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Facilitator Guide
Scoring Baselines (Industry-Level)
Scoring Baselines (Industry-Level)#
Overview#
This section provides scoring baselines for the eleven industries in V7.5. Use these as calibration anchors when scoring participant decisions on Strategic Fit and Execution Risk.
V7.5 Scoring Changes:
- Tail Risk dimension removed. Tail Risk content in industry tables below is preserved as informational context — facilitators use it to identify red-flag candidates and inform Strategic Fit calibration, but it is no longer a separately scored dimension.
- Round score range: -6 to +6 (Facilitator: Strategic Fit ±2 + Execution Risk ±2 + Peer Success ±1 + Peer Impact ±1)
- Peer ranking now adjusts every decision via Most likely to succeed, Greatest impact, and Most aggressive (read-only)
- Strategic Archetypes: Mandatory selection on every worksheet — Labor Reshape / Process Reinvention / Customer/Product Bet / Defensive Hardening / Strategic Swing
- Total scores in legacy "/9" notation in tables below should be read as approximate facilitator-only directionality; new round totals are /6
Other context:
- 11 industries; each scored independently in its own context
- Big Tech scope narrowed: cloud, ads, devices, enterprise software (excludes AI lab/model development)
- Base case fallbacks: see Base Case Fallback Bank
Band-to-Score Translation Reference#
Use this table to quickly map banded inputs to expected score ranges:
| Spend | Time | Complexity | Dependency | Scale | Typical Strategic Fit | Typical Exec Risk | Typical Tail Risk | Typical Total |
|---|---|---|---|---|---|---|---|---|
| Absorbable | 0-3mo | Low | Internal | Pilot | +1 | +2 | +1 | +4 |
| Absorbable | 0-3mo | Medium | Internal | Regional | 0 | +1 | 0 | +1 |
| Material | 3-12mo | Low | Internal | Regional | +1 | +1 | 0 | +2 |
| Material | 3-12mo | Medium | Vendor | National | 0 | 0 | -1 | -1 |
| Transformational | 1-2yr | Medium | Vendor | National | +1 | -1 | -1 | -1 |
| Transformational | 1-2yr | High | Regulator | National | +1 | -2 | -2 | -3 |
| Transformational | 2+yr | High | Ecosystem | Global | 0 | -2 | -2 | -4 |
| Existential | 2+yr | Very High | Ecosystem | Global | -1 | -3 | -3 | -7 |
How to use: If a participant proposes a decision with bands matching one of these rows, the typical score range provides an anchor. Decisions better-executed than baseline get higher scores; worse-executed get lower scores.
Industry 1: RETAIL (Omnichannel Retailers, ~500 stores + e-commerce)#
Strategic Priorities (2026-2030):
- Deploying AI demand forecasting and inventory optimization (margin defense)
- Personalizing customer experience (conversion improvement, brand trust risk)
- Automating supply chain and logistics (cost reduction)
- Competing with Amazon and tech-native direct-to-consumer
Scoring Ranges#
| Dimension | Range | Typical | Notes |
|---|---|---|---|
| Strategic Fit | -2 to +2 | 0 | High if catching up on operational AI (inventory, forecasting); neutral if consumer-facing personalization |
| Execution Risk | -1 to +2 | 0 | Demand forecasting is proven; personalization at scale requires brand safety discipline |
| Tail Risk | -1 to +2 | 0 | Labor backlash if aggressive automation; brand backlash if personalization feels invasive |
Example Decisions (Retail)#
| Decision | Strat Fit | Exec Risk | Tail Risk | Total | Rationale |
|---|---|---|---|---|---|
| Deploy AI demand forecasting + inventory optimization (500 stores, pilot) | +2 | +1 | +1 | +4 | Proven tech; phased; catches up to Amazon; low brand risk |
| Launch omnichannel personalization with transparency/opt-in | +1 | 0 | +1 | +2 | Customer trust managed; execution feasible; brand safe |
| Aggressive dynamic pricing + personalization; brand as efficiency leader | +2 | 0 | -2 | 0 | High margin upside; high brand backlash risk if customers perceive discrimination |
| Cut 30% retail labor; deploy autonomous checkout + fulfillment | 0 | -2 | -2 | -4 | Labor relations damaged; union negotiation required; ROI uncertain |
Industry 2: CPG (Consumer Goods Manufacturer, 35 brands)#
Strategic Priorities (2026-2030):
- R&D acceleration: reduce product development cycles from 18-24 months to 12-15 months (time-to-market advantage)
