Category Archives: Technology

A Good Negotiation is Key in Technology Acquisition

But whatever you do, please don’t mistake cost savings with value generation. But, as usual, let’s backup.

A recent article over on The Financial Express on the importance of a technology procurement negotiator noted that the art of negotiation has taken on a whole new level of complexity, especially in technology procurement and that discovering the most equitable prices … is a strategic imperative at a time when maximizing returns on investments is paramount.

And this is certainly true, as are most of the other messages in their article. Specifically, such a negotiator must:

  • understand the digital disruption
  • have high intelligence, which must go beyond technical expertise
  • understand the high stakes of technology investments
  • have the personality, worldview, and knowledge to navigate the negotiation beyond the technical aspects
  • be able to reflect on the bigger picture
  • be able to sync with the project

… but the criticality of ensuring that the technology procured provides exceptional value for the money spent cannot be over-emphasized. One cannot understate the importance of understanding the product’s role, functionality, and how it aligns with organisational goals. It doesn’t matter how much you save if the product isn’t the right fit. It’s critically important to not only have the technology experts identify the products that could serve your needs, but the right configurations, the associated services that will be required, and the right partners for the organization.

Additional savings is worthless if it comes at the expense of the vendor removing a key module from the reduced offer, not including necessary implementation or integration services, limiting computing or storage, and so on. If you end up paying significantly more after implementation as a result of change-orders, you not only haven’t saved, but you’ve cost the organization more. This is what often gets missed when negotiators lead. While the eventual owners shouldn’t lead, as they’ll always go with their top ranked provider (even if three systems can do the job equally well, and it’s just a preference as to which system is easiest to use), if they’re not kept in lock stop, it’s easy to miss key details or requirements or stray away from what is truly needed for value generation and ROI in the search for the ultimate deal. This is especially true if the negotiator brings a new vendor in at the last minute for price pressure, believing the new vendor, if not perfect, meets all the key requirements, when in reality the vendor’s platform doesn’t.

This is especially important to remember in SaaS negotiations, where it’s common knowledge that most organizations that buy without using a skilled negotiator are overpaying by an average of 30% or more. This is because an average negotiator’s inclination is to drive for massive discounts to prevent overspend, which might result in not only choosing the less optimal vendor, but the less optimal agreement. At the end of the day, price matters, but ROI matters more, especially in Procurement where the right solution will generate a 5X ROI or more and the wrong solution will barely pay for itself.

9% of Companies Claim To Be Ready to Managed Risks Posed by AI? Bull Crap.

the doctor could not believe the recent headline in Forbes that said Only 9% of surveyed companies are ready to manage risks posed by AI. Because there is no way that 9% of companies are ready to manage the risks posed by AI. There’s no way even 0.9% of companies are ready to manage the risks posed by AI.

Why? Because of the rampant introduction of massive LLMs and DNNs that no one understands, for which I’m sure we’ve yet to seen the last of the abysmal failures, hallucinations, and suicide coaxing. There’s simply no way we can even begin to predict all of the potential errors they are going to make, the risks they are putting us under, the repercussions if those errors are made and risks materialize, and how the risks can be minimized, if not mitigated. No way whatsoever.

Not only is it theoretically impossible to be fully prepared, but when you consider that the average organization is not even equipped to handle regular software failures, how can the average organization expect to handle a software-based AI failure it can’t even predict?

The article, which quoted a recent study by RisKonnect (who are obviously able to detect and protect against most types of risk by using RisKonnect, and maybe that’s why they are so confident they can protect and defend against AI risks, but RisKonnect is for traditional enterprise and third-party risk, not cyber risk, and definitely not AI risk — no one can protect against a risk when they don’t even know what the risk is), did quote some very useful statistics on areas of concern. Specifically, of the companies surveyed

  • 65% are concerned about data and cyber,
  • 60% are worried about employees making decisions on erroneous information,
  • 55% are worried about employee misuse and ethical risk,
  • 34% are worried about copyright and intellectual property, and
  • 17% are worried about discrimination risk.

The risks are the right risks, and the order of priority is about the right order, but the percentage of companies concerned is much too low.

1. 100% of companies should be concerned about data and cyber. Not only are we in the age of state-sponsored hacking, which makes any company with useful confidential designs and information a target, but with almost all significant commerce being conducted online, all companies are a target for financial fraud.

2. 100% of companies that need to make decisions based on data analysis should be concerned about erroneous information, as all companies have bad data, and the bigger the company, the worse the data.

But none of these match the risks of AI. As per the quote in the article from Caitlin Begg, an over-reliance on AI can risk robotic, insensitive, spammy, or off-topic messaging, and that’s just the beginning. As noted, most companies haven’t simulated their worst case scenario, and since one can’t even predict what that is with AI, they aren’t even close to ready. It’s not just another article in the organization’s tech stack, even though the article seemed to indicate it is. One can prioritize transparency, accountability, threat and vulnerability monitoring, and risk mitigation, but when most AI applications can’t explain their actions, aren’t accountable humans, have no realistic threat and risk assessments, and there is no way to mitigate risk except not to use the technology in the first place for any decision that should be made by a HUMAN, it’s just not enough.

