Gen-AI is Bad for Consulting Firms … But Even Worse For You When the Consulting Firms Blindly Use It!

A recent post on LinkedIn noted how there’s a wave of AI products flooding the consultancy and advisory space and how they are, frankly mediocre, overpriced wrappers on public models with minimum innovation, if any.

This is sad, but true, and it’s not the worst of it. The worst of it is that some of the Big X firms are training tens of thousands of consultants and f6ckw@ds on these tools to generate hundred page pitch decks and three hundred page strategy and implementation guides of standard generic, meaningless, drivel to deliver to you as “highly tailored guidance and expertise from their leading partners with 20 years experience delivering high-value projects” and charge you tens of thousands of dollars for the privilege.

This is especially egregious when you can use free/cheap (and I’m talking put it on your personal credit card cheap because you won’t notice the fee that is less than your monthly coffee charge from the coffee shop) to build the exact same pitches, strategy, and implementation guides from the thousands of freely available documents on the web in a few hours with a few generic prompts over a Sunday morning coffee. (And then, when the coffee kicks in, realize it’s all a load of cr@p and put in the bit bucket, but at least you will know what a load of cr@p looks like in pitch deck, strategy guide, and implementation plan form and will recognize it the next time an overpriced Big X tries to sell it to you for a ridiculous price tag and will have learned something from the exercise.)

Now that there are companies selling overpriced “custom” products to these consultancies, the situation is only getting worse, especially when the “customization” is just a wrapper with some pre-engineered prompts that aren’t well tested, only work at a point in time, don’t really give the consultancies what they need, and sometimes translate mediocre inputs to inputs that are even worse. Moreover, when you consider the price is sometimes a 100X multiple on the products they build on top of, it’s disgusting. Consultancies are paying more for less, and, in return, you are paying even more for even less!

Which makes no sense when the current publicly available LLM tech is being offered cheap (to try and hook you on it, even though, as we’ve repeatedly explained, the tech is not ready for prime time and will never deliver more than a fraction of what they are promising), and new implementations will get a lot cheaper. Just look at how DeepSeek undercuts the cost by a factor of 100 and gets 90% of ChatGPT (as long as you don’t mind exposing all of your secrets to the CCP). LLMs are nothing more than a fancy next-gen “deep learning” Neural Networks that construct responses vs. serving up canned responses (which is why hallucinations and lies are a core function, not an error that can be trained out) which gets us closer (but no cigar) to decent natural language processing (NLP) for the express purpose of the generation of desired outputs from inputs, but not there (and now, in addition to all the false positives and false negatives, we had to deal with, we now get to deal with hallucinations and lies as well). It’s not secret magic, it’s layers and layers of interconnected statistics and probabilities that no human can understand, in rather standard models that any Theoretical CS and Applied Math PhDs can build, and implementations that are better and cheaper are going to keep appearing as time goes on.

This means three things to any consultancy thinking about using these custom “AI” solutions

  • you still have to be even more tech savvy to use them to any degree of effectiveness
  • it’s not “the art of the prompt“, it’s the art of the training (even though they don’t really learn because they are NOT intelligent) because that determines the maximum level of effectiveness you will ever reach with them (and you need to provide them with sufficient correct data, which needs to be in the high gigabytes at a minimum, and, preferably, in the petabytes)
  • you don’t have to worry about when they are right (enough), which will happen between 90% and 95% of the time with proper training and proper prompting, or when they are obviously wrong, which will happen a very low percentage of the time (say 5% to 9%), but when they are oh so wrong but the response is constructed in a way that is oh so convincing that an above average person in intellect and experience wouldn’t know otherwise (that danger zone between obviously wrong and good enough that is likely only 1% to 2% of the time).

Now remember that your consultants aren’t that tech savvy, and you should know right off the bat incorporating and using these is going to be difficult and time consuming. (There’s a reason we are constantly advising you to be very careful about using Big X for tech selection and tech projects, and that’s because, even though they say it is, it’s NOT their forte. They weren’t built on tech, and they don’t have the best talent in tech — that talent goes to the big tech companies who can offer the 500K salaries to leading devs or the wild-west startups that leading devs think are cool.)

You only have so much clean and complete data you can use for training. You can’t just throw in the 1000s of decks you’ve built as you can’t share work you’ve explicitly created and sold to past clients, and the AI won’t anonymize the decks and suggestions (even though you think it will). It won’t know that “Ford” is the name of your client and might think that “Ford Data” is another term for shallow data and copy sections from that custom strategy straight into your pitch deck for General Motors (and chances are your overworked junior consultant won’t catch it when skimming that 200 page deck with only 2 hours to go before the meeting). And we know what happens then … (and it ends with the consultancy not keeping either client).

