Points of view
Some points of view on frequently asked questions
Twelve short pieces on the requirements that recur in Enterprise AI Architect, Director of AI & Data Management, and Enterprise Data Architect postings. Each one takes about three minutes to read and sets out the principles I apply.
AI strategy as a portfolio decision
“Define and drive the enterprise AI strategy and roadmap”
02ReadMeasuring return on AI investment
“Own the business case and demonstrate measurable value from AI investment”
03ReadAI readiness depends on the data foundation
“Ensure enterprise data is AI-ready and fit for GenAI and agentic consumption”
04ReadDefining autonomy boundaries for AI agents
“Architect agentic AI solutions with appropriate guardrails and human oversight”
05ReadThe semantic layer in retrieval and knowledge graph design
“Design RAG, knowledge graph and semantic search architectures”
06ReadData governance that supports delivery
“Establish enterprise data governance frameworks, policies and stewardship”
07ReadModernizing MDM for AI workloads
“Lead Master Data Management strategy, platform selection and implementation”
08ReadReallocating data engineering capacity with AI
“Modernize the enterprise data platform and data engineering function”
09ReadMaking architecture review boards effective
“Establish architecture governance, standards, guardrails and design assurance”
10ReadExplainability as an architecture requirement in regulated environments
“Deliver AI and data solutions within regulated, audited environments”
11ReadDeveloping architects and technical leads
“Lead, mentor and grow a team of architects and technical leads”
12ReadOwnership and operating model in data programs
“Define the target operating model for data and AI across the enterprise”