- Marketing automation + AI-generated content: reduce marketing spend from 8.2% to 7.0% of revenue (margin gain ~60 bps)
- Demand sensing + supply chain optimization: improve forecast accuracy, reduce inventory, optimize production
- Direct-to-consumer (DTC) expansion: bypass retailers, own customer relationship, premium pricing
- Brand safety: manage AI-generated content backlash; maintain brand equity
Scoring Ranges#
| Dimension | Range | Typical | Notes |
|---|---|---|---|
| Strategic Fit | -1 to +2 | 0 | High if R&D acceleration or brand-safe marketing efficiency; neutral if aggressive DTC (retailer relations risk) |
| Execution Risk | -1 to +1 | 0 | R&D AI is proven; DTC is harder (retailer retaliation, supply chain complexity) |
| Tail Risk | -1 to 0 | -0.5 | Brand safety risk if AI-generated content backfires; retailer retaliation if DTC too aggressive |
Example Decisions (CPG)#
| Decision | Strat Fit | Exec Risk | Tail Risk | Total | Rationale |
|---|---|---|---|---|---|
| Accelerate R&D with AI ideation + formulation support (pilot: 10 product lines) | +2 | +1 | 0 | +3 | Proven ROI (6-month cycle reduction); manageable execution; low brand risk |
| Deploy AI marketing automation + AI-generated copy (with human review); target -60 bps marketing spend | +1 | 0 | +1 | +2 | Cost efficiency proven; human review manages brand safety risk |
| Launch aggressive DTC with AI personalization + dynamic pricing; bypass retailer distribution | +2 | -1 | -2 | -1 | Revenue opportunity; but retailer relationships damaged; private-label retaliation likely |
| Build "no AI" positioning: emphasize human-crafted products, authentic storytelling | -1 | +1 | +1 | +1 | Defensive; low execution risk; but misses R&D acceleration opportunity |
Industry 3: HEALTHCARE PROVIDER (Hospital System, Clinical AI & Operations)#
Strategic Priorities (2026-2030):
- Clinical diagnostic AI (radiology, pathology, cardiology) with FDA approval
- Clinical decision support (reduce medical errors, improve outcomes, physician workflows)
- Operational AI (scheduling, resource allocation, supply chain within hospital)
- Care coordination + risk stratification (population health, EHR integration)
Scoring Ranges#
| Dimension | Range | Typical | Notes |
|---|---|---|---|
| Strategic Fit | -1 to +2 | 0 | High if improving patient outcomes + reducing costs; moderate if defensive/cautious on unproven tech |
| Execution Risk | -2 to 0 | -1 | FDA regulatory approval adds 12-24 months; physician adoption uncertain; EHR integration complex |
| Tail Risk | -3 to 0 | -1 | Patient safety liability if AI error leads to harm; physician autonomy risk; malpractice exposure |
Example Decisions (Healthcare Provider)#
| Decision | Strat Fit | Exec Risk | Tail Risk | Total | Rationale |
|---|---|---|---|---|---|
| Deploy clinical decision support (diagnosis suggestions with physician override); staff training | +2 | -1 | +1 | +2 | Clinical value; physician autonomy preserved; adoption uncertain without workflow redesign |
| Initiate FDA pre-submission for diagnostic radiology AI; plan external validation; 12-month timeline | +1 | -1 | 0 | 0 | Strategic value; long regulatory timeline; patient safety covered; early FDA engagement reduces risk |
| Deploy operational scheduling + resource AI; pilot in 3 departments; monitor physician/staff adoption | +1 | 0 | +1 | +2 | Operational efficiency; lower clinical risk; physician workflow disruption manageable |
| Build clinical validation and governance infrastructure upfront (before major diagnostic AI) | 0 | +1 | +1 | +2 | Defensive; enables faster future diagnostic AI deployment; reduces liability risk |
Industry 4: HEALTHCARE PAYER (Health Insurer, Claims & Coverage AI)#
Strategic Priorities (2026-2030):
- Prior authorization automation (claims cost reduction, liability management)
- Fraud detection + waste reduction (claims review AI, payment integrity)
- Coverage determination + medical policy AI (actuarial models, policy logic)
- Risk stratification + population health (predictive analytics, preventive care targeting)
Scoring Ranges#
| Dimension | Range | Typical | Notes |
|---|---|---|---|
| Strategic Fit | 0 to +2 | +1 | High if improving medical loss ratio (MLR) + reducing administrative costs; moderate if defensive |
