Base Case Fallback Bank
Base Case Fallback Bank#
Overview#
This document defines base case fallback scores for industries that do not receive explicit actions from participants in a given round.
Key Principles:
- Deterministic: Same fallback applies to all instances of an industry across all participants and rounds
- Small deltas: Typically +/-1 per dimension (not -2 to +2), creating scores in the -2 to +2 range
- Plausible: Represent defensive but reasonable moves (e.g., cost control, operational efficiency, maintain status quo)
- Automatic: Applied by facilitator directly; no participant input required
- Not participant-controlled: Fallback scores are fixed; participants cannot modify them by arguing or adjusting
When to apply: After scoring all explicit industry decisions, identify any industry (across all 11 industries) that received no explicit action. Apply its fallback score directly from this bank.
11 Industries (V7.4): Retail, CPG, Healthcare Provider, Healthcare Payer, Finance, Consulting, Law, Manufacturing, Logistics, Big Tech, B2B/B2C SaaS
Fallback Scores by Industry#
Retail (Omnichannel Retailers)#
Fallback Rationale: Maintain pilot-phase demand forecasting in select stores; optimize inventory within existing systems; no new capex or major staffing changes.
| Dimension | Score | Justification |
|---|---|---|
| Strategic Fit | 0 | Neutral move; maintains competitive position but doesn't capture opportunity |
| Execution Risk | +1 | Proven tech; existing infrastructure; low change management burden |
| Tail Risk | 0 | Pilot scope limits downside; if adoption slows, can pause |
| TOTAL | +1/9 | Defensive; preserves optionality |
CPG (Consumer Goods Manufacturer)#
Fallback Rationale: Accelerate minor R&D cycles using existing tools; minor marketing automation with human review; no DTC expansion.
| Dimension | Score | Justification |
|---|---|---|
| Strategic Fit | 0 | Neutral; cost efficiency moves, but doesn't drive growth |
| Execution Risk | +1 | Existing teams; minor process changes; low adoption risk |
| Tail Risk | 0 | Conservative scope; brand safety maintained via human review |
| TOTAL | +1/9 | Defensive operational efficiency |
Healthcare Provider (Hospital System, Clinical Care)#
Fallback Rationale: Deploy operational AI (scheduling, resource allocation); defer major clinical diagnostic AI; maintain standard clinical governance.
| Dimension | Score | Justification |
|---|---|---|
| Strategic Fit | 0 | Operational efficiency; doesn't drive clinical outcomes or revenue growth |
| Execution Risk | 0 | Operational AI (scheduling) proven; workflow integration standard; physician adoption manageable |
| Tail Risk | +1 | Operational scope limits patient safety risk; governance framework maintains liability protection |
| TOTAL | +1/9 | Defensive operational efficiency without clinical risk escalation |
Healthcare Payer (Health Insurer, Claims & Coverage)#
Fallback Rationale: Deploy fraud detection + payment integrity AI; continue prior authorization automation with human review; no aggressive claim denial.
| Dimension | Score | Justification |
|---|---|---|
| Strategic Fit | +1 | Fraud detection improves medical loss ratio; proven ROI |
| Execution Risk | +1 | Fraud detection proven; payment integrity automation standard; compliance clear |
| Tail Risk | 0 | Human review safeguards prevent denial backlash; regulatory MLR compliance maintained |
| TOTAL | +2/9 | Defensive cost-control with member satisfaction safeguards |
Finance (Bank + Insurance)#
Fallback Rationale: Continue current fraud detection arms race; invest in bias auditing and fair lending compliance infrastructure; no major new deployment.
| Dimension | Score | Justification |
|---|---|---|
| Strategic Fit | +1 | Defensive but strategically important; reduces regulatory and litigation tail risk |
| Execution Risk | +1 | Compliance infrastructure proven; existing vendor relationships |
| Tail Risk | +1 | Proactive compliance reduces regulatory backlash; enables future deployment |
| TOTAL | +3/9 | Defensive risk mitigation (higher than other industries due to systemic tail risk reduction) |
Consulting (Big Four / MBB)#
Fallback Rationale: Deploy copilots to research + analysis functions (proven use case); maintain junior hiring at current levels; defer vertical AI expertise build and pricing model changes.
