Thinking Outside the Box series
AI doesn’t “think outside the box”—it explores the box you define, tirelessly and without ego. That’s the power—and the trap.
The critical misunderstanding
Myth: “AI will think outside the box for us.”
Reality: AI amplifies your search-space mathematics—∩ or ∪—based on how you frame the problem.
Implication: Draw a narrow box and AI maps it thoroughly. Draw an expansive box with movable soft walls and AI explores possibilities you’d never manually examine. Your box definition determines everything.
Human–AI roles (be explicit)
Human (authority): set intent; freeze hard walls (law, safety, physics, privacy); list soft walls (policies, habits, vendor defaults); define the deciding metric; apply domain feasibility.
AI (team): generate systematic variants; probe soft walls; propose orthogonal options (counterfactuals, cross-domain analogies); surface contradictions and emergent possibilities; document exhaustively (great for Scientific Research & Experimental Development (SR&ED) trails).
Critical gap: AI has no initiative or “good enough” instinct—if you don’t ask, it doesn’t explore.
Why “tell me something about…” isn’t strategy
AI can hand anyone novelty on demand. That’s easy—and insufficient for problem solvers, strategic thinkers, and leaders.
Casual prompts produce: curiosity, lists, and cool ideas—without boundary control, mechanism, or test.
Strategic prompts create:
- A designed box (intent + hard walls + soft walls)
- Mechanisms, not opinions (TRIZ moves tied to each soft wall)
- Evidence loops (deciding metric + kill criterion + minimal design of experiments—DOE)
Swap this…
“Tell me something about speeding up our pipeline.”
For this…
“IFR: sub-1s latency. Hard walls: privacy law, current sources. Soft walls: batch-only, single region, ETL order. Generate 10 options, each tied to a TRIZ mechanism and a minimal test with a deciding metric and kill criterion.”
Bottom line: Anyone can get an “outside-the-box” idea from AI. Leaders architect the box, name the tensions, demand mechanisms, and move on evidence.
The AI prompt is the box
Narrow box (accidental ∩):
“How can we improve our current data-pipeline performance?”
→ AI optimizes the existing architecture; misses reframes like “what if the pipeline disappears?”
Expanded box (intentional ∪):
“Our data pipeline has 3-second latency. Hard walls: privacy laws, existing sources. Soft walls: current architecture, batch assumption, single-region deploy. Generate 10 approaches that each challenge a different soft wall. Tag walls hard/soft. For each, identify the TRIZ mechanism and the minimal test.”
→ AI explores streaming vs batch (Dynamics), edge vs central (Local Quality), prediction vs processing (Pre-action), and architecture alternatives, not just optimizations.
Practical AI–human protocol (for ∪)
Phase 1 — Human box definition (5 min)
Ideal outcome (1 line) · hard walls · soft walls · deciding metric + kill criterion.
Phase 2 — AI exploration
Generate 15–20 variants; apply TRIZ operators to each soft wall; add cross-domain analogies; propose falsifiable minimal tests.
Phase 3 — Human filter (10 min)
Apply feasibility; pick 2–3 most testable hypotheses; design evidence collection.
Phase 4 — Document for SR&ED
AI captures the trail: hypothesis → mechanism → test design (DOE) → evidence → decision.
Guardrails (so AI helps, not overwhelms)
Without structure: hundreds of disconnected ideas, poor contradiction coverage, signal lost in noise.
With TRIZ-informed prompts: specific operators → specific constraints; systematic coverage; each idea tied to mechanism + minimal test + deciding metric.
Prompt scaffold:
“Apply [Separation in Time/Condition • Local Quality • Intermediary • Parameter Change] to [soft wall S]. Return: mechanism name, how it resolves the tension, minimal test this week, deciding metric, kill criterion.”
Why this matters for R&D leaders
- Bias mitigation: AI proposes options humans skip (anchoring, status-quo bias).
- Search-space expansion: expert intuition + AI coverage → larger ∪.
- SR&ED advantage: a timestamped, complete exploration trail demonstrating systematic uncertainty and investigation.
Bottom line
AI doesn’t replace judgment—it amplifies your math.
Draw a narrow box → AI maps it thoroughly (∩).
Draw an expansive box with testable walls → AI reveals emergent possibilities (∪).
The breakthrough isn’t “in the AI”—it’s in how skillfully you frame exploration.
👉 Comment COPILOT for the TRIZ-guided AI Prompt Template (wall tags, mechanisms, and test scaffolds).
— About the author: Innovation & SR&ED advisor. I use AI + modern TRIZ to help IT and manufacturing teams turn constraints into disciplined breakthroughs—and SR&ED tax credits.
#Innovation #AI #TRIZ #Leadership #Brainstorming #Strategy #RnD #SRandED