Data quality metrics should tell you whether the business can rely on its data for a particular decision. A dashboard full of passing checks can still leave that question unanswered. Imagine a billing team receiving 990 usage records. Every record has the required fields, a valid customer identifier and a nonnegative quantity. The checks look
Data Quality Metrics: What Leaders Should Measure Beyond Pass Rates Read More »
How to tell whether AI has become part of useful work: a fictional 1,000-seat adoption funnel, five metric layers and a review cadence from pilot to scale.
AI Adoption Metrics: What to Measure After the Pilot Goes Live Read More »
A cost model for a fictional supplier-confirmation agent showing cost per successful task, payback, break-even volume and why review policy matters more than token prices.
AI Agent Cost: What an Agent Has to Save Before It Pays for Itself Read More »
A practical AI prioritization framework that ranks AI projects on value that survives exceptions, review time, adoption and missing baselines, then tests whether the ranking holds.
AI Prioritization Framework: Which AI Project Should You Fund First? Read More »
The same AI agent scored 0%, 79% and 93% on the same 14 business questions. Here is the evaluation method I use to see what the score hides.
AI Agent Evaluation: How I Test an AI Agent Before I Trust It Read More »
PRACTICAL AI EXPERIMENT A semantic layer AI setup improved Databricks Genie’s performance on the same 14 business questions from 79% with prompt-based rules to 93% with governed metrics. My manual review found all 14 final answers business-correct. More importantly, the semantic layer fixed a return-rate calculation that had previously produced 56.89% instead of the verified
Semantic Layer for AI: Prompt Rules vs Governed Metrics in Databricks Read More »
AI agent observability becomes useful when a run reports success and the business record says otherwise. Imagine a purchasing agent checking a supplier confirmation for 100 industrial valves. The purchase order requires delivery on October 15. The supplier offers October 22. The agent retrieves the correct PO, completes without an error and labels the confirmation
AI Agent Observability: What to Watch After an Agent Says Done Read More »
What Is an AI Agent Harness? A Business Example of How Agents Work An AI agent harness is the software around an AI model that lets it carry out a task across several steps. It supplies instructions, access to information, tools, a place to keep track of progress, and rules for checking or stopping work.
What Is an AI Agent Harness? A Business Example of How Agents Work Read More »
Shadow AI often starts with a reasonable work problem. Someone needs to sort customer tickets before a service review, but the approved tool cannot read the export. They paste the file into a personal AI account, get a useful answer, and move on. A manager who discovers it has two jobs: find out what data
Shadow AI at Work: What Managers Should Do About Unapproved Tools Read More »
An enterprise AI agent sounds appealing when a team is drowning in follow-ups. It can read records, compare them, prepare a next step, and sometimes act through connected systems. The useful question for a business leader is narrower: which part of a real workflow should the agent handle, and how will we know it made
Enterprise AI Agent: How to Choose a Business Workflow Worth Building Read More »
