In the age of Generative AI and Agentic AI, your ProcureTech success comes down to your ProcureTech provider being able to effectively answer the following two sets of questions:
Generative AI
- With Generative AI, are they using a single-strand or a simultaneous multi-strand data scraping process?
- If the answer is the latter, you should then ask your ProcureTech solution provider what self-learning algorithm architecture they use to consolidate multiple strands into a single output that provides the optimum collective predictive outcome.
Introduction Reference Links:
High-Level Excerpt:
“Both the IBM RS/6000’s use of agent-based architecture and Hansen’s RAM leveraged agent-based models to optimize complex systems—network management in the case of RS/6000 and procurement processes in RAM. This shared approach underscores the versatility and effectiveness of agent-based models in managing and improving intricate operations across different domains.
Jon W. Hansen’s theory of strand commonality, his 1998 Relational Acquisition Model (RAM), and IBM’s RISC System/6000 (RS/6000) converge on the principle of integrating disparate data streams to optimize complex systems.“
Strand Commonality Theory:
“Hansen’s strand commonality theory posits that seemingly unrelated data streams, or “strands,” possess interconnected attributes. Identifying and leveraging these connections can enhance decision-making and operational efficiency. This concept underscores the importance of recognizing hidden relationships within data to inform strategic actions.”
Agentic AI
- What acquisition model did they use to identify both human and technology “Agent” identification, input, and practical integration with an effective lookback learning process?
- How do they merge human and technological inputs into a single source of truth?
Introduction Reference Links:
High-Level Excerpt:
“The critical part of the human-led agent-based model is that the continuous learning capabilities of the algorithms occur by adapting to how internal and external human and technology agents work in the real world in the end-to-end supply chain to effectively and efficiently manage and grade performance metrics from order placement through to order delivery. In essence, the Agent-Based Metaprise model performs a self-cleaning of the data as a part of the human and technology agents’ daily routine inputs becoming a seamless part of the order to fulfillment process.”
“While agentic AI can operate autonomously to a degree, initial human-defined objectives and occasional oversight are critical to ensure alignment with intended purposes and to mitigate risks. Fully independent learning without any human input is theoretically possible but risky and not yet practical for complex, real-world applications.”
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Procurement Insights
The two most important set of questions to ask your ProcureTech solution provider up front
Posted on April 19, 2025
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In the age of Generative AI and Agentic AI, your ProcureTech success comes down to your ProcureTech provider being able to effectively answer the following two sets of questions:
Generative AI
Introduction Reference Links:
High-Level Excerpt:
“Both the IBM RS/6000’s use of agent-based architecture and Hansen’s RAM leveraged agent-based models to optimize complex systems—network management in the case of RS/6000 and procurement processes in RAM. This shared approach underscores the versatility and effectiveness of agent-based models in managing and improving intricate operations across different domains.
Jon W. Hansen’s theory of strand commonality, his 1998 Relational Acquisition Model (RAM), and IBM’s RISC System/6000 (RS/6000) converge on the principle of integrating disparate data streams to optimize complex systems.“
Strand Commonality Theory:
“Hansen’s strand commonality theory posits that seemingly unrelated data streams, or “strands,” possess interconnected attributes. Identifying and leveraging these connections can enhance decision-making and operational efficiency. This concept underscores the importance of recognizing hidden relationships within data to inform strategic actions.”
Agentic AI
Introduction Reference Links:
High-Level Excerpt:
“The critical part of the human-led agent-based model is that the continuous learning capabilities of the algorithms occur by adapting to how internal and external human and technology agents work in the real world in the end-to-end supply chain to effectively and efficiently manage and grade performance metrics from order placement through to order delivery. In essence, the Agent-Based Metaprise model performs a self-cleaning of the data as a part of the human and technology agents’ daily routine inputs becoming a seamless part of the order to fulfillment process.”
“While agentic AI can operate autonomously to a degree, initial human-defined objectives and occasional oversight are critical to ensure alignment with intended purposes and to mitigate risks. Fully independent learning without any human input is theoretically possible but risky and not yet practical for complex, real-world applications.”
30
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