| Execution Risk | -1 to +1 | 0 | Prior auth automation proven; fraud detection is arms race; regulatory compliance adds complexity |
| Tail Risk | -2 to +2 | 0 | Denial rate increase + litigation risk; member backlash if coverage perception erodes; regulatory penalty risk (medical loss ratio limits) |
Example Decisions (Healthcare Payer)#
| Decision | Strat Fit | Exec Risk | Tail Risk | Total | Rationale |
|---|---|---|---|---|---|
| Deploy prior authorization AI; mandatory human review for high-cost/rare procedures. Phased rollout | +1 | 0 | +1 | +2 | Proven ROI (15-20% claim speed improvement); human safeguards manage denial litigation; clear pathway |
| Deploy fraud detection + waste reduction AI; automated payment integrity for high-variance claims | +2 | +1 | 0 | +3 | High strategic fit (cost reduction); proven technology; manageable execution; low tail risk |
| Deploy risk stratification AI + predictive models for preventive care targeting; partner with providers | +1 | 0 | +1 | +2 | Population health upside; execution depends on provider cooperation; reduces adverse selection risk |
| Aggressive claim denial automation (>70% auto-denials); minimize human review | 0 | +1 | -2 | -1 | Cost reduction upside; high denial litigation + regulatory backlash risk; member satisfaction damage |
Industry 5: FINANCE (Bank + Insurance, Underwriting & Trading)#
Strategic Priorities (2026-2030):
- AI underwriting + fraud detection (core profitability levers)
- Synthetic fraud detection (arms race; ongoing investment required)
- Fair lending compliance (regulatory scrutiny; accuracy/explainability trade-off)
- Back-office optimization (claims, KYC/AML, document review)
Scoring Ranges#
| Dimension | Range | Typical | Notes |
|---|---|---|---|
| Strategic Fit | -1 to +3 | +1 | High if improving underwriting ROI with compliance safeguards; moderate if defensive/cautious |
| Execution Risk | -2 to +2 | -1 | Regulatory approval adds timeline; fair lending audits add complexity; talent availability varies |
| Tail Risk | -3 to +2 | -1 | High tail risk (systemic, discrimination litigation, regulatory backlash) |
Example Decisions (Finance)#
| Decision | Strat Fit | Exec Risk | Tail Risk | Total | Rationale |
|---|---|---|---|---|---|
| Deploy AI underwriting (with explainability requirements); human review for denials >threshold | +2 | 0 | +1 | +3 | Core profitability; human safeguards; regulatory pathway clear |
| Deploy next-gen synthetic identity fraud detection (partnership with specialized vendor) | +1 | 0 | 0 | +1 | Defensive (staying in arms race); proven technology; manageable cost |
| Deploy AI trading; autonomous for 80%+ trades; minimal oversight | +2 | +1 | -3 | 0 | High profit upside; systemic risk if model fails; regulatory backlash certain |
| Invest in bias auditing + fair lending compliance infrastructure upfront | +1 | +1 | +1 | +3 | Defensive; reduces regulatory penalty risk; enables faster future deployment |
Industry 6: CONSULTING (Big Four / MBB Archetype, $20-25B Revenue, 40K Employees)#
Strategic Priorities (2026-2030):
- Copilot deployment in delivery (research, analysis, proposal development, client presentations)
- Vertical-specific AI expertise practices (Financial Services AI, Healthcare AI, Manufacturing AI)
- Premium AI governance + compliance offerings (regulatory uncertainty, high-margin)
- Talent repositioning (junior analysts -> complex, judgment-intensive work; AI handles routine analysis)
- Pricing model transition (value-based, outcome-based; away from time-and-materials)
- Competing against AI-enabled boutiques and client in-house AI capabilities
Scoring Ranges#
| Dimension | Range | Typical | Notes |
|---|---|---|---|
| Strategic Fit | -1 to +3 | +1 | High if building vertical expertise + deploying copilots + pricing innovation; moderate if generic services without differentiation |
| Execution Risk | -1 to +1 | 0 | Copilot adoption straightforward; vertical expertise requires hiring + client relationships; pricing models hard to change |
| Tail Risk | -1 to +2 | 0 | Junior talent commoditization risk; pricing pressure from AI boutiques; competitive disintermediation as clients build in-house AI capabilities |
Example Decisions (Consulting)#
| Decision | Strat Fit | Exec Risk | Tail Risk | Total | Rationale |
