Each article is written from project experience, focusing on architecture, governance, and practical trade-offs for teams deploying AI and automation in production environments.
How to define scope, metrics, and technical constraints for AI pilots so they can realistically graduate into supported, production-grade services.
Common architectures for retrieval-augmented generation with internal content, including data modeling, indexing, and access control considerations.
Practical guidance on environment design, DLP policies, and solution lifecycle management for large organizations using Power Platform.
A decision framework for when to configure existing platforms and when to invest in purpose-built systems.
Approaches for defining and tracking metrics that reflect real operational improvements, not just automation coverage.
A practical checklist for security, compliance, and operations teams reviewing new AI-powered capabilities.
