Better decisions
Connect the commercial, technical, and human sides of a problem. Understand the alternatives, assumptions, and consequences well enough to decide what deserves further work.
How I use AI
Building the capacity to learn, make decisions, and turn ideas into useful work.
I use AI to connect commercial experience, technical learning, research, and creative work. My objective is to turn observations into informed decisions, and decisions into work that can be tested, improved, and reused.
Across the businesses and projects I’m developing, I want each investment of time to build something that lasts: knowledge I can return to, a process I can repeat, or an idea I can examine more closely. AI supports the research, preparation, and execution. I provide the direction, context, and judgment.
What I’m working toward
Knowledge that carries forward
Preserve the reasoning behind a decision, including corrections. A new conversation or project should be able to build on completed work and the lessons from earlier attempts.
More capacity to build
Move from an observation to a research brief, comparison, learning tool, draft, or small application. Make the next step concrete enough to review before committing more time or money.
More room to explore
Develop technical understanding, investigate history, tell stories, and make things with my children. Learning, creativity, and time together are part of what I want this work to support.
How my approach has evolved
My use has expanded from individual questions into commercial research, recurring planning, technical learning, and connected projects. The important change has been learning how to carry context and responsibility from one piece of work to the next.
Questions became investigations
Research developed into fuller pictures of companies, markets, equipment applications, and supply chains. A useful answer increasingly needed to connect several fields and lead to a practical decision.
Tasks became workflows
Voice capture, daily planning, project records, and follow-up introduced questions of continuity: what has already been done, what changed, and what still needs attention?
Outputs became things to test
Documents, spreadsheets, reference tools, and websites give an idea a form I can examine. Feedback on an actual result is more useful than another description of what might be built.
Experience is shaping the method
Missed instructions, repeated work, and resource limits have made verification and clear checkpoints central to the approach. I am still improving how these parts work together.
How the work moves
I am developing a repeatable method around the decision or deliverable. The records and tools serve that purpose. The quality of the handoff matters as much as the quality of the initial answer.
- Define the objective. Identify the audience, the decision to support, the constraints, and what a useful finished result would contain.
- Bring forward the context. Assemble relevant experience, earlier decisions, records, and research. Identify missing information before building on it.
- Develop and compare. Use AI to organize the problem, examine alternatives, explain unfamiliar material, and prepare something concrete.
- Review and test. Check the evidence, follow the links, use the tool, or read the actual revision. Record what is complete and what remains unresolved.
- Retain and improve. Keep the result, its sources, and the lessons available for the next iteration. Advance the work through the appropriate review and approval.
The workflows, responsibilities, and lessons behind this method →
Where I apply it
01Business and commercial decisions
Market research, account preparation, opportunity assessment, and the operating questions behind developing a business.
02Technical understanding
Electronics, hydraulics, controls, and schematics, studied through the equipment and applications I want to understand.
03Industrial development
Foundation-drill concepts, preliminary component breakdowns, and U.S. sourcing research at the concept stage.
04Media and historical work
Connecting photographs, recorded observations, and research to develop supported stories and preserve context.
05Everyday information
Purchase histories, account exports, recordings, and personal records used to investigate questions and support planning.
06Creativity and shared experiences
Interactive mysteries, stories shaped by our choices, and experiments that bring familiar toys into new adventures.
What connects the work
My background spans logistics, storage systems, mobile equipment, and hydraulics. That experience helps me see the work between functions: how a customer requirement becomes a technical question, how a component choice affects sourcing, and how information reaches the person who needs to act.
I see an opportunity to apply that perspective across the ventures I’m developing. A technical lesson can sharpen a commercial question. Research can support a business investigation and, through a separately reviewed public account, inform a story. A workflow improved in one project can be adapted to another.
The value I am trying to build lies in the combination of experience, retained knowledge, and repeatable execution. Private business information and family records remain subject to their own boundaries; connecting the work does not make every source suitable for publication.
What I want the next stage to demonstrate
The next stage is about reliability: useful work completed consistently, with visible progress between versions. My evaluation criteria are time to a reviewed result, corrections required, processing cost, reuse of completed work, and whether the output supports the next decision. These are measures I want to establish across workflows.
That also means matching the resources to the task. Routine organization and a difficult technical investigation require different levels of effort. I want to resolve the uncertainty that matters before making a larger commitment, and keep what I learn available afterward.
Some applications are already part of my working practice. Others, including the industrial concepts and wider coordination between projects, are still developing. Each iteration should make the difference clearer and produce something I can use.
Build capability through completed cycles
The most promising connection between these applications is the learning that survives a finished task. A commercial investigation can reveal a technical question. Studying that question can improve the next supplier conversation. The resulting decision can then become part of a project record rather than disappearing into another conversation.
That cycle also gives the work a practical test. Did the research change a question, clarify an alternative or produce something ready for review? Could the next person understand the result without reconstructing all of the earlier exchanges? Those are the kinds of outcomes I want to examine as these methods develop. A polished document is useful, but the larger objective is stronger judgment and a greater ability to finish the next piece of work.