Insights
Thinking built
in the field.
Field notes on the decisions that move enterprise AI from pilots into everyday practice.
Insights from practice
The thinking and the practice evolved together.
These articles reflect my thinking about enterprise AI adoption, my experience learning to use AI as a writing partner, and real-world experience helping our customers. The ideas, arguments, and conclusions are mine. AI helped me develop, structure, test, and refine them.
Some of the earlier pieces are both thought pieces and practical experiments in AI-assisted writing. Because AI changes quickly, the publication date is part of the context. Earlier articles reflect the tools, capabilities, and practices available when they were written, while many of the underlying adoption questions remain relevant. As the tools and my own practice have matured, the writing has become more consistent and the collaboration less visible. I have kept the full collection because it shows what learning with AI looks like over time.
TechSense Solutions, Inc.
Voice Mode Captures the Thinking Typing Misses
Talking lets people bring their full context to AI. Adoption often begins when someone works through an unfinished problem out loud.
Read article ↗02Experimentation Needs a Container, Not a Ceiling
Exploration and production are two kinds of spending. Give each its own container, and teams keep experimenting while finance keeps its footing.
Read article ↗03AI Training Was the Easy Part
Training introduces the tool. Fluency develops during the application period, when people use AI on their own work over time.
Read article ↗04The AI Ownership Stack
Agents act inside workflows before anyone names an owner. Three layers of ownership close that gap ahead of the first incident.
Read article ↗05The Missing Context Layer in Executive AI Adoption
An executive's judgment lives in their head. Five kinds of context turn a general AI tool into a useful thinking partner.
Read article ↗06AI Scales Output. Judgment Requires Design.
As AI accelerates production, organizations have to design the experiences that build judgment and evaluation.
Read article ↗07The Best AI Governance is Infrastructure
Policies describe intent. Infrastructure determines what AI can access, do and move forward.
Read article ↗08The Enterprise AI Decisions Gap
AI surfaces insight faster than most organizations decide. Clear ownership and decision rights connect insight to action.
Read article ↗09Beyond the AI Champion
A champion proves AI works in your environment. Moving the organization takes a repeatable path the rest of the team can follow.
Read article ↗10What 5,000 AI Use Cases Tell Us About Enterprise ROI
AI produces stronger returns when organizations apply it to complex work, keep expertise close to execution and shorten feedback loops.
Read article ↗11Five Weeks to a Cross-Domain Breakthrough
A structured five-week approach for using AI to bring multiple functions together around problems no one team owns.
Read article ↗12Five Things to Build AI Fluency
Five small experiments help people build practical fluency by using AI on work they already understand.
Read article ↗13When the "Paper Calendar Guy" Became the AI Guy
A seasoned leader moved from AI training to practical fluency by applying the tools to real work with people he trusted.
Read article ↗14The Research is In: What the Winners Do Differently
Four major studies point to the same divide: AI leaders build reusable systems, shared governance and business capability instead of isolated projects.
Read article ↗15Governing the Learning Curve
Governance and learning work best together, giving people clear boundaries, confidence and room to build practical AI capability.
Read article ↗16The Translation Problem: Why AI Pilots Don't Scale
AI pilots stall when leadership cannot translate technical potential into strategic decisions, trust and organizational momentum.
Read article ↗