The following is a DPW post on LinkedIn:
Why do some companies scale AI while others get stuck in the proof of concept stage?
Accenture research reveals that 75% of executives fear their business won’t survive without scaling AI in the next 5 years. Yet, 76% are struggling to make it happen.
At hashtag#DPWAmsterdam, Accenture’s Kris Timmermans highlighted 3 key factors that drive the shift from experimental to exponential AI impact.
I’d love to know where you stand on this. What do you think is holding companies back to scale AI? Let me know👇
The following is my response to the above DPW post by Matthias Gutzmann:
Technology and data should not be the primary focus when discussing AI scalability. These are secondary considerations behind understanding your organization’s “Error Chain.”
Check out the following video both before and after reading the following two key points – https://bit.ly/40fJxNY
⚠️ Almost all initiatives use a technology-led equation-based development and implementation model with an 80% failure rate. The “Error Chain” is not a technology problem. Instead, it results from incorrectly recognizing and understanding all impacting agents both within and external to the operating enterprise.
💡 Conversely, using an agent-based AI implementation model—do not confuse this with the Gartner-introduced term “Agentic AI”—all impacting agents are correctly identified and incorporated into the collective process, resulting in a successful implementation and outcome. It’s about “strand commonality,” which was the basis for my SR&ED funding in the late 1990s
To put it in simpler terms, we are overcomplicating the situation. Maybe it’s time to remember the story of the truck getting stuck under the overpass and start to let some air out of the tires. AI scalability doesn’t start with AI – it starts with agent-based modeling.
“AI is going to replace every human being who does not want to work with AI” – Kris Timmermans
Today’s Takeaway—If we accept what Mr. Timmermanns said in the last 30 seconds of his presentation, we will have this same conversation 10 to 15 years from now as we look back on today’s technology with the same dismissive attitude we now have towards floppy disk drives, 2800bpm modems, and 10MB hard disk drives because he is championing a “leading with technology” equation-based model which you cannot scale.
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Procurement Insights
The last 30 seconds of Kris Timmermans’ DPW presentation is shocking – but not in a good way
Posted on October 25, 2024
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The following is a DPW post on LinkedIn:
Why do some companies scale AI while others get stuck in the proof of concept stage?
Accenture research reveals that 75% of executives fear their business won’t survive without scaling AI in the next 5 years. Yet, 76% are struggling to make it happen.
At hashtag#DPWAmsterdam, Accenture’s Kris Timmermans highlighted 3 key factors that drive the shift from experimental to exponential AI impact.
I’d love to know where you stand on this. What do you think is holding companies back to scale AI? Let me know👇
The following is my response to the above DPW post by Matthias Gutzmann:
Technology and data should not be the primary focus when discussing AI scalability. These are secondary considerations behind understanding your organization’s “Error Chain.”
Check out the following video both before and after reading the following two key points – https://bit.ly/40fJxNY
⚠️ Almost all initiatives use a technology-led equation-based development and implementation model with an 80% failure rate. The “Error Chain” is not a technology problem. Instead, it results from incorrectly recognizing and understanding all impacting agents both within and external to the operating enterprise.
💡 Conversely, using an agent-based AI implementation model—do not confuse this with the Gartner-introduced term “Agentic AI”—all impacting agents are correctly identified and incorporated into the collective process, resulting in a successful implementation and outcome. It’s about “strand commonality,” which was the basis for my SR&ED funding in the late 1990s
To put it in simpler terms, we are overcomplicating the situation. Maybe it’s time to remember the story of the truck getting stuck under the overpass and start to let some air out of the tires. AI scalability doesn’t start with AI – it starts with agent-based modeling.
“AI is going to replace every human being who does not want to work with AI” – Kris Timmermans
Today’s Takeaway—If we accept what Mr. Timmermanns said in the last 30 seconds of his presentation, we will have this same conversation 10 to 15 years from now as we look back on today’s technology with the same dismissive attitude we now have towards floppy disk drives, 2800bpm modems, and 10MB hard disk drives because he is championing a “leading with technology” equation-based model which you cannot scale.
30
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