|---|---|---|---|---|---|
| Deploy copilots firm-wide (research, analysis, proposals); maintain junior hiring; invest in vertical AI expertise (50 specialists) | +2 | 0 | +1 | +3 | Productivity gains proven; vertical expertise defensible; junior transition managed |
| Build premium AI governance + compliance offerings (regulatory focus); differentiate from AI-native boutiques | +3 | +1 | +1 | +5 | High strategic fit (regulatory uncertainty real); premium pricing justified; execution feasible with talent |
| Pilot outcome-based pricing with 5 trusted clients (lower-risk engagements); document value creation | +1 | -1 | +1 | +1 | Addresses pricing pressure; pilots learning; adoption risk high (new model for clients + firm) |
| Hold on copilot deployment; maintain traditional leverage model; wait for AI disruption to stabilize | -1 | +2 | +1 | +2 | Low execution risk; but strategic lag; competitors moving faster; talent attrition likely |
| Aggressive junior headcount reduction (>40%); rely on AI for analyst-level work | +1 | -1 | -2 | -2 | Short-term cost savings; destroys talent pipeline; client delivery quality at risk; future partner pipeline broken |
Industry 7: LAW (AmLaw 50 Firm, Billable Hour Economics, Partner/Associate Leverage)#
Strategic Priorities (2026-2030):
- AI-assisted legal research, due diligence, and contract review (efficiency gains within billable model)
- Bar rule compliance for AI-generated work product (jurisdiction-specific; rapidly evolving)
- Malpractice liability management (attorney review protocols for AI output)
- Associate leverage model adaptation (AI handles routine tasks; associates redeployed to complex work)
- Pricing model defense or transition (billable hour under pressure from client demands for efficiency)
- Competition from legal AI platforms (Harvey.ai, CoCounsel) and alternative legal service providers
Key Economic Context:
- Revenue driven by billable hours x realization rate x partner/associate leverage ratio
- AI efficiency gains create a paradox: better for clients, potentially destructive to revenue model
- Partner economics depend on associate leverage (billing associates at 3-4x cost); if AI replaces associate work, leverage model erodes
- Bar associations in multiple jurisdictions actively developing rules on AI-generated work product
- Malpractice insurance implications for AI-assisted work not yet settled
Scoring Ranges#
| Dimension | Range | Typical | Notes |
|---|---|---|---|
| Strategic Fit | -1 to +2 | +1 | High if piloting AI tools while managing bar compliance; moderate if fully committing to pricing model change; low if ignoring AI entirely |
| Execution Risk | -2 to +1 | -1 | Bar rule uncertainty adds complexity; malpractice review requirements slow adoption; partner resistance to pricing change |
| Tail Risk | -2 to +2 | -1 | Malpractice exposure if AI-generated work has errors; bar discipline risk if rules violated; revenue erosion if billable model disrupted without replacement |
Example Decisions (Law)#
| Decision | Strat Fit | Exec Risk | Tail Risk | Total | Rationale |
|---|---|---|---|---|---|
| Pilot AI research tools (Harvey.ai/CoCounsel) in 3 practice groups; mandatory attorney review; bar rule compliance assessment per jurisdiction | +2 | 0 | +1 | +3 | Proven tools; phased; attorney review manages malpractice; bar compliance addressed |
| Deploy AI-assisted contract review firm-wide; invest in quality control infrastructure; maintain billable rates | +1 | -1 | 0 | 0 | Strategic value; execution harder at scale; bar rule compliance across jurisdictions complex; revenue model preserved near-term |
| Transition to alternative fee arrangements with 5 major clients; maintain billable for rest | +1 | -1 | +1 | +1 | Addresses client demand; pilots learning; risk managed via limited scope; partner buy-in uncertain |
| Reduce associate class by 30% over 24 months; redeploy remaining to complex work; AI handles routine | 0 | -1 | -1 | -2 | Cost savings real; but leverage model damaged; staffing large matters becomes harder; lateral partners may leave |
| Build proprietary legal AI platform for contract analysis | +1 | -2 | -1 | -2 | Differentiation potential; but massive capex; competing against well-funded legal AI startups; no AI talent in-house |