It will take a lot of analysis to identify those 1% to 2% of cases where it is very, very wrong but so convincingly right that you will miss some. What happens when you do and give your client advice that explodes in their faces? (We’ll let you answer that one.)

And for you as a consumer, if your consultancy is using this Bogus AI tech, it means that:

  • the situation that results from solution delivered might be even worse than the situation you started with (as should be evidenced not just by the tech project failure rate that is approaching 92% but the fact that 42% of projects are being abandoned during implementation!)

A solution designed by Gen-AI is not a solution. A real solution is a solution designed by human intelligence that uses real, augmented intelligence, to research and validate that solution. Remember that if you are going to hire a consultant!

Moving Forward to Supply Chain Aware Direct Sourcing Is Not an Easy Task

As Bob and I point out in our joint series on Supply Chain Matters on why legacy sourcing and (supply chain) planning solutions can’t handle today’s supply chain challenges (and why direct sourcing needs to be supply chain aware), moving forward is not an easy task and consists of three main parts:

  • persuasion
  • platform
  • process

and each of these parts presents its own set of unique challenges.

The Persuasion

The technical challenges that will need to be solved will be difficult, but likely won’t be anything that talented engineers can’t solve if they put their minds to it (and avoid the distraction of shiny new technology and the buzzword filled marketing that surrounds it).

But, as we keep saying, it can never be “technology” first.

Moreover, the people challenge — convincing the source-to-supply-to-service professionals, stakeholders, and sponsors to buy in is the harder challenge. Moreover, unless you succeed in this challenge, you won’t make any progress at all.

There’s quite a few reasons for this, as well as requirements for success, and we strongly recommend you dive deep into Part 6 of our series for a discussion.

The Platform

There are a number of considerations with regards to getting the “platform” right, and great engineers will be able to do it, if they realize all that they need to do.

Let’s give them a few tips by starting at the foundations.

They will need to address, and resolve, the:

  • data fabric
  • federation
  • integration
  • orchestration
  • alignment
  • the multi-platform metaprise requirements
  • purpose

There’s a lot to unpack here. For a start, we strongly recommend you dive deep into Part 6 of our series for a discussion.

The Process

The process needs to be much more involved than traditional software / SaaS / app selection. It is not just sending out an RFP to a vendor, evaluating the responses, picking one, and then having them and/or your favourite implementation partner implement it.

That’s because you need to do proper Design Engineering and define an ecosystem solution that fits your end-to-end processes, which not only differs between manufacturing and distribution centric organizations, but also across industries (which are subject to different regulatory constraints and operate differently) and even geographies (for enterprises that are regional vs. global).

We offer a few more tidbits on this requirement in Part 6 of our series, but this may just be the process of a future series as we have written multiple series on the past on enterprise technology selection alone, and this goes beyond that as you can’t assume all the vendors did appropriate Design Engineering that considered anything beyond the platform they were selling, if they even considered Design Engineering when they designed their platform.

The short story here is that moving forward is more than just a decision to “make it so”, but a complex endeavour that will take time, money, and top talent. But it is necessary to survive the future of anti-globalization when supply chains are fundamentally global and insanely complex to the point where they cannot be unravelled in some industries in anything short of a decade!

It’s Not A Supply Chain Anymore, It’s a Supply Ecosystem!

As Bob clearly points out in our joint series on Supply Chain Matters on why legacy sourcing and (supply chain) planning solutions can’t handle today’s supply chain challenges (and why direct sourcing needs to be supply chain aware), Supply Chains Have Evolved to Demand and Supply Ecosystem Networks.

They are not a simple linear chain anymore. They aren’t even a simple tree where a supplier uses sub-tier suppliers for parts that uses sub-tier suppliers for components that uses sub-tier suppliers for raw materials. A tier 1 supplier could be using a tier 2 supplier that is used by other tier 2 suppliers and a tier 3 supplier could be using a tier 1 supplier for products for manufacturing. Then you have intermediate assemblers and distributors that pull parts, components and materials in, assemble some, and package others in a bundle for resale. Maybe you are selling your product to a tier 3 supplier and don’t even know it. It’s a many-to-many graph. and a very convoluted network that can only be described as a complex supply ecosystem.

It’s a complex network that requires the orchestration of strategic and tactical product sourcing with supply management teams who might be participants in the customer fulfillment process, leaders of the sourcing process, managers of the logistic network, or all three. All depending on what is needed, when, where, and why.

If you only look at one need, from one perspective, at one point in time, you miss the fact that any decision not only impacts every other internal organizational unit in the supply chain ecosystem (which includes sourcing, procurement, logistics, supply chain, R&D, production, and operations), but has network wide impacts, implications, and response.

