The Laws of Nature Governing Business
With Author and Turn-Around Expert, Clayton Williams
Clayton Williams, author of What Strategy Is: The 18 Fundamental Laws of Strategy, explains strategies in the business world using natural parallels.
What early experiences made you decide to treat strategy as a system rather than a slogan?
I grew up between two worlds: the Kruger, where I learned how living systems keep balance, and the corporate world, where I saw how easily human systems lose it. My mother’s fieldwork in nature conservation trained me to notice patterns of adaptation; flying as a teenager taught me how fast those patterns collapse when one element is out of sync. Later, while running a flying school and then during several turnarounds, I discovered that organizations behave exactly like ecosystems: coherence, rhythm, and feedback decide whether they survive. That’s why my book, What Strategy Is: The 18 Fundamental Laws of Strategy, treats strategy as a living system rather than a management slogan.
How do you embed sustainability as a hard design constraint—without turning it into PR theatre?
Treat sustainability as a survival constraint, not a slogan. In my book I call this Law 16: Survival as a Necessary Condition—if an organization can’t survive regulatory shocks, talent expectations, supply-chain scrutiny, or customer trust erosion, nothing else matters. So the move is simple: translate “initiatives” into survival metrics tied to cash durability and license-to-operate—in other words, exposure to compliance discontinuities, conversion penalties from trust loss, or margin drag from waste. Fold those into your operating cadence the way you’d treat uptime: measured, budgeted, defended. When sustainability tightens survival odds, it’s strategy; when it doesn’t, it’s marketing theatre.
In a turnaround, how do you quickly stem the ruptures in the system?
Strategy sits at the system level; people act at the actor level. The bridge is engineered behaviour. In the book this shows up as Law 14 (goal-maintaining vs goal-achieving), Law 17 (choose the right unit of analysis), and Law 18 (align external positioning with internal structure). Practically: define a handful of system KPIs that express the turnaround thesis (cash conversion days, activation→retention lift, unit economics at the right resolution), redesign SOPs so work routes through the behaviors that protect those KPIs, and update delegation and approvals so frontline decisions can keep the organism “goal-maintaining” under pressure rather than locally optimized but systemically harmful. If your metrics, SOPs, and decision rights don’t cohere, your campaigns will cannibalize the strategy.
How do you use information of uncertain value to spend less and earn more?
Don’t start with the data! Start with the decision that you need to make and always work backwards: decision → insight → analysis → data. Suppose churn is climbing in your self-serve plan. The decision is whether to push a pricing change or an onboarding change. The insight you need is which behavioral bottleneck dominates: failed aha-moment or price-sensitivity at first bill. Then specify the analysis (cohort survival curves split by first-week activation events), and only then pull the data (event logs, invoice timings, plan tiers). Run an A/B that changes the onboarding path for “no-aha” cohorts and price-tests the rest. Profit moves when the decision is well-posed; starting with whatever data you have leads to elegant dashboards that answer nothing. (This design discipline mirrors Law 17’s insistence on the right resolution for the question at hand.) People who start with the data, do what they can with the data, and typically come up with interesting insights that inform nothing.
Segmentation is a modeling choice, not a persona exercise—the category must explain variance in results, or it isn’t a segment.
How do you choose the right resolution and unit of analysis for segmentation?
Start where Law 17 (resolution & unit of analysis) points you: Segmentation only earns its keep if the segment variable is an independent variable that changes the outcome you care about. Most “segments” are spurious because they’re picked for convenience (demographics, broad industries) rather than because they move conversion, payback, or retention. The practical move is to derive segments from your value drivers—the causal levers in your value-driver tree—so the split reflects different mechanisms of value creation (e.g., problem severity, switching cost, activation trigger, compliance burden, purchase urgency), not different labels. Then test it: if creative, offer, channel, or onboarding produce different lifts by segment, you’ve found the right resolution; if they don’t, collapse the segmentation and keep searching. In short, segmentation is a modeling choice, not a persona exercise—the category must explain variance in results, or it isn’t a segment.
How do you architect an organization to function strategically?
Make exogenous logic (your market promise) and endogenous logic (how you’re built) say the same thing—this is Law 18. Then guard against the drift where teams optimize locally (Law 14) and quietly undermine the system. Concretely translate positioning claims into operational commitments—response-time SLAs, offer architectures, eligibility rules, post-sale handoffs—and put those into the CRM and SOPs so they’re enforced by default. Compensation, SLA dashboards, and approval paths should reward the behaviors that keep the promise and keep the organism coherent. If your brand says “fast and clear,” but legal approvals take nine days, alignment is impossible no matter how good the creative is.
SUBSCRIBE TO OUR FREE NEWSLETTER
What do sales and marketing often get wrong about finance’s role?
Finance too often get their own role wrong. Finance matches costs and revenues to the period they appear, not to the event that caused them—which garbles marketing’s economics. Strategy punishes that mistake because order and timing matter (Law 13): actions reshape future option sets and payoffs. Use causal, resolution-aware attribution instead: attribute acquisition spend to the cohorts and paths it actually changes, capitalise or amortise where the value truly unfolds, and make overhead earn its way in under a Discrete Costing Rule (only include a cost at a resolution if it exists because that unit exists). That’s the difference between pretty reports and decisions you can price, scale, and defend.
How do you maintain momentum when the landscape is in upheaval?
Name the uncertainty, then instrument the learning. In the framework, you embrace uncertainty and dynamism (Law 5 & Law 7), reduce it with tight learning loops (Law 6 & Law 8), and refuse to demand certainty the system can’t yet supply (Law 9). In practice: publish a short list of live unknowns; run weekly experiments that directly attack them; measure “questions retired per week” alongside revenue; and reward teams for closing uncertainty, not just hitting outputs. That simple shift keeps people energised because progress is visible even when the market is choppy
What’s the AI mistake strategists keep making?
It’s too broad to answer responsibly. My work starts with the decision and the uncertainty—what must be decided, what evidence would change that decision, and where uncertainty sits in the value path. Sometimes AI is the sharpest tool for that; sometimes it’s better data plumbing, better incentives, or better ops. I’m wary of sweeping AI generalisations because they skip the question that actually makes money: which decision, what evidence, which behaviour changes now?
If you can define problems cleanly, design lean tests, and update your beliefs without ego, you’ll outperform flashier résumés.
How do you become a strategy engineer as opposed to someone who just builds decks?
Master the meta-skills: learn how to learn, reason from first principles, and turn learning into small, daily application. Turbulence isn’t a glitch; it’s the landscape you’ll always work on. If you can define problems cleanly, design lean tests, and update your beliefs without ego, you’ll outperform flashier résumés. That’s the spirit of the book: strategy as the craft of reducing uncertainty into advantage—one well-posed decision at a time.…
Share this on:
About Clayton Williams
Clayton Williams is the author of What Strategy Is: The 18 Fundamental Laws of Strategy and the researcher behind QCEA-T, a simulation-native theory that treats strategy as a complex-adaptive process grounded in information, entropy, and feedback. A former pilot and turnaround CEO, he now helps organisations build “strategy organisms” that preserve coherence in uncertainty. His work spans academic theory and field diagnostics, unifying rigor with execution.