| Hold: defer AI adoption; maintain traditional model; monitor bar rule developments | -1 | +2 | 0 | +1 | Low execution risk; but strategic lag; competitors gaining efficiency; clients demanding AI-enabled delivery |
Industry 8: MANUFACTURING (Heavy Manufacturing, 28 plants)#
Strategic Priorities (2026-2030):
- Predictive maintenance (downtime reduction, equipment life extension)
- Production optimization (throughput, quality, energy efficiency)
- Quality inspection AI (defect detection, process control)
- Labor transition (retraining, "no-layoff" agreements, union cooperation)
Scoring Ranges#
| Dimension | Range | Typical | Notes |
|---|---|---|---|
| Strategic Fit | -1 to +2 | 0 | High if prioritizing high-ROI plants (8-12 vs. all 28); moderate if spreading capex thin |
| Execution Risk | -1 to +2 | 0 | Predictive maintenance is proven; OT/IT integration is complex; equipment retrofitting labor-intensive |
| Tail Risk | -2 to 0 | -1 | Union relations if labor displacement not managed; supply chain dependencies if suppliers not ready |
Example Decisions (Manufacturing)#
| Decision | Strat Fit | Exec Risk | Tail Risk | Total | Rationale |
|---|---|---|---|---|---|
| Deploy predictive maintenance in 8 highest-ROI plants (pilot); phased OT/IT integration | +2 | +1 | +1 | +4 | Proven tech; 3.2-year payback; high ROI on priority plants; phased approach reduces risk |
| Announce "no-layoff" retraining agreement with unions; commit $35-40M over 2 years | +1 | +1 | +2 | +4 | Preserves labor relations, safety culture; union cooperation enables faster automation; long-term competitive advantage |
| Deploy warehouse automation + logistics integration across all plants simultaneously | +1 | -2 | -1 | -2 | High labor displacement without transition plan; execution risk high (OT/IT complex); union friction likely |
| Hold on manufacturing AI; focus on operational efficiency (cost controls, headcount management) | -1 | +2 | +1 | +2 | Defensive; low execution risk; but strategic lag if competitors gain efficiency advantage |
Industry 9: LOGISTICS (Freight/3PL/Warehouse, 5,000+ vehicles)#
Strategic Priorities (2026-2030):
- Route optimization + fuel efficiency (cost reduction, proven ROI: $180-200M potential)
- Predictive vehicle maintenance (downtime reduction, equipment life)
- Driver assistance + safety systems (accident reduction, driver acceptance)
- Autonomous vehicle pilots (partnerships with Waymo/Aurora; regulatory uncertainty; long timeline)
Scoring Ranges#
| Dimension | Range | Typical | Notes |
|---|---|---|---|
| Strategic Fit | -1 to +2 | +1 | High if prioritizing route optimization + driver acceptance; moderate if overcommitting to unproven AV |
| Execution Risk | -1 to +1 | 0 | Route optimization is proven; driver adoption varies by age cohort; AV regulatory timeline uncertain |
| Tail Risk | -2 to +2 | -1 | Driver resistance; union concerns; last-mile profitability limits; autonomous vehicle regulatory risk |
Example Decisions (Logistics)#
| Decision | Strat Fit | Exec Risk | Tail Risk | Total | Rationale |
|---|---|---|---|---|---|
| Deploy route optimization to 2,000 trucks (pilot: 40% of fleet); driver engagement program | +2 | +1 | +1 | +4 | Proven tech; $45-50M annual savings; phased approach allows learning; driver buy-in investment |
| Partner with Waymo/Aurora for long-haul autonomous pilots (5-10% of fleet); manage expectations | +1 | -1 | 0 | 0 | Strategic value; regulatory timeline uncertain (2028-2030); partnership de-risks vs. internal development |
| Deploy autonomous vehicles internally (full long-haul fleet); eliminate driver roles | 0 | -3 | -3 | -6 | Regulatory approval uncertain; driver/union resistance certain; execution technically and politically infeasible |
| Accept last-mile profitability limits; optimize only high-density urban routes | +1 | +2 | 0 | +3 | Realistic; lower total savings; but avoids overselling optimization to unprofitable segments |
Industry 10: BIG TECH (Google/Meta/Microsoft/Amazon-Class — Cloud, Ads, Devices, Enterprise Software)#
Scope Note: Big Tech in this exercise covers cloud infrastructure, advertising, devices, and enterprise software. AI lab and foundation model development decisions are excluded from participant scope; those dynamics are introduced via facilitator injects only.
