the real opportunity was only in differentiation, specific country/regulatory support, or price-point (as there weren’t a lot of solutions at an affordable price point for smaller mid-size businesses until a few years ago). And even worse, many of these founders thought analysts and potential buyers should be super impressed that they essentially re-invented the software process wheel for a particular function for the twenty-forth time.

So, dear vendor, before you go to market, do your research (or contract someone who can do it properly for you)! And if you don’t understand your real value, contract an analyst that can identify it for you. The market is fickle, unforgiving, and easily swayed by a better presentation (even when from a competitor with lesser technology). Given that the knowledge and resources are out there, there’s no reason NOT to get it right.

Lack of competitive landscape knowledge and the real needs going unserved overall or at an affordable price-point for their target market

Building on our last point, it’s not just knowing what’s out there and what it does, but where your competition is strong and weak, what markets they are going after, what markets you should be going after based on your relative strengths and weaknesses, and what price point that market can easily afford and buy within a reasonable length sales cycle.

the doctor realizes this can be very time consuming, but this is where an implementation consultant or the right analyst can be extremely valuable, as they can quickly provide you with this information based on publicly available knowledge on currently released products based on demos and product reviews they have done, (feedback from) implementations they have been associated with, and (feedback from) integrations that they (or consultants they work with) needed to do, and buyers. A good analyst can do this without sharing any roadmap or non-public details on not-yet released capabilities, and should do that as roadmap and un-released capabilities might never be released, and is not something you should be basing your direction on.

Not knowing your true capitalization needs pre-profitability

While we should applaud companies that can bootstrap, and provide a standing ovation to companies that can raise angel / VC capital early to accelerate development, we should ONLY do so if they make an effort to understand their true cost of development, how long it’s really going to take to make that first sale, how long after that until they will make enough sales to support the minimum headcount they will need to sell and support those customers, and how much cash it’s going to take to get them there and raise it, or at least pre-negotiate follow on raises/loans to get there after the first investment.

Too many good companies fail because

  • they don’t take the time to estimate the true cost
  • they do, but don’t stick to their guns and when the investors say “final offer” at 70% of the estimated amount, they say “we’ll make it work”

And they try. They make a valiant effort. And as money dwindles, they put in 80 hour work weeks, developing more, faster. They amp-up cold-calling, content generation, reach outs, etc. They make their most heroic efforts. But all for naught. You can rush development, but you can’t rush a sales cycle. People need to realize they need a solution, do their research, qualify you, get a budget, go out to RFP, follow an archaic corporate process, and, then, hopefully, they can buy your product. If you’re lucky, you’re entering the process after they get the budget, but then you are fighting against a favoured “incumbent” that they plan to buy from (once they eliminate you), but usually it’s before, which means, on average, you are waiting six months for them to get a budget in the next cycle. If you’re a few months away from closing the doors, that doesn’t help you.

So if you can’t get what you need, don’t start. We all know entrepreneurship sounds great. We all know it’s a great experience to have on your resume. But it’s stupid to start something you know has no chance of succeeding. After all, there’s always another opportunity out there where you stand a chance of success. (And that’s it, even if you have enough in a typical case scenario, pandemics can happen, disasters can happen, markets can shift, or a better solution can be released halfway through your development by someone else that had the idea before you and is currently developing it in stealth mode with double your funding and a marketing budget out of the gate.) So, dear vendor, wait until have you a true chance. Otherwise, you’re wasting your contribution and letting down your early adopters when you close your doors (and that hurts us all when they lose faith in smaller companies and go back to ERP).

Overvaluing the tech (and AI)

A good tool is worth good money, and a great tool is worth great money. And if the great tool significantly increases efficiency, identifies meaningful cost avoidance, and delivers a 5X ROI, such a tool can be worth hundreds of thousands (or even millions of dollars if it is used by hundreds, or thousands, of users globally). But good and great is relative to what it does, how many people in the business it’s used by, the value it is delivering, and, ultimately, the budget the business class you are targeting can afford to pay based on the first three factors.

