EDITOR’S NOTE: For me, the three would be Niki Lauda, Jackie Stewart, and Emerson Fittipaldi.
In case you haven’t picked up on it, which is possible, all three of the above names I shared for 2025 are FI race car champions. Now, you may wonder what that has to do with the procurement profession and AI. As you will soon discover, everything, especially when it comes to the development of today’s procurement professionals.
A ScenarioTo Consider
Let’s say they put you behind the wheel of the same F1 car that an Oscar Piastri, Max Verstappen, or Lando Norris is driving. All four of you are at the starting line for a race. Even though all four of you have the same “equipment,” what is your chance of winning, or even coming in second, or third? After all, all four of you are driving the same, or a variation of, the exact same powerful and advanced vehicle. What is the edge one driver has over another?
Poll Time
The following is a poll that was posted in the Global & Purchasing Supply Chain Professionals LinkedIn Group, which has over 164,000 members. Use this link to check out the group.
“Don’t blame the tools—learn how to use them.”
The fact is that AI, Generative AI, or Agentic AI doesn’t take you, the procurement professional, out of the equation and turn you into a black box spectator. In short, digital procurement transformation success doesn’t need data scientists or IT programmers to succeed; it requires you, the experienced procurement professional, to learn how to hone your industry expertise to fully leverage the promise of AI and technology in general.
Case Examples
Here are several procurement and business-specific examples of the saying “A poor craftsman always blames his tools” in action:
Example 1: Procurement Platform Underuse
Scenario:
A procurement team adopts a robust source-to-pay platform like Coupa or Ivalua. After several months, savings targets aren’t met.
Blame Game:
The team complains the platform is “too complex” or “not intuitive enough.”
Reality:
No one invested time in proper training, and category managers are still relying on spreadsheets.
Lesson:
The tool isn’t the issue—underutilization and lack of change management are. A skilled “craftsman” would learn and optimize the platform, not blame it.
Example 2: Supplier Risk Program Failure
Scenario:
A company implements a risk-monitoring tool like Prewave or Achilles, but supply disruptions still occur.
Blame Game:
Executives say the risk tool doesn’t work or is “too slow.”
Reality:
The tool flagged risks, but no one acted on the alerts or updated supplier data in real time.
Lesson:
Tool output is only as good as human input and decision-making. Tools are enablers, not replacements for accountability.
Example 3: ERP Implementation Delays
Scenario:
An SAP Ariba or Oracle implementation misses timelines and goes over budget.
Blame Game:
Project sponsors blame the software’s “rigid architecture.”
Reality:
Internal teams didn’t map processes clearly, had no unified change management plan, and pushed unrealistic deadlines.
Lesson:
A capable procurement leader aligns internal governance before the tool enters the picture.
Example 4: Poor Spend Visibility
Scenario:
A company adopts an analytics solution like SpendHQ or Sievo but still lacks visibility into indirect spend.
Blame Game:
Finance says “the platform doesn’t capture the right data.”
Reality:
Key data sources weren’t integrated, taxonomy was outdated, and stakeholder buy-in was weak.
Lesson:
Great insights require clean, structured input. It’s not the platform—it’s the discipline behind the data.
Summary:
The saying reminds us that successful outcomes depend more on human ownership than the technology itself. Poor performance is often the result of:
- Lack of training
- Weak leadership
- Incomplete integration
- Resistance to change
TODAY’S TAKEAWAY
“Our cognitive landscape is undergoing a seismic shift. We enter an era in which neither humans nor AI monopolize adaptability or learning. Instead, we find ourselves in a dynamic equilibrium where both forms of evolving intelligence—neuroplastic and technoplastic—continuously evolve.” – John Nosta, Techno-plasticity in the Age of Artificial Intelligence (Psychology Today)
AI mirrors the unique human ability of neuroplasticity.
Like the technologies that came before it and those to come, it has been — and will always be — about Human with AI, Not Human Versus AI.
