Business decision
Define the decision, business outcome, owner, and standard for success before selecting a tool.
Menu
What I'm building with AI
AI works better when it understands the work around the work.
I am building practical AI-enabled systems that connect knowledge, relationships, decisions, and execution. The work is hands-on and evolving, strengthening the judgment I can bring to enterprise growth and transformation. What matters most is not the tool itself, but whether it helps people make a better next move while retaining judgment and accountability.
Connected contextBring relevant information and relationships into view.
Useful decisionsTurn complexity into a clearer choice or next action.
Human accountabilityAI assists; people retain judgment, approval, and ownership.
Commercial judgment
Start with the operating decision.
Then evaluate the AI.
In an enterprise growth organization, the important question is not whether a tool can produce an output. It is whether the tool improves a consequential decision without weakening evidence, privacy, adoption, or accountability.
Evidence and context
Determine what the system needs to know, what it may use, and how people can verify the result.
Workflow and adoption
Design for the real handoffs, relationships, approvals, and behaviors required for the work to improve.
Ownership and risk
Keep people accountable for consequential choices and make privacy, security, and failure modes explicit.
This is the same discipline I bring to growth systems: understand the work, make the decision model visible, and invest only where the operating result can improve.
See how my teams helped activate OpenText Aviator in the market →Selected working examples
Enough to show the thinking.
Not the blueprint.
These examples demonstrate how I approach AI-enabled work without publishing the prompts, system instructions, data structures, orchestration, or internal operating logic behind them.
Career intelligence
Connect opportunity, evidence, and narrative
I use a system I built to bring opportunity context, career evidence I can properly use, positioning, and narrative review into one connected process. It helps me examine fit, challenge assumptions, and keep the final story accurate and defensible.
What it demonstrates Evidence discipline, synthesis, executive positioning, and controlled use of AI.
Human-centered decision support
Keep a long, complex decision connected
I helped build a protected system for a multi-year university journey. It keeps research, direct experience, changing priorities, reflection, and next actions connected without asking an algorithm to make the decision.
What it demonstrates Privacy-conscious design, relationship-aware context, visible tradeoffs, and human decision authority.
What remains private: These are working examples, not commercial products or client implementations. Private career, employer, student, and family information remains private. Detailed workflows, prompts, schemas, system roles, and implementation logic are intentionally not published.
The working toolkit
A mix of tools.
One connected way of working.
I select tools based on the work they need to support: research, reasoning, knowledge management, local experimentation, software development, document collaboration, and human review. The list matters less than the purpose each tool serves.
ChatGPTReasoning, research, drafting, and coordinated workOfficial site ↗ClaudeAnalysis, synthesis, and an additional review perspectiveOfficial site ↗CodexAgentic software development, automation, and implementationOfficial site ↗GitHubVersion control, source collaboration, and release historyOfficial site ↗ObsidianConnected knowledge, evidence, and decision historyOfficial site ↗OllamaLocal model experimentation and private prototypingOfficial site ↗HirebaseMarket and opportunity researchOfficial site ↗Google DriveSource materials, evidence, and document collaborationOfficial site ↗LinkedInProfessional relationships, recommendations, and market insightOfficial site ↗GmailCommunication, workflow coordination, and follow-upOfficial site ↗
One example of an output
An evidence-backed decision brief
A concise view of the situation, the evidence that can be relied upon, the important open questions, and the next decision requiring human judgment. The output is useful; the system that produces it remains controlled.
The executive pattern
Start with the work.
Then decide where AI belongs.
I begin by understanding the decisions, participants, knowledge, handoffs, risks, and accountability already present. From there, I determine where AI can remove friction, strengthen context, or improve a next move without weakening human ownership.
Understand the work
Make the real workflow, relationships, decision points, and friction visible.
Apply AI deliberately
Use the right combination of tools where assistance can materially improve the work.
Keep people accountable
Preserve human judgment, privacy, approval, and responsibility for consequential action.
This is the same leadership principle behind the growth systems I have built throughout my career: understand how the work connects, make the operating model visible, and help people act with greater clarity.
A conversation starter
The most useful question is not “Which AI tool?” It is “What should work better?”
Start by understanding the work, and the people doing it.
Organizations rarely struggle because people are not working hard. More often, priorities, information, accountability, and operating systems have stopped connecting.
Returning to sprint training after 27 years reinforced the same lesson I bring to leadership. Progress comes from consistent work, useful feedback, and the confidence to keep moving before the result is visible.
The same principle guides how I approach AI. Understand the work first. Connect the context. Use technology to help people make a better next move.