The precautionary steps are not to identify where AI can be most effective and incorporate it, the steps should be to

  1. identify where partners and third parties are using AI and putting your organization at risk
  2. identify where employees might be using unapproved web-based AI applications and put a stop to it
  3. identify where your SaaS providers are not only using, but introducing, AI into their applications after purchase and delivery and ensure that any utilization is bounded, tested, and properly constrained to prevent risk

Then, instead of unbounded AI, identify appropriate automation technologies that can be properly configured, integrated, and managed as part of an enterprise stack. And reap the rewards while your competitors deal with risks.

Do you want to get analytics and AI right? Don’t hire a F6ckW@d from a Big X!

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.)

Now, I’m going to upset a lot of people with this, but I don’t care because the linked article below is literally the best article I ever read on why you should NOT hire F6ckW@ds from Big X (or any other) Consulting Firms who claim to be analytics and AI experts when they don’t actually know

  • the difference between a mathematical formula to calculate the center of gravity of a falling object and to calculate the median spend in a category
  • proper software architecture
  • proper compute resource allocation
  • your business
  • the difference between real ML technology, RPA and a few formulas, and the current Gen-“AI” where the “AI” stands for artificial idiocy

because

  • you’ll spend 3 years and millions of dollars to implement something that should take 3 to 6 months
  • you’ll spend hundreds of thousands on big vendor software licenses you don’t need
  • you’ll spend hundreds of thousands on compute power you don’t need

After all, these guys and gals get paid by the hour and the commission on the resell license is a percentage of the total price they convince you to pay for it. So, the longer the project takes and the more licenses and compute power they sell …

Read the linked article. Twice. And then tape it up to your fridge. The situation described in the article is NOT the exception. As a former CTO and 25 year consultant/analyst, I know this is the norm!


I Accidentally Saved Half A Million Dollars
 

Now, if you’re wondering how to tell who is a F6ckW@d and who’s not when it comes to analytics and AI at the Big X, I’m sorry to say that it’s not so easy (especially when it only takes a few bad apples to spoil the bunch, and while the good firms will do mandatory pruning of the consulting tree annually to weed those bad apples out, you don’t want to be the unlucky client who gets one on your project) .

It used to be if they were there for more than a year or two, their was a possibility that they were, or at least not as good as they claimed to be,  that especially if they were junior, right out off school, no real experience. This was because, first of all, tech talent wants to go either to the big glorious tech firms (Alphabet, Meta, etc.) or the wild-west startup frontier, and big consultancies were the backup until they got enough talent to move on.

Thus, the real talent in tech and analytics, who didn’t get promoted quickly in the Big X, usually didn’t stay long before they moved on to specialist firms where they felt they were more respected, higher up, could control the projects, and, more importantly, being higher up, were higher paid.

(Tech/Analytics people take pride in their work [and not their title], and seek the job that gives them the most pride.  Also, even though good tech/analytics people won’t contradict managers because they want to be important, and will only contradict managers because they want the job done right, the reality is that junior people or new hires in big firms often have the impression that this is discouraged in a larger firm [even if it’s not] where you are supposed to learn from and follow your manager’s lead because you don’t see the big picture and may not speak up on the way a project is being approached when they are unsure.  They might be wrong, and should stay quiet, but they don’t learn if they don’t ask.)

However, now that all the big firms are acquiring mid-market experts, with some of the Big X acquiring 3 or 4 specialist plays in analytics and AI over the past couple of years, it’s much harder to differentiate if you are getting the best talent or not.  You have to vet every candidate.  Not the Big X.  YOU!

And you need to remember that some of this AI and analytics stuff is literally so complicated that you need degrees in mathematics and computer science and sometimes a decade of experience to get it right! (It took the doctor two advanced degrees and building advanced analytics and optimization systems for multiple leading companies in the 2000s before he really understood the art of the possible and, more importantly, what was relevant for an industry and what was not.)

In other words, it’s okay if you don’t really get it as a manager. Just find those one or two people who do who you can trust, pay them well, and let them do what they need to make your department look good (be it hire internally, choose a consulting firm you never heard of, hire former colleagues on short-term contracts, use their contacts to get the right person at the Big X, etc.).

They’ll get the job done right and be quite happy to let you take all the credit IF you give them regular raises and a bonus any time they do particularly well. Just put your ego aside and let the people who get it make the tech/analytics decisions, and everyone will win!

But, whatever you do, don’t throw a poorly formed project description over the wall in advanced analytics and AI to a Big X (or any other vendor) and expect good results.

If you don’t know what you need, why, and how you expect to get it, instead focus on what you understand and Use the Big X firm for all of the things you know it is good at, understands implicitly, and has the history and experience to figure out simply based on the type of company you are.   Used appropriately, like any service provider, a Big X can deliver amazing value.   See the linked article on when you should use Big X in our opinion.