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30
Procurement Insights
Who are Procurement’s 2025 AI’s Oscar Piastri, Lando Norris, and Max Verstappen
Posted on June 3, 2025
0
EDITOR’S NOTE: For me, the three would be Niki Lauda, Jackie Stewart, and Emerson Fittipaldi.
In case you haven’t picked up on it, which is possible, all three of the above names I shared for 2025 are FI race car champions. Now, you may wonder what that has to do with the procurement profession and AI. As you will soon discover, everything, especially when it comes to the development of today’s procurement professionals.
A ScenarioTo Consider
Let’s say they put you behind the wheel of the same F1 car that an Oscar Piastri, Max Verstappen, or Lando Norris is driving. All four of you are at the starting line for a race. Even though all four of you have the same “equipment,” what is your chance of winning, or even coming in second, or third? After all, all four of you are driving the same, or a variation of, the exact same powerful and advanced vehicle. What is the edge one driver has over another?
Poll Time
The following is a poll that was posted in the Global & Purchasing Supply Chain Professionals LinkedIn Group, which has over 164,000 members. Use this link to check out the group.
“Don’t blame the tools—learn how to use them.”
The fact is that AI, Generative AI, or Agentic AI doesn’t take you, the procurement professional, out of the equation and turn you into a black box spectator. In short, digital procurement transformation success doesn’t need data scientists or IT programmers to succeed; it requires you, the experienced procurement professional, to learn how to hone your industry expertise to fully leverage the promise of AI and technology in general.
Case Examples
Here are several procurement and business-specific examples of the saying “A poor craftsman always blames his tools” in action:
Example 1: Procurement Platform Underuse
Scenario:
A procurement team adopts a robust source-to-pay platform like Coupa or Ivalua. After several months, savings targets aren’t met.
Blame Game:
The team complains the platform is “too complex” or “not intuitive enough.”
Reality:
No one invested time in proper training, and category managers are still relying on spreadsheets.
Lesson:
The tool isn’t the issue—underutilization and lack of change management are. A skilled “craftsman” would learn and optimize the platform, not blame it.
Example 2: Supplier Risk Program Failure
Scenario:
A company implements a risk-monitoring tool like Prewave or Achilles, but supply disruptions still occur.
Blame Game:
Executives say the risk tool doesn’t work or is “too slow.”
Reality:
The tool flagged risks, but no one acted on the alerts or updated supplier data in real time.
Lesson:
Tool output is only as good as human input and decision-making. Tools are enablers, not replacements for accountability.
Example 3: ERP Implementation Delays
Scenario:
An SAP Ariba or Oracle implementation misses timelines and goes over budget.
Blame Game:
Project sponsors blame the software’s “rigid architecture.”
Reality:
Internal teams didn’t map processes clearly, had no unified change management plan, and pushed unrealistic deadlines.
Lesson:
A capable procurement leader aligns internal governance before the tool enters the picture.
Example 4: Poor Spend Visibility
Scenario:
A company adopts an analytics solution like SpendHQ or Sievo but still lacks visibility into indirect spend.
Blame Game:
Finance says “the platform doesn’t capture the right data.”
Reality:
Key data sources weren’t integrated, taxonomy was outdated, and stakeholder buy-in was weak.
Lesson:
Great insights require clean, structured input. It’s not the platform—it’s the discipline behind the data.
Summary:
The saying reminds us that successful outcomes depend more on human ownership than the technology itself. Poor performance is often the result of:
TODAY’S TAKEAWAY
“Our cognitive landscape is undergoing a seismic shift. We enter an era in which neither humans nor AI monopolize adaptability or learning. Instead, we find ourselves in a dynamic equilibrium where both forms of evolving intelligence—neuroplastic and technoplastic—continuously evolve.” – John Nosta, Techno-plasticity in the Age of Artificial Intelligence (Psychology Today)
AI mirrors the unique human ability of neuroplasticity.
Like the technologies that came before it and those to come, it has been — and will always be — about Human with AI, Not Human Versus AI.
30
30
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