| Dimension | Score | Justification |
|---|---|---|
| Strategic Fit | +1 | Copilots improve delivery efficiency; maintains junior talent pipeline |
| Execution Risk | +1 | Copilot adoption straightforward; existing vendor relationships (GitHub Copilot, internal tools) |
| Tail Risk | 0 | Junior hiring preserved; no aggressive commoditization; talent pipeline maintained; pricing stability |
| TOTAL | +2/9 | Defensive productivity move; preserves talent model and client relationships |
Law (AmLaw 50 Firm)#
Fallback Rationale: Pilot AI research tools (legal research, due diligence) in 2-3 practice groups with mandatory attorney review; maintain billable hour model; maintain current associate hiring levels; defer pricing model changes; monitor bar rule developments across jurisdictions.
| Dimension | Score | Justification |
|---|---|---|
| Strategic Fit | 0 | Conservative approach; captures minor efficiency gains but doesn't transform delivery model or address pricing pressure |
| Execution Risk | +1 | AI research tools proven (Harvey.ai, CoCounsel, Westlaw AI); attorney review protocol manageable; limited scope reduces adoption friction |
| Tail Risk | +1 | Mandatory attorney review eliminates malpractice exposure from AI-generated work; bar rule compliance maintained; billable model stable near-term; no associate displacement |
| TOTAL | +2/9 | Conservative but defensible; preserves current economics while building AI familiarity |
Manufacturing (Heavy Manufacturing)#
Fallback Rationale: Selective predictive maintenance in highest-ROI plants (pilot phase); continue "no-layoff" retraining commitment; no aggressive automation.
| Dimension | Score | Justification |
|---|---|---|
| Strategic Fit | 0 | Neutral; cost reduction at 2-3 plants, but not enterprise-wide transformation |
| Execution Risk | +1 | Predictive maintenance proven; phased OT/IT integration manageable |
| Tail Risk | +1 | Union cooperation maintained via "no-layoff" commitment; labor relations preserved |
| TOTAL | +2/9 | Conservative but labor-cooperative |
Logistics (Freight/3PL/Warehouse)#
Fallback Rationale: Deploy route optimization to subset of fleet (pilot); driver engagement program; defer autonomous vehicle decisions.
| Dimension | Score | Justification |
|---|---|---|
| Strategic Fit | +1 | Route optimization proven ROI; $45-50M annual savings potential (limited scope) |
| Execution Risk | +1 | Technology proven; driver adoption managed via engagement program |
| Tail Risk | 0 | Pilot scope; union concerns addressed; no autonomous displacement |
| TOTAL | +2/9 | Proven, labor-safe cost reduction |
Big Tech (Google/Meta/Microsoft/Amazon-Class — Cloud, Ads, Devices, Enterprise Software)#
Fallback Rationale: Continue incremental AI infrastructure investments for cloud and enterprise products; integrate AI into existing products cautiously; defer transformational new product launches. (Excludes foundation model development decisions.)
| Dimension | Score | Justification |
|---|---|---|
| Strategic Fit | +1 | Incremental AI product features maintain competitive position; defensive move in cloud and enterprise |
| Execution Risk | +1 | AI feature integration proven; existing infrastructure and talent; low risk |
| Tail Risk | 0 | Incremental scope limits regulatory/antitrust risk; margins preserved via cost management |
| TOTAL | +2/9 | Defensive infrastructure + product evolution |
B2B/B2C SaaS (Workday/Salesforce-Class)#
Fallback Rationale: Bundle AI copilots into standard product SKU; maintain pricing at current levels; defer AI-native product launches.
