EDITOR’S NOTE: The following assessment overview is based on available information regarding Duke Energy, its SVP of Supply Chain, Real Estate, and Chief Procurement Officer’s Best and Preferred Relationship Model, and 10 strategically selected ProcureTech solution providers using the RAM 2025 6-Model Assessment Tool. All of the information used to complete this preliminary assessment is publicly available knowledge and does not disclose any information beyond what is readily available through public records. In short, no proprietary or confidential information was used with the Level 1 and Level 2 assessment runs. Level 3 and Level 4 assessment runs are strictly confidential.
Using the RAM 2025 Levels 1 and 2 advanced AI algorithm framework, I assessed 10 potential ProcureTech solution providers for Duke Energy based on several critical criteria, including Dwight Jacobs ‘ personal relationship assessment model profile. Remember, it takes people to make technology work, and not the other way around. While the entire report is several pages long, here is the screenshot of the final selection based on the Hansen Fit Score.
Final Recommendation
SAP Ariba is the optimal choice for Duke Energy based on:
- Convergent Expert Assessment: 4 of 6 models independently reached this conclusion
- Dwight’s Leadership Profile: Aligns with his risk oversight, financial expertise, and transformation experience
- Duke Energy’s Context: Matches the scale, complexity, and existing infrastructure of a Fortune 150 utility
- Strategic Risk Management: Provides the lowest implementation risk with the highest probability of measurable value delivery
ConvergentIS represents a compelling alternative for organizations prioritizing innovation-led transformation over proven stability, but the consensus favors SAP Ariba’s combination of utility sector expertise, enterprise scale, and integration capabilities for Duke Energy’s specific context and Dwight Jacobs’ leadership requirements.
As a point of clarification, between the RAM 1998 model, which achieved an accuracy rate of 97.3% over seven consecutive years, and considering the RAM 2025 loopback learning curve, the accuracy of this recommendation is estimated to be between 80% and 87.5%.
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Procurement Insights
RAM 2025 Levels 1 and 2 Assess 10 ProcureTech Providers For Duke Energy
Posted on June 24, 2025
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EDITOR’S NOTE: The following assessment overview is based on available information regarding Duke Energy, its SVP of Supply Chain, Real Estate, and Chief Procurement Officer’s Best and Preferred Relationship Model, and 10 strategically selected ProcureTech solution providers using the RAM 2025 6-Model Assessment Tool. All of the information used to complete this preliminary assessment is publicly available knowledge and does not disclose any information beyond what is readily available through public records. In short, no proprietary or confidential information was used with the Level 1 and Level 2 assessment runs. Level 3 and Level 4 assessment runs are strictly confidential.
Using the RAM 2025 Levels 1 and 2 advanced AI algorithm framework, I assessed 10 potential ProcureTech solution providers for Duke Energy based on several critical criteria, including Dwight Jacobs ‘ personal relationship assessment model profile. Remember, it takes people to make technology work, and not the other way around. While the entire report is several pages long, here is the screenshot of the final selection based on the Hansen Fit Score.
Final Recommendation
SAP Ariba is the optimal choice for Duke Energy based on:
ConvergentIS represents a compelling alternative for organizations prioritizing innovation-led transformation over proven stability, but the consensus favors SAP Ariba’s combination of utility sector expertise, enterprise scale, and integration capabilities for Duke Energy’s specific context and Dwight Jacobs’ leadership requirements.
As a point of clarification, between the RAM 1998 model, which achieved an accuracy rate of 97.3% over seven consecutive years, and considering the RAM 2025 loopback learning curve, the accuracy of this recommendation is estimated to be between 80% and 87.5%.
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