Tech for the sake of tech, while cool, has no value beyond being cool. Even if you have a lot of actual “AI” baked in (and let’s face it, if you do, the “AI” is only solving a very focussed, niche, problem), it’s still valueless unless it delivers value. It doesn’t matter how long you took to build it, how much it cost you (which can be a very poor measure because if you didn’t have a good team, overpaid that team, didn’t have the product or goal well designed when you started, p!ss3d hundreds of thousands away on Class A office space and big parties, it might have cost you tens of millions to build something a smarter, more focussed, cost conscious team could have built for two million), or how unique it is — in business, it needs value.

And before you try to sell it, you need to understand that value from a customer’s viewpoint, otherwise you’re going to have quite a challenge and customers who would otherwise jump on something fairly priced will not buy it even when it could be the best solution for them. (It’s not what the competition is selling it for, it’s what it should be sold for … one of the reasons too many Procurement departments don’t have modern tech is that they can’t get the budget for software priced using traditional enterprise software pricing that only the F500s/G3000s can afford.)

Undervaluing the tech (and AI)

Again, a good tool is worth good money and a great tool is worth great money when it delivers the right efficiency gains, cost avoidance, and value to a business that is losing a lot of money due to inefficiency and lack of insight.

Thus, you also have to be careful NOT to undervalue the tool or slash the price in an effort to get customers in the door quickly or sell to smaller organizations than you should be selling to, especially if the tech was expensive to build and no other organization could build it for less than 80% (or more) of what your organization invested into it and the cost of maintenance/continued development (due to advanced tech or unique capabilities or lots of integrations) is high. The reality is that once you set a price, that doesn’t become the floor, it becomes the ceiling, and if the price is not sustainable, you will go out of business and that will hurt not only you, but any early adopter that buys into you (and, again, that will hurt us all when they lose faith in smaller companies and go back to ERP).

Overestimating the DiY nature of the tech

Easy for you is not easy for a buyer. Remember, you’re the expert in the Tech — you built it, as well as an expert in the inefficiencies in the tech that came before — that’s why you built it, and an expert in the workflows that work well — that’s how you built it. You have the deep knowledge of the tech, the deep knowledge of the best practices, and the deep knowledge to know when a problem is best addressed by the tool, and when it’s not, and how out-of-the-box the support is, and how much has to be customized.

On the other hand, your potential client might be spending most of their time in Gmail and Excel, have never used the previous tool, and have no knowledge of current best practices. The customer may need training on the best practices, the workflows, and the tech, as well as a large reference library to remind themselves on how to use the tool if certain aspects of the process are not done very often (like once a month at most).

If services are needed, customers are not going to respond well to software only, or believe a low-cost when they know they will need the services. Understand the balance, present it appropriately, and sell it appropriately.

Misunderstanding the average customer capability & TQ

Building on the above, in addition to not overestimating the DiY nature of the tech, you should not misunderstand the average customer capability and the Technical Quotient of your target market. As we noted above, not all Procurement departments are advanced in the tech they have access to, and not all Procurement Professionals are adept with / used to modern tech. One has to remember that, for the longest time, Procurement was literally the island of misfit toys, and their understanding of technology and technology-enabled best practices was literally non-existent (as they typically didn’t use technology beyond the fax machine).

Even today, they may not be familiar with much more than basic consumer software for searching, e-reading, e-commerce, email, and gaming. Customized, deep, enterprise software may not be in their experience or repertoire.

Alternatively, if you are selling to a risk management or data analytics departments at big companies, they may have hired data scientists with deep training in mathematics and computer science used to not only using difficult mathematical (like Matlab and Octave) and analytics platforms (Qlik and Tableau), but building their own using open source analytics and data science platforms (like SciKit and Dataiku).

Know your audience and what they are capable of.

Failing to put the relationship first

In consumer software, it’s a transaction. But in enterprise software, it’s a relationship that you need to build, support, and adapt with. If the customer wants a transaction, they’ll use mass-market user-subscription based software or shareware. They’re going to you because they need services and support from a software provider that are experts in the technology and the process, can help them achieve their goals, and will keep the SaaS platform relevant.

* (HINT! HINT! STARTUPS/BEST-OF-BREEDS, STOP ASSUMING YOU KNOW IT ALL AND CAN DO IT ALL IN HOUSE! YOU CAN’T! YOU DON’T HAVE THE BUDGET FOR A FULL TIME EXPERT IN EVERY AREA. BUT YOU CAN OFTEN GET AWAY WITH PART TIME/SHORT TERM CONSULTING / ADVISORY. SO JUST DO IT!)