Fail Fast And Forward? How About Not Failing At All?

A recent article over on The Sourcing Journal indicated that one should Fail Fast and Fail Forward When Implementing AI into Workflows. WTF? Why fail at all? Especially since if you’re using AI where you are expecting a high risk of failure, there’s no reason to expect that you’ll only fail once, or that you can actually fail forward.

Now, if we were talking traditional ML, where it’s just a matter of continually expanding and refining the model and training data, tweaking the parameters, and starting small, then fail fast, fail forward, get it working, use the spice weasel, knock it up another notch, and continue until you have automation across the platform in appropriate places, it would be good advice.

But when we are talking full fledged Gen-AI (which is the article’s focus) based on massively large and entirely unpredictable LLMs or super-sized DNNs, you can fail fast, but, with absolutely no way to control the models, you can’t fail forward. So while fail fast and fail forward is a good motto in general for technology, process digitization, and automation, as long as you take things step by step and control the risk, it’s not appropriate at all when we are talking about AI!

Automation is Good Across the Board! But Automation still does NOT mean Automated.

Not that long ago, we penned Procurement Automation: Good. Automated Procurement: Bad because organizations that embrace the right digital technology do much better than their peers, but organizations that go all in and put too much trust in unproven technology without human oversight (while trying to run before they’ve learned how to walk) or good data (and then make worse decisions than having no technology at all, as recently determined by Gartner) are making a huge gamble while forgetting that it is the house who always wins. (And in this case the house is the technology provider that is charging you a lot of money for the technology that eventually fails and costs you time, money, and in the worst case, your job and/or business. But we digress.)

And while this blog is a Sourcing, Procurement, and related Supply Chain Technology blog, it was very happy to see a recent release from the Hackett Group, as advertised in a recent press release on yahoo! Finance / BusinessWire, that noted that while HR (and Humans are VERY important to successful Procurement Operations) operating costs increased significantly in 2023, Digital World Class organizations continued to spend significantly less than their peers while delivering more resiliency, employee productivity, and greater business value with less staff than their peers. The Hackett Group concluded that increased spend on technology plays a key part in driving the superior performance.

Other key metrics that Hackett pointed out is that companies with at least one business services function operating at Digital World Class levels see a five-year average performance premium over their industry medians -– an 80% improvement in net margin; 24% higher earnings before interest, taxes, depreciation and amortization; 89% greater return on equity; and 44% higher total shareholder return. (So imagine how good your organization would be doing if you were world class in Procurement and HR, and ensured that your organization always acquired, trained, retained, and promoted the best of the best.)

Hackett found that a key aspect of Digital World Class Organizations in HR, just like Procurement, was a greater use of technology (to the tune of 60% more likely to have and use the full capability of Human Capital Management applications).

There are a lot of great applications that a leading HR organization can employ that go beyond the specific applications mentioned of:

  • Human Capital Management
  • Time Sheet Management (for hourly employees / contractors)
  • Health (& Welfare) management

and, as Hackett points out, include the use of emerging technologies such as:

  • smart automation (not automated Gen AI applications)
  • advanced analytics
  • collaborative tools

For example, a good HR department will employ platforms that:

  • will use smart automation to onboard employees, ensure they get paid on a regular basis, ensure that their expense claims are properly routed and evaluated on a timely basis (and OCR use to reduce receipt processing), ensure that all information they enter on health/disability/etc. claims is auto-routed to the right third party systems (and not lost/transcribed wrong), etc.
  • will use advanced analytics to analyze its highest contractor/third party costs, determine what functions should maybe be brought (more) in-house, analyze it’s biggest employee benefit plan costs, optimize those costs (without reducing benefits), etc.
  • use collaborative tools for onboarding, training, and continued professional development, especially for remote learning and self-study

Just like a good Procurement department will employ platforms that

  • use smart automation to onboard suppliers, automatically distribute and collect RFPs, verify data that can be verified by a third party, do automated sanity checks, do initial analysis for presentation to a HUMAN, automatically generate POs from carts/contract schedules, automatically match, to the extent possible, invoices to POs, etc.
  • use advanced analytics to identify not only the greatest costs but the greatest opportunities available to the organization based on PPV (purchase price variance), market opportunities, consolidation, demand management, substitution, etc.
  • use collaborative tools to involve all stakeholders and make sure processes are automated to the extent possible

Because modern technology is far superior for tactical processing (thunking) than we are as humans. However, the leaders understand machines, while they can augment our intelligence with finely tuned applications, cannot think and leave the final decisions to the humans. Technology is applied appropriately for maximum success.

As Hackett says, the bottom line is that Digital World Class HR organizations are better at enabling their companies to succeed. They have streamlined the day-to-day transactional elements of their operations, and through systematic use of global business services and process automation have freed up an additional 12% of their teams’ efforts to focus on value-added activities. Now, they can more effectively focus on attracting, retaining, developing and engaging employees. The right digitalization helps people, and that’s why the right digitalization helps Procurement.