itssujeeth Senior Expert Software Engineer (Enterprise Architecture, Director) · 2023 Proof of concept
Enterprise GenAI Blueprint and POCs
The enterprise generative AI blueprint for a large logistics company, answered with working proofs of concept across Google and Microsoft stacks rather than slideware. None of the POCs went to production, and I say so.
Leadership at a Fortune-class logistics enterprise wanted three answers before committing to generative AI: what value it could deliver, which use cases were the low-hanging fruit, and how to implement it securely. Teams were already experimenting with no shared direction.
I led the blueprint: model and platform evaluations, proofs of concept, reference architectures, responsible-AI guidance, and adoption guidance, working with product, engineering, security, data, and business leaders on trade-offs and roadmaps. I built the POCs: EnterpriseBard, the company's first conversational agent inside Microsoft Teams, connecting Microsoft Virtual Agent through Power Automate to the Vertex AI API; a retrieval-based HR assistant on Azure OpenAI and Azure Cognitive Search over HR documents; and an invoice-verification POC on Google Document AI with a custom parser. I also led a GenAI developer-productivity analysis that mapped friction across build, review, testing, and release and defined AI-assisted workflows and adoption metrics against it.
EnterpriseBard and the HR assistant became the initial triggers for GenAI use cases across the technical community. Neither went to production. The invoice-verification approach proved itself and moved to the product architecture team to take forward. The blueprint shaped how the enterprise approached generative AI, and the vulnerability-remediation work it spawned is described separately.
The honest version of this story is that the POCs did their job and then stopped. Their job was to answer leadership’s three questions with something they could click on. A conversational agent inside the tools employees already used proved that conversational AI could live where people work. The HR assistant proved the same retrieval pattern on the Microsoft stack. The invoice POC proved that document AI plus a custom parser could match carrier invoices to shipments, and that one was handed to the product architecture team to build properly.
What the blueprint changed was the direction, not a production dashboard. Evaluations across vendors, reference architectures, responsible-AI guidance, and a developer-productivity analysis gave product, engineering, security, and data leaders a shared view of where generative AI belonged and how to introduce it safely. That is the internal version of what an agentic practice does for customers: answer the value, prioritization, and security questions with working proofs, then hand validated approaches to the teams that will build them.
I previously described this work on this site as three production RAG applications. That was wrong, and this page replaces it. The applications were proofs of concept. The production work from the same period is the pricing-email automation.