If you change the supplier for a part during a sourcing process and award a new contract, that affects Procurement since they have to update their catalog and reorder systems; Supply Chain as they need to have the appropriate network in place for cross-docking, temporary warehousing, and storage; logistics as they may need to onboard a new carrier to pick up from the local factory; Production as they have to confirm the part will work across all product lines the part being replaced was used in (and, if not, you may have to retain the current supplier at a much smaller volume or find a replacement); R&D as they will have to confirm the part is okay for all the products they are developing; and operations needs to ensure it is categorized and tracked properly so they understand the data shifts during their analysis and cash flow forecasting.

When you change the supplier, that could have a negative impact on the former incumbent who might have been continuously allocating 40% of their capacity to your business with no quick way to recover that (because they had to cool their pipeline to support you), especially if that part was 75% of your business. (And this could have a negative impact on you if you are relying on them for other parts.) Chances are they’ll have to do layoffs in the short term, and that will impact their OTD and quality on your remaining contracts.

When you change the carrier, they need to reallocate the driver. Probably not a big deal, but if they were subcontracting to a Mom & Pop Trucking Co. for a route they didn’t normally do, that’s a big deal to that Mom & Pop Trucking Co. that needs to find a new regular route fast. And it’s a big deal to you if cancelling that route drops you below the commitment you made (and you lose your discounts and special rates you spent weeks negotiating).

The impact on all of these departments and gears in the supply chain machine have to be considered, especially as those gears turn back towards you. To make the best decision, you truly need to do an integrated analysis across multiple levels of planning (long-term, mid-term, and short-term).

Moreover, this analysis needs to be done in near real-time as businesses need to be able to quickly pivot to changing demand or supply balancing requirements in today’s dynamic global marketplace (brought on by pandemics, border closings, canal closures and slowdowns, sanctions, economic upheavals, wars, and trade wars). This requires an integrated network view across business departments, views, and timeframes.

And, as we have said before, it requires that business finally Think Different, and stop reverting to spreadsheets as a means to attempt to span non-integrated internal and external information streams. But considering that Excel is still every analysts favourite tool and they all want to be King of the Spreadsheets, who knows when the shift to modern, integrated, analytics will finally happen.

Direct Sourcing Should Be Part of Supply Chain Management …

… but it’s not, and the reality is that none of the Supply Chain Management solutions (which are often still in the middle ages of technology) are anywhere close to even considering direct sourcing, so you can guess how close they are to being able to support it.

This means that, in order for us to get to the point where we have a direct sourcing solution that works, integration will be key. Furthermore, the integration must be as native as possible between the direct sourcing and supply chain planning and management solutions, with full, real-time, data exchange in compatible models.

This is because, as pointed out in Part 4 of our direct sourcing series with Bob Ferrari of Supply Chain Matters on how integration is key, without native integration, it’s hard to react and execute effectively when:

  • consumer demand suddenly spikes or flatlines
  • supply lines (or a supplier) suddenly become unavailable
  • costs significantly change over night

Only with direct integration to the supply chain visibility, planning, and execution systems will you:

  • see the spike when it happens, not when it’s too late to even attempt to do anything about it
  • see the delay in a shipment that could indicate a supply chain disruption and see the alternate supply lines or suppliers that could be rapidly be routed to or ordered from
  • see what your alternatives are, what they would cost, and how long it would take to bring them online

… and begin an emergency sourcing process for substitute parts, alternate carriers, or even new suppliers in time to at least prevent part of the forthcoming disruption, and pull all the data in from the relevant systems that you need to start that process.

It’s the only way to create a supply chain aware integrated sourcing strategy, which changes the static old world view of:

  • Identify the core product lines for the next 5 years, create the regional and local supply chain hubs, (blanket) contract the major carriers, and set up the infrastructure
  • Finalize the products for the next 1 to 3 years, do the sourcing events, cut the contracts, define the normal delivery schedules and routes, set up the (auto) reorders, and set the supply chain in motion.
  • Make minor changes based on localized demand variations, localized/temporary disruptions, or unexpected situations, get things back on track, and continue with the production and sourcing plan as normal

This worldview is unable to cope in today’s global economy that is plagued by pandemics, wars, major shipping route interruptions due to severe weather (Panamanian droughts) and rebels/terrorists (Houthis in the Red Sea), border closings and trade wars. While it more-or-less worked fine for the relatively stable global “free” trade of the first two decades of the century (except for when natural disasters occured), it’s clear the model no longer works and needs to be replaced by a better one.

More specifically, an integrated multi-level model that can react and re-run continuously as a result of signal changes to help the organization react and adapt to changes while still keeping long term and mid-term goals in mind with every decision. An ideal model that is spelled out in Part 4 of our direct sourcing series with Bob Ferrari of Supply Chain Matters on how integration is key.