Two posts ago on the top final procurement concern of today (and the last five and the next three years) we told you that Gen-AI, which is (still) the tech-du-jour, is not really any different than every other tech-du-jour that we’ve had over the last two decades and, like all these preceding technologies (that were all over-hyped), it is not the panacea that will solve all your problems (despite claims to the contrary) and is, in fact, simply the latest incarnation of silicon snake oil.

Then, in our last post, we asked, and answered, why most (new) vendors are building on it. There are a host of reasons — which include greed, low TQ, hype, and cluelessness — and none of them are good. That’s why, as we stated, most (AI-first) start-ups today SUCK, and, to be honest, why most start-ups in our space suck in general (and do for at least the first few years of their existence, even if they aren’t AI first).

But we also told you that we’d tell you how a vendor can build a good solution, starting with V1. Just like selecting a solution that actually works is possible 80%+ of the time (if you follow the right method that we outlined in our series on Successful Vendor Selection Series, because, otherwise, your chances of success are about 12%), there are best practices that will maximize your chances of success. But like solution selection, don’t expect any of the big analyst or consult firms (that depend on never ending hourly support contracts) to give you any real advice! (They are all instances of The Vendor in Black … Comes Back!)

1A. Get Relevant Procurement Experience and Insight
By this I mean that if you’ve only worked for one or two companies and only done things one or two ways, you don’t really understand what Procurement needs generally — you only understand what your companies needed and what very similar companies in your niche industries need. With limited experience at one or two companies, you’re not building the perfect solution for the industry, you’re building the perfect solution for YOU, and YOU may not represent the majority of the market!

You don’t have this in your late 20s, or even your 30s. You have this in your 40s. (And then to run a successful startup, you need management experience — that’s why they’re saying 50 is the new 30 for startups … by then you truly understand what is needed and likely have the management experience to pull it off.) Any earlier/younger than this, and you better engage some real independent Procurement experts to help you define what you really need to do to address entire verticals or wide swaths of the market.

1B. Get Relevant SaaS Development Experience
You also need real SaaS Development Experience. The ability to vibe code, the ability to use low-code / no-code solutions, and even the ability to write web script DOES NOT COUNT! Script kiddies don’t build enterprise apps — the dot com boom and bust (which some of us remember — and the rest of you need to study because the Gen-AI bust could be as bad) made this clear. You need real, educated, experienced developers and architects who have worked in real tech companies building, deploying, and actually delivering enterprise apps! These are the only resources who build enterprise apps.

Now, it’s very, very unlikely you have both. That’s okay. That’s why you get the perfect partner that compliments you so that you collectively possess CPO (Chief Product Officer) vision and CTO capability from day one. Then, if the Procurement Expert founder is not a CEO, the two founders seek a third founder who is a real CEO with relevant C-Suite domain experience, and if the Procurement Expert founder is a CEO, the two founders seek a real domain expert who has product management experience who can be the CPO.

2. Define the problem you want to solve in detail!
What is the real pain point? What does the solution look like? How do you measure it? How do you get there?

Once you’ve answered the key questions and fully defined the problem, define the process that solves it. Then define the variations to the process. I.E. What are the core, required, steps. What are additional optional steps. Where might approvals or sub-processes be required in specific situations.

Then define what can be automated, what needs to be done by a human, and where there are multiple options.

Only once you fully understand the process and variation across companies of different sizes, categories of different complexity, and departments of different maturity in the verticals you are going for can you attempt to build a platform that will support it.

3. Identify the minimally appropriate and best-match algorithms for each process step and the best tech for stringing the algorithms together.

Some steps will just be collecting information on a form, validating the response type with regular expressions, and validating the data with third party integrations … and possibly require a(nother) user to accept it. Other steps will just be running pre-defined analytics and suggesting or taking an action based on the result, possibly using a rules-based multi-select with adjustable parameters. Others will be RPA auto-execute based on previous steps. Others still will be machine learning based on collected inputs from previous steps. And so on. (Very rarely will you need advanced AI and rarer still will you need [anything close to] Gen-AI. This is another reason AI-first is so wrong!)

