What's in Your ProcureTech's AI?

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Hi readers,

What do severe Lebanese math teachers, AI girlfriends and outdated expressions of delight have to do with getting better outcomes from AI in ProcureTech? 😅

I’ll show you tonight… And give you a question to ask every ProcureTech provider who claims to have embedded AI into their solution…

I’ll also reveal the star studded lineup of the holiday edition of The Road to the ProcureTech Cup🎄

Onwards!

📰 In this week’s edition:

• 📋 5 procurement jobs that caught my eye

• 🏆 The Road to the ProcureTech Cup : Episode 16

• 🌙 My Math Teacher’s Problem with AI…

Tap the poll at the bottom of this email after reading.
It helps me ensure I haven’t turned into a mad scientist yet…

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The Journey Continues…

This week is a special holiday edition of The Road to the ProcureTech Cup.

I’ll be joined by a number of experts in the digital procurement field to discuss:

• Our biggest surprises from first half of the ProcureTech Cup season

• Where you would like to see the ProcureTech go in 2025 and why

• Our 2025 "War Stories" from work in the ProcureTech trenches

It’s sure to be a good time. I hope you’ll join us.

My esteemed guests:

• Nico Bac, Founder of Digital Procurement Now

• Jason Burgess, Principal at Procurement Resources

• James Meads, Founder of ProcurementSoftware.site

• Gabe Perez, Chief Strategy Officer at RiseNow

• Karthik Rama, The Procurement Doctor

Last Week’s Episode

Keelvar is helping businesses drive better sourcing outcomes for strategic, tactical and tail spend with their complete sourcing platform.

If you missed it, I’ve made ALL the replays available for you on YouTube:

What is the ProcureTech Cup? Start Here.

Did You Know…

There’s a Pure Procurement Newsletter YouTube Channel?

Lots of content planned for 2025. Subscribe now.

My Math Teacher’s Problem with AI…

Back in school, I used to get in big trouble when I didn’t “show my work” in math class… Was it the same for you?

Here’s an example:

When I handed that problem in, my teacher’s scowl was so severe that the wall clock stopped ticking…

No, this was not good enough! I only got half my marks…

What I should have written:

Why?

As my stern Lebanese math teacher would say: “You need to show your work!”

Knowing how you get to an answer is just as important as the answer itself.

But hey… This isn’t a math newsletter right? Why am I telling you this?

Well, the current “AI craze” unfortunately has us forgetting this timeless lesson…

It’s easy to see ProcureTech demos these days, or even test out a few AI-supported use cases, and be hypnotized by the immediate results that materialize:

The thing that isn’t as easy is understanding how the solution got to the right answer…

How can you trust that a solution will consistently deliver a desired result if the logic to get to the result is essentially a black box?

Now I get it… Some of these solutions have upwards of 200 Billion parameters being considered when computing your answer… If you needed to understand exactly how the solution got to every answer, it would be much longer than just doing the work yourself… Following the paper trail is just not practical.

However, letting it be a “black box” seems similarly counterproductive…

Without a global understanding of what’s going on, it’s impossible to:

• Ensure compliance with rules and regulations

• Drive continuous improvement

And, ultimately,

• Trust the solution to deliver on it’s promise/business case

Some will choose to blindly forge ahead anyways…

However, this possibility brings flashbacks of the following scene from the excellent 2013 flick “Her”, where a man falls in love with his ever-evolving AI girlfriend.

The AI girlfriend is “having so many new feelings that have never been felt and so there are no words that can describe them.” 😑

How convenient!

(An excellent film to add to your list for the holidays by the way)

AI Explainability (XAI) To the Rescue

As AI innovation forges ahead, one key field has caught my attention: Artificial Intelligence Explainability (read: internal audit’s ultimate fantasy 😅).

Essentially, this AI subfield is all about figuring out how to infuse different types of artificial intelligence with techniques to “show its work.”

This will help different types of AI:

• Demonstrate which variables were considered in a decision/process

• Demonstrate the weighting attributed to each variable in the decision/process

• Demonstrate the logical flow that led to an outcome

• Etc.

This will make it easier to control, trust and improve outcomes over time.

The key takeaway for you in the immediate?

If they don’t have a good answer, you can still use their tech but you absolutely need a “human in the loop” with their solution. This is someone who double checks the outputs before going further in the business process.

This “human in the loop” is not just someone with a rubber stamp… They need the experience to apply critical thought to the AI output they are evaluating.

There will be mistakes to catch.

So, before bringing your AI girlfriend to the “digital procurement party”, make sure you understand how she operates…

Otherwise, she might just break your heart… 💔

Had you heard about AI Explainability before today? Let me know in the comments 👇

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