When AIs Run the Enterprise IT Landscape
Andy Forbes
Capgemini America Salesforce Core CTO - Coauthor of "ChatGPT for Accelerating Salesforce Development"
#GenerativeAI
The opinions in this article are the author's and do not necessarily reflect the opinions of their employer.
In the mid-term future of enterprise IT, the landscape will be dominated by advanced AI entities, each governing a specific domain of organizational operations – CRM, supply chain, finance, etc. Let's follow an enterprise architect, Robert, and his personal AI assistant as they embark on an initiative to integrate real-time reporting of parts consumption from the factories and the field service organization into the supply chain system.
Initial Consultation
An experienced enterprise architect, Robert starts his day by convening a virtual meeting with the system integrator AI, a sophisticated entity specialized in orchestrating enterprise updates. Accompanied by his AI assistant, he outlines the primary objective: to create a seamless, real-time data flow between the factory, field service, and the supply chain system.
The system integrator AI, equipped with a comprehensive understanding of the organization's IT ecosystem, recognizes the complexity of this integration. It involves the technical aspects of data transmission and processing and the nuances of inter-AI communication and negotiation, ensuring that the new data streams do not disrupt existing processes.
Design and Planning
The system integrator AI proposes a design strategy which includes a detailed analysis of the current systems' capabilities, data formats, and communication protocols. It suggests developing a middleware solution, which will act as a bridge between the factory and field service systems and the supply chain AI. This middleware will collect, transform, and transmit data in real-time, ensuring compatibility and minimal latency.
Robert and his AI assistant work closely with the system integrator AI to refine this strategy. They consider scalability, security, and compliance with industry standards. With its ability to refactor and update code autonomously, the system integrator AI starts developing the initial version of the middleware, incorporating feedback and specifications provided by Robert and his AI assistant.
Negotiation and Coordination
By mid-morning, the system integrator AI initiates negotiations with the supply chain AI and the AIs governing the factory and field service systems and their human operators. These discussions focus on the data exchange protocols, the impact on system performance, and the allocation of resources for processing the incoming data streams.
Robert oversees these negotiations, ensuring the business objectives and operational constraints are adequately addressed. His AI assistant is crucial in translating the technical aspects into business language, facilitating effective communication between Robert and the AI entities.
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Testing and Validation
After lunch, the focus shifts to rigorous testing and validation. The system integrator AI coordinates a series of simulated scenarios to test the reliability and efficiency of the data flow. Robert and his AI assistant review the test results, identifying potential issues and providing feedback for optimization.
During this phase, Robert's AI assistant also assists in assessing the impact of the new integration on related business processes and workflows. It helps to identify areas where further adjustments may be required to maximize the benefits of the real-time data reporting.
Deployment and Monitoring
Once the testing phase confirms the stability and performance of the solution, the system integrator AI schedules the deployment. This phase is carefully planned to minimize disruption to ongoing operations. The deployment is executed in the evening, outside peak business hours, with the system integrator AI closely monitoring the process.
After the deployment, intensive monitoring ensures the integration operates as intended. The supply chain AI begins to receive real-time data from the factory and field service systems, enabling more responsive decision-making and optimization of the supply chain processes.
Post-Deployment Review
In the day following the deployment, Robert conducts a post-implementation review. He, along with his AI assistant and the system integrator AI, analyze the performance data, user feedback, and the overall impact on business operations. They identify any areas for further improvement in future enhancements.
Conclusion
The successful deployment of the real-time reporting integration marks a significant milestone in the organization's digital transformation journey. Robert's expertise, combined with the capabilities of his AI assistant and the collaborative efforts of the various AI entities, demonstrates the power of human-AI synergy in driving innovation and efficiency in enterprise IT landscapes.
In this future, the role of enterprise architects like Robert evolves beyond traditional boundaries, blending technical acumen with strategic vision enabled by AI entities that bring unparalleled speed, scalability, and intelligence to enterprise IT operations.
The supply chain AIs review recent political, market and geographic events. As the Procurement managers log in they review the AIs suggestions for buying in potentially stranded raw materials at a lower cost as buffer stocks to take advantage of local surpluses and also potential low cost storage options. The speak to the supply managers and authorize a local auction with co-opetitiors who they could work with to create a bigger order and leverage the advantage. They then look at the AI predictions for currency fluctuations and hedge options before feeding this into the T&C's for longer term contracts. And so on...