Advanced Sourcing Tomorrow — No Gen-AI Needed!

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

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

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

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

TOMORROW

Automatic Strategic Sourcing Events

Just like tomorrow’s Procurement platforms will automatically identify products/services and (sub) categories that should be pulled out of the tail and inventory/catalog/one-time req buying and pulled into a strategic sourcing event, tomorrow’s sourcing platforms will create automatic events from them. Furthermore, tomorrow’s sourcing platforms will automatically create the entire event using the default category strategy (possibly adjusted to the current market conditions, see the next forthcoming capability), automatically pull in the (organizationally approved) suppliers, automatically pull in any questionnaires or documents that need to be completed by the bidders, automatically pull in supplier profile information and current prices (where available), and, if you set the flag for “no review prior to event initiation”, automatically send out the RFX, which could be the first in a series of RFXs/e-Auctions in a multi-round event. If the event is multi-round, after each round it can analyze the responses and any supplier who provides all of the necessary information (and makes the cut price/quality/risk/carbon/etc. cut) makes the next round. It will auto-execute the next round and keep going until the event has been completed and an award recommendation is made. Then, depending on the setting (auto-award, human review), it will either compute a recommended award and notify a buyer to approve, modify, or reject the award, or automatically send the award to to the suppliers for acceptance (with a contract for high-value or strategic products/services or a PO for lower value, more tactical offerings).

From a tech perspective, all this needs is the ability to analyze spend patterns and demand trends (trend analysis) to identify categories ripe for sourcing, product classifications to match to the category strategy, and product-supplier pairings to pull in the suppliers (and associated data), with current and preferred suppliers getting priority if there are too many. The rest is just workflow automation until the initial responses are returned. Then, it’s just analyzing the data with respect to expectations and tolerances, and either recommending an award based on the strategy, organizational priorities, and organizational constraints, or sending out the next round requests (deeper RFIs, price updates, etc.) to those suppliers who provided complete, satisfactory, answers according to business rules. This is just analytics, optimization, and good ol’ math coded with human intelligence (HI!).

Market-Based Sourcing Strategy Identification

Today, the best platforms support category-based sourcing strategy identification where the platform can identify the standard, best-practice, strategy based on the category and items, determine whether or not the strategy is likely to be relevant given available market data (supply availability, historical price variants, current market prices, etc.), and make a go-no recommendation to the buyer. Tomorrow, these platforms will be able to first analyze all of the market information, supplier information, product information, carbon information, risk information, and compare that to current company performance an demand and identify the right sourcing strategy for the event, making sure to dynamically align the category (which can include adding or dropping items and services) as required.

From a tech perspective, all this needs is access to extensive market data feeds, a large history of sourcing event and results with associated market data (relative to the supply vs. demand imbalance, price trends, demand trends, major risk factors, etc.), pattern analysis that correlates successful events (with results < market price) with market conditions (supply > demand, prices steady or falling, low market risk in the supply base –> e-Auction; supply >= demand, prices rising with inflation, low to moderate risk –> RFX; supply projected <= demand, prices rising above inflation, moderate risk –> renegotiate with the incumbent(s) before the contracts expire), pattern analysis of the current market conditions compared to historical patterns of success, and the selection of the best match. All trend analysis, correlation/(k-)means analysis, tolerances, and, you guessed it, math! Then you just kick off the category-attuned sourcing event as above.

Real-Time Strategy Alignment in (Automatic) Strategic Sourcing Events

However, tomorrow’s AI-based sourcing capabilities won’t stop there. The platform will monitor all relevant market (related) conditions as the event progresses, compare all of the responses to those that were predicted/expected, and if, at any point during the (automatic) event something is too far off, it will automatically pause the event and either, depending on system configuration, alert the buyer that a shift in strategy is required (and what the new strategy it should be) or simply shift the event as appropriate (if possible; in the public sector, not always possible, but in the private sector, usually possible).

From a tech perspective, all this needs is trend and outlier analysis, pattern matching, and, you guessed it, math.

SKU Recommendation and Replacement

Tomorrow’s platforms will get better at identifying replacement SKUs not just in indirect (paper with similar thickness, weight, and gloss when the differences are inconsequential from a business point of view), but direct as well (compatible processors, with the same form factor, number of connections, compatible clock rate, and sufficient L1 cache). This is difficult because you need a lot of specification data, and most applications need it appropriately structured in a format no other application supports in order to process it. But, despite the focus on the Gen-AI bullcr@p, semantic processing is continuing to advance and as more and more validated database are built on each product and service type, and more specifications are added to each product and service type. As a result, these applications are getting better and better at helping to identify acceptable alternates with slightly different, but compatible, specs that can help Procurement and engineers find more cost-effective alternatives, including new tech that will have a longer shelf life.