| Dimension | Score | Justification |
|---|---|---|
| Strategic Fit | 0 | Bundling maintains customer lock-in; doesn't drive premium pricing or market share |
| Execution Risk | +1 | AI feature bundling proven; existing infrastructure and vendor relationships |
| Tail Risk | 0 | Standard bundling avoids pricing backlash; customer churn risk manageable at current pricing |
| TOTAL | +1/9 | Defensive feature parity with margin pressure |
How to Apply Fallback Scoring#
Example 1: Participant Covering Retail + CPG Submits Only Retail Decision#
- Participant submits explicit decision: Retail (e.g., "Deploy demand forecasting to 500 stores, 90 days, $2M budget")
- Retail scored normally: (e.g., +2/9)
- CPG receives no explicit action
- Fallback applied: CPG fallback from bank = {0, +1, 0} = +1/9
- Both posted: Retail +2/9 (explicit), CPG +1/9 (fallback)
Example 2: Participant Covering Healthcare Provider + Payer Submits Only Provider Decision#
- Participant submits explicit decision: "Deploy clinical decision support with physician override; staff training"
- Healthcare Provider scored normally: (e.g., +2/9)
- Healthcare Payer receives no explicit action
- Fallback applied: Healthcare Payer fallback = {+1, +1, 0} = +2/9
- Both posted: Provider +2/9 (explicit), Payer +2/9 (fallback)
Example 3: Participant Covering Finance + Consulting + Law Submits Only Finance Decision#
- Participant submits explicit decision: Finance (e.g., "Deploy AI underwriting with explainability + bias audit")
- Finance scored normally: (e.g., +3/9)
- Consulting and Law receive no explicit actions
- Fallbacks applied: Consulting = {+1, +1, 0} = +2/9; Law = {0, +1, +1} = +2/9
- All three posted: Finance +3/9 (explicit), Consulting +2/9 (fallback), Law +2/9 (fallback)
Example 4: Minimum Model (5 Participants, Each Covering 2 Industries)#
Scenario: 5 participants, each assigned 2 industries. Each submits 1 explicit decision; 1 industry per participant falls back. 1 industry unassigned (auto-fallback).
- Participant A (Retail + CPG): Retail decision +2/9 -> CPG fallback +1/9
- Participant B (Healthcare Provider + Payer): Provider decision +1/9 -> Payer fallback +2/9
- Participant C (Finance + Consulting): Finance decision +3/9 -> Consulting fallback +2/9
- Participant D (Manufacturing + Logistics): Manufacturing decision +1/9 -> Logistics fallback +2/9
- Participant E (Big Tech + SaaS): Big Tech decision +2/9 -> SaaS fallback +1/9
- Law (unassigned): Auto-fallback +2/9
Fallback Summary Table (11 Industries)#
| Industry | Fallback Strat Fit | Fallback Exec Risk | Fallback Tail Risk | Fallback Total | Rationale |
|---|---|---|---|---|---|
| Retail | 0 | +1 | 0 | +1 | Pilot-phase demand forecasting |
| CPG | 0 | +1 | 0 | +1 | Minor R&D + marketing automation |
| Healthcare Provider | 0 | 0 | +1 | +1 | Operational AI (scheduling) + governance |
| Healthcare Payer | +1 | +1 | 0 | +2 | Fraud detection + prior auth with human review |
| Finance | +1 | +1 | +1 | +3 | Compliance + fraud defense infrastructure |
| Consulting | +1 | +1 | 0 | +2 | Copilot deployment + talent preservation |
| Law | 0 | +1 | +1 | +2 | Pilot AI research tools + maintain billable model + attorney review |
| Manufacturing | 0 | +1 | +1 | +2 | Selective predictive maintenance + union cooperation |
| Logistics | +1 | +1 | 0 | +2 | Route optimization pilot + driver engagement |
| Big Tech | +1 | +1 | 0 | +2 | Incremental AI infrastructure + product features |
| B2B/B2C SaaS | 0 | +1 | 0 | +1 | AI copilots bundled into standard product |
Facilitator Checklist: Applying Fallbacks#
After scoring all explicit decisions:
- Identify industries across all participants that received no explicit action
- For each fallback industry, reference the table above and note the deterministic fallback score
- Post fallback score for each industry (e.g., "Law falls back to +2/9 -- pilot AI research tools, maintain billable model")
- Post all scores with transparency (show explicit vs. fallback breakdown per industry)
- Update cumulative scores and look up Industry Health conditions (reference Industry Health Signal Tables)
Important: Do NOT negotiate fallbacks. They are deterministic. Do NOT allow participants to argue for higher/lower fallbacks. If a participant wants to improve an industry's score, they must submit an explicit decision in the next round.
Reference: Use this bank during scoring (Adjudication Rules). Participants consult this implicitly during decision planning (see Overview and Runbooks for round structure).