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Cross-Round Integration Guide

Cross-Round Integration Guide#

Part of the Project Threshold Tabletop Exercise V7.4 — Round Materials

Cross-Round Spillovers & Cascades#

Throughout Rounds 1-4, the following causal flows are critical for industry participants to track and for facilitators to emphasize:

1. Earnings & Valuation Cascades (All Rounds)#

Real earnings data is the most powerful signal. Participants must calibrate their own productivity assumptions against what the market is seeing in reported results. Early-movers who reported strong results in R1-R2 set benchmarks that laggards must match or beat in R3-R4.

What happens:

Round 1: Participants make aggressive/defensive AI deployment decisions
Round 2: Early adopters report earnings gains (or disappointments)
Round 3: Market prices in early performance. Laggards face competitive pressure to match
Round 4: Valuation multiples reflect realized vs. promised AI productivity

Facilitator move: In Round 2+ share-outs, explicitly call out earnings surprises: "Retail reported 8% margin expansion. Finance reported only 2%. Notice the divergence? That reflects execution quality, not just deployment pace."

2. Regulatory Decisions → Industry-Specific Constraints (R1-R3)#

Regulatory clarity (or uncertainty) cascades downstream. Healthcare regulation tightens adoption timelines; finance regulation creates compliance friction; labor regulation constrains workforce optimization. Participants must anticipate and plan for regulatory pressure in their industry.

What happens:

Round 1: Regulatory landscape is ambiguous. Participants choose fast or cautious
Round 2: Facilitator Market Shocks and injects clarify regulatory direction
Round 3: Binding regulatory requirements are in place. Participants pivot strategy
Round 4: Regulation is part of competitive landscape. Winners are those who built governance early

Facilitator move: When regulatory injects hit, explicitly connect to participant decisions: "In Round 1, you chose aggressive deployment. Regulation has now shifted the landscape. Your strategy must adapt. This is real."

3. Labor Market Dynamics → Talent Scarcity & Wage Inflation (R1-R4)#

As AI adoption accelerates, talent becomes scarce and wages rise. Organizations that invested early in AI talent acquisition (R1) have an advantage in R2-R3. Those that delayed face higher costs and lower skill availability. This is a self-reinforcing dynamic.

What happens:

Round 1: AI talent is available but expensive. Early investors gain skill advantage
Round 2: Talent scarcity increases. Wage inflation accelerates
Round 3: Talent market is tight. Organizations without strong teams are at disadvantage
Round 4: Labor displacement from AI is visible. Retraining candidates from displaced workers become available (too late for market leaders)

Facilitator move: In Round 2+ injects, emphasize talent constraints: "You want to expand AI deployment, but talent market is tight. You can hire at higher wages, or you can slow deployment and invest in reskilling internal teams. Cross-industry competition for talent is fierce."

4. M&A Consolidation → Market Structure (R2-R4)#

Larger firms acquire smaller competitors for talent and IP. This consolidates the market, reduces competitive intensity, and increases barriers to entry for mid-market players. Consolidation trends in Round 2 set up the market structure in Rounds 3-4.

What happens:

Round 1: Market structure is stable. Participants make organic growth choices
Round 2: Consolidation begins. Larger companies acquire smaller ones for talent and capability
Round 3: Market is more concentrated. Mid-market players are consolidation targets or acquired
Round 4: Market structure is highly concentrated. Winners are the survivors; losers have exited or been acquired

Facilitator move: When M&A happens, emphasize the strategic consequences: "Big Tech just acquired an AI startup for their talent. Now they have enhanced platform capabilities. But they also have integration risk and antitrust scrutiny. The regulatory environment is watching."

5. Consumer Trust & Transparency → Speed of Adoption (R1-R4)#

Low consumer trust in high-stakes domains (Healthcare Provider, Healthcare Payer, Finance, Retail) slows adoption and raises compliance costs. Participants that invest early in transparency and governance (R1) build trust and move faster in R2-R4. Those that prioritize speed over governance face regulatory and reputational friction.