Strategic Priorities (2026-2030):
- AI-powered product features (search, advertising, recommendation, cloud services)
- Enterprise AI services (APIs, platform tools, developer ecosystems)
- Cloud infrastructure scaling (GPUs, data centers, inference capacity for enterprise customers)
- AI cost management + margin defense (compute cost inflation, infrastructure investment)
- Regulatory scrutiny navigation (antitrust, data privacy, content moderation)
Scoring Ranges#
| Dimension | Range | Typical | Notes |
|---|---|---|---|
| Strategic Fit | 0 to +3 | +2 | High if investing in AI product features + enterprise services; very high if staying ahead of competition on cloud/platform |
| Execution Risk | -1 to +2 | +1 | Massive CapEx required for infrastructure; talent competition fierce; regulatory approval uncertain for some products |
| Tail Risk | -2 to +2 | -1 | Antitrust scrutiny + regulatory backlash; margin compression from AI compute cost inflation; competitive disruption from open-source models |
Example Decisions (Big Tech)#
| Decision | Strat Fit | Exec Risk | Tail Risk | Total | Rationale |
|---|---|---|---|---|---|
| Invest in cloud/inference infrastructure ($15B+ capex); build custom chips for enterprise AI workloads | +3 | +1 | 0 | +4 | Core strategic value; execution capability exists; margin pressure manageable with scale |
| Launch enterprise AI APIs + platform tools; compete on cost/performance for enterprise adoption | +2 | 0 | 0 | +2 | Platform leverage; proven go-to-market; execution straightforward; tail risk low |
| Integrate AI deeply into core products (search, ads, cloud); expect 5-10% productivity gain | +2 | +1 | +1 | +4 | Strategic fit high; user experience upside; execution proven; regulatory risk limited (private integration) |
| Defend market against open-source model commoditization; cut enterprise AI margins to 10-15% | +1 | 0 | -1 | 0 | Defensive pricing; maintains enterprise stickiness; profitability erodes; shareholder backlash risk |
Industry 11: B2B/B2C SaaS (Workday/Salesforce/SAP-Class, ~100K+ employees)#
Strategic Priorities (2026-2030):
- AI feature integration into products (copilots, predictive analytics, automation)
- Pricing model evolution (AI features bundled vs. premium tier)
- Competitive threat from AI-native startups (simpler, cheaper, specialized products)
- Margin pressure from AI infrastructure costs (training, inference, compute)
- Customer retention + upsell via AI (lock-in effect)
Scoring Ranges#
| Dimension | Range | Typical | Notes |
|---|---|---|---|
| Strategic Fit | 0 to +3 | +1 | High if bundling AI features + defending margins; moderate if uncertain on pricing/competition |
| Execution Risk | -1 to +1 | 0 | AI feature integration proven; pricing model change is hard; competitive response unpredictable |
| Tail Risk | -2 to +2 | -1 | Pricing pressure + margin compression; customer churn if AI features disappoint; startup disruption from specialized competitors |
Example Decisions (B2B/B2C SaaS)#
| Decision | Strat Fit | Exec Risk | Tail Risk | Total | Rationale |
|---|---|---|---|---|---|
| Bundle AI copilots into core product; include in standard SKU; no premium pricing upside | +1 | +1 | 0 | +2 | Maintains customer lock-in; execution straightforward; margin pressure from cost (no revenue offset) |
| Integrate AI prediction + automation deeply; sell as premium tier at 30-50% price increase | +2 | 0 | +1 | +3 | Strategic fit high (differentiation); proven pricing model; execution proven; churn risk manageable |
| Build AI-native product for vertical (e.g., HR-specific AI recruiting); compete on cost + specialization | +2 | -1 | 0 | +1 | High strategic value (new market); execution complexity (new product); adoption uncertain |
| Maintain traditional product; defer heavy AI integration until market stabilizes | 0 | +2 | -1 | +1 | Low execution risk; but competitive lag; startup disruption risk rises; customer churn risk |
Using Baselines During Industry-Level Scoring#
- Participant proposes decision. Identify the industry (Retail, CPG, Healthcare Provider, Healthcare Payer, Finance, Consulting, Law, Manufacturing, Logistics, Big Tech, B2B/B2C SaaS).