When you go through this process, you will find that not only do most steps not require any (Gen-)AI at all, but most are better served without AI. You’ll find it only fits in the few situations it is good at (natural language processing, large document search and summarization, potential pattern identification, etc. for Gen-AI), and that if you apply it, you should do so narrowly, with custom trained models with guardrails and, if possible, have users accept recommendations to modify rules to reduce dependence over time.

4. Remember that good enterprise solutions have MDM (Master Data Management), Workflow, and Orchestration at the core.

These are not after thoughts. In addition, if you plan to support global users or sell your solution globally, multi-language support and internationalization MUST be at the core as well.

5. Select a programming language and an enterprise stack that supports ALL of the requirements identified above.

Not the stack that is cool, the stack that makes it super simple to get MVPs out the door, the stack used by your favourite AI platform, the stack recommended by your favourite cloud provider, but the stack that will work for the enterprise application you want to build. Then select the cloud provider — most of them are pretty competitive, and most of them support the majority of enterprise stacks, especially if they are not Microsoft (which wants a .Net/C# Azure Friendly Stack).

6. Plan out three years of major features.
These major features will support additional process extensions and related processes as there’s no significant shelf life for a niche app that only does one thing unless that one thing is so complex that almost no other application does it and the cost of building such an app from scratch by a new startup is prohibitive (especially relative to the untapped market potential).

Too many startups define the MVP, race to build the MVP, and then try to figure out what comes next. This is equivalent to shooting yourself in both feet with your brand new shotgun.

1) While you’re trying to figure out what to do next, your competitors are already building it.

2) By failing to define where you are going, you’re taking shortcuts and building the foundations for a dinky niche SaaS app versus a full-fledged enterprise application. The way I like to explain this to non-technical folk is that if you’re designing to MVP, you’re building the foundation for a two-story house and that means all you can ever build on that foundation is a two-story house. When you’re thinking three years ahead, you’re building the foundation for a multi-story apartment complex, building the first floor, and just pausing before you build the second floor. (And so on.)

In the first case, once you figure out what comes next, you realize you don’t have the right architecture or infrastructure, and then have to stop and rebuild the core, slowing down your advancement and future releases even more unless you can miraculously define the minimal API to the core you will be rebuilding up front, simultaneously build the new features perfectly to that API while trying to re-architect the core, and somehow fully achieve that API and don’t have to change it significantly during implementation when you find out it just won’t support the required workflow or orchestration … which it inevitably won’t, and then you need to update the API, and then this necessitates a rewrite of the business logic layer (and even UX) on the fly, which not only results in wasted time but wasted development because you tried building multiple levels of a house of cards all at once. A few extra months of research and planning up front will save you years!

7. Get a couple of beta customers by the time you hit beta on the MVP.
You need to verify all the assumptions YOU made in the design and implementation with a real customer (that wasn’t one of the companies you came from), test the usability, and see how real Procurement departments work (that weren’t the one or two you had experience with). You might find you have a lot more work to do before release than you thought, but it’s better, and easier, to do this before you sell it to enterprise customers as a ready-to-use enterprise product than after!

In other words, it’s not just designing an MVP on a napkin, vibe coding your way to implementation, giving a flashy demo, and delivering on a major cloud platform. (Which is what a lot of startups are doing, and that’s why so many SUCK.) It’s deep thought from day one over months and months, if not a year or two (if you are trying to do something significantly complex). But then it’s a real solution that will be relevant for years (and years) if done right (and continuously improved, appropriately maintained, and always priced appropriately).

And yes, you can argue that more steps, or at least a deeper refinement of the above steps, are needed, but these are the absolutely critical steps and many of the ones that often skipped — which results in poor solutions and sometimes complete startup failure!