Multi-Domain Problem Solving
Boyd's strategic theories (1994) + 23 years military + 15 years systematic study + LLMs as latest tool = 30-year proven methodology for complex operational challenges.
This approach to complex problem-solving was built over three decades, not invented with LLMs.
The Core Methodology: Boyd Since 1994
John Boyd's "Destruction and Creation," "Patterns of Conflict," and "Strategic Game" have shaped my approach to complex problems for 30 years—since West Point in 1994. LLMs are just the newest tool for applying these time-tested principles.
ChatGPT launched November 2022. My problem-solving foundation was established 28 years earlier.
Since 1994, I've applied John Boyd's "Destruction and Creation" methodology to solve complex problems across domains. This is not theory—it's a 30-year practice of breaking apart concepts and synthesizing new solutions from the pieces.
Boyd's approach asks: What lies hidden under the question marks? To find the answer, you must examine problems from multiple perspectives and synthesize patterns across domains.
Examine the problem from multiple perspectives. Look across different domains—not just within one field. Ask: What is strategy? What is the aim? What are the key ideas?
Break apart concepts into constituent parts (Destructive Deduction). Identify the core components that matter. Strip away the non-essential.
Synthesize patterns ACROSS domains (Creative Induction). Find common qualities and link pieces together in new ways. Test for consistency and match with reality.
Apply the synthesized solution in real environment. Measure outcomes. Feed results back into the cycle. Repeat: Survey → Condense → Synthesize → Implement.
Boyd's 4-step Strategic Game is strengthened by three complementary frameworks that address different aspects of organizational complexity:
Technology is BOTH the product of human action AND the medium that structures human action. This duality explains why technology-first approaches fail: deploying AI tools alone doesn't work because technology must be appropriated by users within their organizational context.
The "science of common sense" for beings who "are not ants, or birds." Humans are homo-faber (makers), homo-ludens (players), and homo-narrans (storytellers). The 3Is—Intelligence (reflection, abstraction), Intentionality (purpose, choice), Identity (how we "show up")—cannot be reduced to computational models.
Strategic mapping visualizes the competitive landscape and component evolution. Maps value chains on an evolution axis (genesis → custom → product → commodity) to identify strategic opportunities, anticipate market changes, and reduce risk by understanding dependencies.
Step 1 (General Survey) requires examining problems from multiple perspectives. These are the five domains I draw from:
Each domain provides "constituents" that can be broken apart (Step 2) and synthesized across domains (Step 3).
Survey: Examine production bottlenecks from multiple angles.
Condense: Break problem into mission-critical elements.
Synthesize: Apply 2/75 mission planning + intelligence targeting concepts.
Implement: Execute, measure, refine.
769 books across domains (Drucker, Lean, complexity, AI/ML) provide the "constituent library" for Step 1 (Survey) and Step 3 (Synthesis). Each domain offers pieces that can be recombined.
LLMs speed up all four steps: faster survey of perspectives, rapid condensation, quicker pattern testing, and accelerated iteration. But the 4-step methodology remains unchanged since 1994.
The Division of Labor Philosophy
Humans excel at the 3Is: Intelligence (reflection, abstraction), Intentionality (purpose, choice), Identity (how we show up). We navigate complexity, synthesize across domains, and make judgment calls that matter.
LLMs and AI tools accelerate the 4-step cycle: faster survey, rapid condensation, quicker pattern testing, accelerated iteration. They compute at scale but don't replace human sense-making.
The Snowmobile: A human-machine hybrid where the human steers (sense-making, judgment, cross-domain synthesis) and the machine provides power (computation, acceleration, scale). This division of labor is the key to effective AI adoption. Not replacing humans with AI—augmenting human sense-making with computational power.
"Humans are not ants, or birds. We make things, play, and tell stories. Machines help us do it faster."
Diverse experience across military, manufacturing, and continuous learning creates unique problem-solving perspectives.
Engineering and leadership foundation
5 years, multiple combat deployments including Operation Red Wings recovery
HUMINT analysis, targeting, counterintelligence, pattern recognition
Systematic analysis, decision-making under uncertainty, structured problem-solving
Koch Industries: Analyst → Shift Coach → GenAI Champion
Oregon State Athletics, Soldiers to Sidelines: coaching, entrepreneurship, veteran transition
Lean principles, continuous improvement, cross-domain knowledge transfer
3,000+ pages capturing operational reality, pattern recognition at scale
A 2-year AI-enabled organizational transformation at Georgia-Pacific Monticello paper mill, demonstrating Boyd's 4-step methodology from initial survey to $546M enterprise-scale opportunity.
Examined paper mill operations from multiple perspectives: manufacturing processes, training systems, leadership development, organizational culture, AI technology landscape, and operator knowledge capture.
Broke problem into core components: downtime patterns, operator knowledge gaps, documentation needs, adoption barriers, cultural resistance. Identified key question: "Can we use the Kata?" (Toyota improvement methodology). Condensed to mission-critical insight: Zero adoption in technology-first approach.
Synthesized patterns ACROSS domains to create new approach. Cross-domain synthesis: 2/75 Ranger after-action reviews → Shift knowledge capture system. Mission Command operator empowerment → Bottom-up AI adoption. TPS "respect for people" → Operator memory scaffolding (not replacement). Boyd's OODA loop → Real-time operational learning.
Embedded as Shift Leader to capture real data. Documented 1,408.7 hours of downtime across all shifts. Quantified $64M annual opportunity at Monticello. Scaled to enterprise level: $546M/year potential across 400 sites. Validated with live field data, not simulations. "No Capex. No Pilot. Just Recovered Execution."
Interested in discussing cross-domain problem-solving approaches? Want to learn more about the methodology? Let's talk.
Current Focus: Sharing practical approaches to multi-domain problem-solving, building teachable methodologies, and demonstrating how decades of systematic work create unique capabilities.