As this tech continues to improve, it will be able to not just look at SKUs, but subassemblies, such as processor-controller board-memory combinations, that can be switched out to provide more cost effective alternatives with better reliability, risk span, or quality. This will be the result of not only a better understanding of each subcomponent, but the interaction requirements and overall processing power capable of handling the combinatorial explosion needed to automatically identify new potential subsystems, and not just components, automatically.

EOL Recommendation

Many niche PLM systems will already do this, but tomorrow’s sourcing systems will do this not just from a traditional “tech curve” perspective, but also from a Procurement and Supply Chain perspective, balancing life-span with price trends, material supply, market risk, and carbon impact. If a current product requires a large concentration of a rare earth mineral or metal (in short supply) or an ingredient that can only be grown in a few places in the world, and a new product comes along that requires less (or none) but still provides the same use (or at least a suitable alternative for consumption in the latter case), then it makes sense to switch over as soon as the cost is appropriate. Similarly, if one product is only available from a risky supplier or a risky country (with rising political or market instability) or has an unnecessarily high carbon cost, switching out could also be a priority.

Using trend analysis on demand and (future) cost, risk projections, and carbon costs, tomorrow’s sourcing systems will find the optimal inflection points (using analytics and optimization) for switch over and make early end-of-life recommendations so Procurement and Engineering can plan early for the switch-over and schedule the appropriate sourcing events for the appropriate timeframes (and ensure contract lengths are optimal). And, again, no Gen-AI needed!

SUMMARY

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

Digital Procurement Transformation Requires Strategy and Design …

… not just new technology! (As THE REVELATOR would say, an agent-first approach, not an equation-first approach.)

A recent article on Turner and Townsend noted that while effective digital-first procurement strategies are key to capturing the necessary data and providing the comprehensive visibility needed to manage complex and multi-faceted risks, a digital-first procurement strategy demands a strategic overhaul – it cannot only be about adding technology to existing processes.

Furthermore, it needs more than a cultural shift to integrate a digital golden thread that aligns the organization’s overarching commercial vision and the enterprise-wide digital ecosystem. It needs a technological shift, one that goes from looking at technology as a saviour to technology as what it always was, just a tool, and a tool that only works if

  • properly selected,
  • properly used, and
  • placed in the hands of an appropriately educated, trained, and skilled individual.

Furthermore, it doesn’t matter how modern the tool, how much “AI” inside, or what provider is offering it. Just like a power drill won’t screw in a nail, a Gen-AI solution won’t provide a strategy, won’t analyze generic data in a meaningful way to select a sourcing strategy, and won’t properly parse and automate that invoice. (That’s not what it’s for. It will summarize large supplier RFP submissions and crawl through your contracts for common clauses, or lack thereof, but that’s it … it’s just a huge document parser and summarizer.)

Only the right platform will solve your problems, and you’ll only be able to select one if

  • you analyze your processes and identify the data you need
  • you analyze where the data comes from
  • you analyze who has to create / enter any data that needs to be manually vetted …
  • you determine the TQ level of all those individuals who need to use the system
  • you analyze the potential systems with respect to their ability to store the data you need, collect it automatically from any data feeds it is available in, and collect it through manual submission in easy-to-use interfaces that minimizes the chance of error on data entry
  • and when you find ones that meet the data need, then you confirm they can support the process needs …
  • and then you do vendor diligence.

But without the right platform, no progress will be made and, in fact, if you consider the failure stats, chances are the wrong platform will worsen the situation. Technology is NOT an easy button. You still have to do the work of vetting it, implementing it, configuring it, and even when it can automate a task, verifying it on a regular basis (as well as identifying when an exceptional condition arises and dealing with that regularly). Technology can make your life (much) more efficient, and easier, but it’s not an easy button. Never forget that.

Always Start Your Vendor Qualification with a Deep-Dive Demo!

In a recent article, THE REVELATOR asked how many practitioners do a pre-demo discovery call to determine whether seeing a demo is even warranted??

It was a fair question, but for most practitioners, the question is unnecessary because,

  • if you agreed to the demo as a practitioner, then you should have confirmed from the initial sales call that there was enough to actually see (by listening to a rep that sold a solution, not software, and that answered your tough questions);
  • the demo will tell you if it’s worth diving into the vendor’s background, philosophy, and services approach; and, most importantly,
  • if you’re not a senior executive at a large enough company, there’s no way you’re going to get the attention of the right people for that discovery call. (As a [perceived] unqualified lead, you’re not getting a senior person on that pre-demo interview … just a Sales VP who knows what to say to hook you, whether it’s true or not!)

The reality is that any discovery beyond an initial demo to confirm the vendor actually has a solution and, more importantly, a solution that might actually help you by solving some of your problems, is meaningless. Company history, philosophy, and go-forward don’t matter if they don’t have anything worth working with them.