- Reference the industry baseline. Check typical Strategic Fit, Execution Risk, Tail Risk ranges for this specific industry.
- Calibrate your score. If participant decision is better than baseline, score higher; if worse, score lower.
- Score independently. Remember: all 11 industries are scored separately. A participant covering both Consulting and Law has each decision scored on its own industry-specific merits.
- Post and move on. Explain score concisely; don't debate. Remember you may be scoring multiple decisions per round (variable based on participant count and industry assignments).
Example#
Participant: Consulting participant proposes to "deploy copilots to 50% of consultant base over 90 days; hire 50 AI specialists to build vertical AI practices; maintain junior hiring (zero layoffs in pilot phase)."
Baseline (Consulting): Deploy copilots + maintain junior hiring + build vertical AI = +2 to +3 typical score.
Your calibration:
- Specificity is excellent (50%, 90 days, zero layoffs, vertical hires quantified)
- Timeline is tight but feasible (copilots are proven; vertical hiring is hard but not impossible)
- Commitment to zero layoffs + vertical hiring reduces tail risk and addresses competitive disintermediation
Score: +2 (Strategic Fit) + 0 (Execution Risk) + +1 (Tail Risk) = +3/6 (in line with baseline; executed well).
Note: If the same participant also covers Law, the Law decision will be scored separately based on Law baselines + Law-specific context (billable hour impact, bar compliance, malpractice liability).
Base Case Fallback Scoring (NOT Covered Here)#
Industries without explicit participant actions receive base case fallback scores from the fallback bank (see Base Case Fallback Bank). Fallback scores are:
- Deterministic: Not varied per participant or round; same fallback applies across all instances
- Small deltas: Typically +/-1 per dimension (not -2 to +2)
- Plausible: Represent defensive but reasonable moves (e.g., cost control, operational efficiency)
- Automatic: Applied by facilitator without participant input
Fallback industries are NOT scored using the baselines in this document. Baselines apply only to explicit decisions submitted by participants.
Summary: When to Score High, Medium, Low (Explicit Decisions Only)#
High Score (+2 to +3 range)#
- Participant is responding to clear competitive threat (e.g., competitor AI advantage)
- Technology is proven and participant's organization has execution capability
- Scope is realistic (phased, limited, with clear milestones)
- Participant acknowledges and mitigates tail risk (pilot, human oversight, severance plan)
Medium Score (0 to +1 range)#
- Strategic alignment is moderate (neither captures opportunity nor avoids disaster)
- Execution is feasible but carries standard risk (integration, talent, regulatory timeline)
- Tail risk is managed but not eliminated
Low Score (-1 to -2 range)#
- Decision is defensive or reactive (holding pattern, waiting)
- Execution faces material barriers (talent shortage, long regulatory approval, capital constraints)
- Tail risk is not addressed or is material
Very Low Score (-3 or exception +/-3)#
- Decision triggers a red-flag (see Plausibility Decision Trees)
- If red-flag fires, unlock +/-3 exception scoring to reflect severity
Use these baselines to calibrate, not to lock scores. Participants proposing above-baseline decisions earn higher scores. Participants proposing below-baseline decisions earn lower scores. Move briskly; facilitate, don't debate.