Stress-test strategic decisions by modeling best, worst, and likely scenarios using assumptions and risks surfaced in team discussions. Identifies trigger points for plan B and builds contingency playbooks.
Leadership teams make big bets—market expansion, pricing changes, headcount plans, product pivots—but rarely stress-test the assumptions behind them. "What if enterprise sales take 50% longer than projected?" surfaces in a pipeline review but nobody models the downstream impact on cash runway, hiring plans, and customer commitments. This skill captures the assumptions and risks discussed across meetings, builds structured best/worst/likely scenarios with financial and operational implications, and identifies the specific trigger points that should activate contingency plans. The result is a decision brief that lets leadership make bets with eyes open, knowing what could break their plan and what to do when it does.
Extract assumptions from recent leadership discussions: Identify the 3–5 core assumptions driving your decision (sales targets, hiring timelines, customer adoption rates, market timing, competitive dynamics, retention assumptions). Source them: "In the Feb pipeline review, you said 60% conversion by Q3. In the board prep call, you mentioned 14-month enterprise sales cycle as a risk. In Slack, you noted we might lose 2 key hires to startup offers." List each assumption, its source, and the date.
Identify the decision being stress-tested and its key variables: What is the bet? "Expand into European market." What are the top 3 variables that make or break it? (Enterprise sales cycle length, regulatory approval timeline, hiring velocity for London office.) What is the default plan if all goes well?
Build three scenarios:
For each scenario, model the impact: Revenue (how much does slower sales affect annual ARR?), cash runway (how many months do you have if this scenario hits?), team capacity (can the core product team support the new market while hiring?), customer impact (do existing customers get deprioritized?), timeline shifts (what launches slip or delay?).
Identify trigger points: Specific, observable signals that tell you which scenario is unfolding. (Example: "If we have fewer than 3 EU enterprise pipeline meetings by April 15, we're tracking worst case. If we hit 6+, we're in best case.") Triggers should be measurable by end of month or quarter, not vague.
Design contingency actions for each trigger point: When you see the trigger, what do you do? Who decides? How fast? (Example: "Trigger: <3 EU meetings by April 15 → Decision: Partner-led entry instead of direct sales. Owner: VP Sales + CEO. Timeline: 2 weeks to activate.") Include: what to do, who owns it, what it costs, how it changes the plan.
Stress-test the stress test: What scenario did we not consider? (e.g., "We modeled sales cycle but not customer implementation complexity." "We didn't model losing a key investor relationship.") What's the true worst case—the one you're not comfortable saying out loud? Add it.
Ridgeline (Series B SaaS, 120 people) — Decision: Expand into European market
Assumption Registry:
Scenarios:
Best Case (30% confidence): Sales cycle 5 months, regulatory approval 2 months, hire 1.5 people/month, 92% retention → Year 2 ARR from EU: $800k. Cash runway: 18 months. "We hit it right. EU prospects move fast. We get first 3 deals in Q2/Q3, use them as reference. London team ramps quick."
Base Case (50% confidence): Sales cycle 9 months, regulatory approval 4 months, hire 1 person/month, 90% retention → Year 2 ARR from EU: $200k. Cash runway: 16 months. "Standard enterprise sales. Regulatory is normal. We miss 2 hires in first 6 months. We invest $400k in Year 1, break even in Year 2."
Worst Case (20% confidence): Sales cycle 14 months, regulatory approval 6 months, hire 0.5 people/month + lose 2 people mid-year, 85% retention → Year 2 ARR from EU: $50k. Cash runway: 13 months. "Market moves slower. Regulatory surprises. We burn $550k in Year 1 and don't hit break-even until Year 3. Product team gets distracted supporting new market. We lose 2 senior people because London hiring is slow and they're needed at SF HQ."
Trigger Dashboard:
Trigger: <3 EU enterprise pipeline conversations by May 31 → Scenario: Worst case
Trigger: Regulatory approval not in hand by August 1 → Scenario: Worst case
Trigger: London office filled to 5+ people by October with <2 departures → Scenario: Best/Base case
Contingency Playbook: (if worst case triggers)
Blind Spot Check:
Too many variables to model: Narrow to the top 3 that move the needle most. (Don't model 15 variables if 3 account for 80% of the outcome.)
All scenarios look bad: Present that honestly. "All paths require we cut headcount, extend runway, or raise capital." Recommend the least-bad path and the triggers that matter most.
Optimism bias from leadership: "The founder says sales cycle will be 4 months." Anchor to data: "Analyst research shows 9 months. Our past deals averaged 8 months. We recommend 9 months as base case." Give leadership permission to be optimistic in best case, but not in base case.
Fast-moving crisis where scenarios change weekly: Scenario analysis is forward-looking. In a crisis, assume it's base case or worse and act fast. Re-run scenario analysis monthly, not weekly.
Scenario planning for product decisions: This skill works for feature bets too ("Launch AI copilot vs. invest in integrations"). Model user adoption, retention impact, engineering capacity, revenue upside, and contingency if the feature flops.
Present as decision brief: 1-page recommendation + three 1-page scenario models + one-page trigger/contingency dashboard. Recommendation should say: "We recommend [decision] because [reason]. Best case delivers [outcome]. Base case is [outcome]. Worst case is [outcome]. We monitor [triggers] and activate contingency [if/when]. Board should know: [one scary thing]."