It’s important to remember that technology cannot overcome a solution provider’s misaligned business values and goals. If the tech is wrong (or just not there), the tech is wrong. Not only do you need real tech (and not vapourware), but you need tech that solves one or more problems you have.

As such, if you dig in on a company before seeing the tech, you could be wasting your time. Especially if you do it for every provider given that you will likely go through half a dozen potential providers before you even find one worth to include in your RFP (when you consider all the overhyped marketing and misleading marketing you need to work your way through).

Moreover, forcing a demo early will quickly cause some vendors (without a solution) to self deselect! If you insist on a demo that shows how they solve the problems they claim and how it’s relevant to you, and they don’t have a deep solution and/or knowledge of your industry, they will likely decide it’s not worth the time trying to bluff you and save you the time and effort of invalidating them as a potential provider. (And, in effect, bypassing the technology-led equation-based providers off the cuff, since they won’t even get the demo if they can’t convince you they are about solving problems first and tech second.)

However, if you get through the demo, put the vendor on your shortlist, and tell them that, you can be sure your follow-up company deep dive call will include the right senior people at the vendor, and not just a say-what-you-want-to-hear Sales VP.

So You Admit You Might Be a Dead-Company Walking. How Do You Avoid the Graveyard? Part 4

In short, as per Part 1, you

  1. keep admitting to every mistake you are making and do something about it, then
  2. continue by looking for cost-effective opportunities for improvement and pursue them and finally
  3. never, ever, ever forget the timeless basics.

Today, we’ll continue by describing what you do when you identify, and admit to, one of the next two mistakes (mistakes 5 & 6) we chronicled in our two part introduction to our “dead company walking” (Part 1 and Part 2) series (where we helped your potential customers identify problems that signify you are a SaaS supplier they should be walking away from). (You can find part 2 and part 3 here.)

5) An innovation burst is enough, especially if it is disruptive

A successful innovation burst is great as it can get you noticed, and as it’s very hard to get noticed in an overcrowded space (again, see the Mega Map), that’s a great start. But someone noticing you does not mean they’ll engage with you, and engagement does not mean they will buy from you.

Moreover, it’s never long before a one-trick pony loses the limelight as fast as he enters it. If you want to stay in the limelight, which wants to move on to the next story as soon as you’ve had your 15 seconds of fame, you need to keep innovating, or at least developing the core functionality necessary to flesh out the value message to the point the overall message is so compelling people want to hear it and repeat it.

The reality is that it is continued development, especially around core processes your technology is being designed to support, that will at least keep you on the periphery of the limelight, and that is where you need to be to not only attract enough potential customers, but convert them into customers who want, as we’ve been stating repeatedly, solid solutions to their problem and not shiny tech that looks cool but doesn’t do what they need it to do.

6) Too much investment, too soon, against an overly ambitious plan

This is one of the biggest threats to your success, especially since you will be pushed to scale up fast to support the rapid growth, that won’t come, or at least not early in your corporate development. Falling for this will burn the cash well before you are even close to break-even, and if you can’t raise additional funds fast when you run out, you’re dead.

The first thing to do when you raise too much money is to stop dead in your tracks, stop all external hiring and engagement, and step back and do the detailed market research described in our first mistake and figure out the MVP you’ll actually need to build a significant market share, and focus first on hiring the talent / or acquiring the third party tech, to get there as soon as possible.

Then you need to figure out what not only makes a good customer, but one that is easy to sell. It’s likely that the majority of these customers will need education to get them there. The next thing to do is hire the product people who can build these educational assets for marketing, sales, partners, and customers.

When you get close on the product and the marketing, then you start to ramp up marketing and high-performing sales (who can work without a lot of support and incomplete, but progressing monthly, assets) to start building the initial funnel when you are ready to go hard.

Then start building up your services teams with senior resources who can do multiple roles and initial implementations with little support.

And only once all the pieces start falling into place do you start scaling up.

And in the process, be sure to:

  • review the marketing plan: cut the funding to anything not focussed on education and thought leadership in the early days
  • review sales: cut the “leads” to those truly qualified with problems that match your solution; and definitely cut the spray and forget power washer lead blasting from 3rd parties, you want well qualified leads only
  • review the development plan: make sure it’s 90% steak and only 10% sizzle; sizzle doesn’t solve problems, or fill bellies, and that’s why customers want steak
  • review the budget: anything not going to educational/thought leadership marketing, qualified solution-based lead generation, or solid development is extraneous and needs to be cut ASAP to ensure the money lasts until the solution is broad and deep enough to serve the intended market, command the expected price tag, and get the interest you need for steady, continued, growth

Stay tuned for Part 5!