What happens:

Round 1: Participants choose aggressive or transparent approach to AI deployment
Round 2: Consumer sentiment data emerges. Transparent participants have higher trust scores
Round 3: Regulatory and consumer pressure targets low-trust approaches. Governance becomes mandatory
Round 4: Transparency is a competitive advantage. Participants that built trust early are favored by regulation and customers

Facilitator move: In Round 2+ injects, emphasize trust dynamics: "Consumer trust in AI recommendations is at 45%. Industries that disclosed AI usage have higher trust (55%). Those that didn't disclose have lower trust (35%) and now face regulatory pressure."

6. Peer Accountability → Strategic Discipline (R1-R4)#

The Collective Bonus creates a lightweight but recurring accountability loop. Every round, participants may publicly recognize strong strategies or flag risky ones. This shapes behavior: participants know their peers are watching and may nominate.

What happens:

Round 1: Initial nominations reflect first impressions and declared intent
Round 2: Nominations begin reflecting execution against Round 1 commitments
Round 3: Nominations reflect demonstrated track record; cumulative score impact becomes material
Round 4: Final nominations assess full strategic arc across all rounds

Facilitator move: After each Collective Bonus, connect the results to strategy quality: "Three participants nominated Finance as a risky strategy this round. Why? They cited the mismatch between aggressive automation claims and the regulatory enforcement reality. That's peer accountability in action — and it costs Finance -2 on their cumulative score."

Industry Health Assessment Across Rounds#

At the end of each round, facilitators should:

Collect participant decision data from decision worksheets
Assess each industry's strategic position using the banded scoring framework:
- Strategic Fit: {-2, 0, +2} — Does decision align with industry fundamentals?
- Execution Risk: {-2, 0, +2} — Can this be executed?
- Tail Risk: {-2, 0, +2} — Does this create or hedge risk?
Assess how each industry's health outlook is evolving heading into the next round
Communicate industry health trajectory to participants so subsequent decisions are made with awareness of how prior choices are shaping their strategic position

Example: Retail participant decides to aggressively automate customer service. Facilitator scores:

Strategic Fit: 0 (neutral; aligns with margin pressure but creates brand risk)
Execution Risk: +1 (feasible but requires talent and change management)
Tail Risk: -1 (creates customer trust risk if not managed well)
Total: 0 points this round

Then facilitator signals to Retail participant: "Your aggressive automation decision is strategically sound, but risky on execution and customer trust. In Round 2, we'll see if you execute well. Your industry health outlook will weaken if customer trust drops."

Participant Response Quality Rubric#

For facilitators evaluating industry participant responses (and communicating feedback):

Strong Response Characteristics:

Acknowledges both opportunity and risk/execution challenge
References specific data from injects or prior rounds
Considers second-order effects (e.g., labor relations, regulatory response, market consolidation)
Articulates clear decision rationale tied to financial/strategic goals
Anticipates cross-industry spillovers and adjusts strategy accordingly
Updates prior assumptions when new data contradicts them
Recognizes how decisions in one industry create constraints/opportunities for other industries

Example (Strong): "In Round 1, we chose aggressive automation to match competitors' margin expansion. In Round 2, we see labor resistance and customer trust drops. We're now investing in transparency and reskilling programs, accepting slower margin gains but protecting brand and regulatory relationships. This decision also affects Healthcare Provider's talent costs and Finance's headcount reduction strategies. We're coordinating with them on transition timing."

Weak Response Characteristics:

Overconfident in ability to execute (ignores complexity or risk)
Dismissive of contrary data ("This won't happen in our industry")
Ignores labor, regulatory, or consumer sentiment factors
Makes decisions in isolation without considering cross-industry spillovers
Repeats R1 strategy in R2-R3 without adjusting for new circumstances
Confuses correlation with causation (assumes past trend will continue)
Misses how their decisions cascade to other industries

Example (Weak): "We're doubling down on automation. We don't care about labor sentiment or consumer trust. Our competitors are moving fast, so we move faster. What other industries are doing doesn't affect us."

Individual Industry Decisions (V7.4 Model)#

In V7.4, each industry participant submits ONE decision per round. This reflects the individual decision-maker model where each industry is represented by a single decision-maker.

What is an Industry Decision?

One strategic choice per industry per round
Decision reflects the industry's response to injects and market conditions
Use banded framework to avoid false precision
Decision must address industry-specific challenges and opportunities

Cross-Industry Spillovers: Spillovers propagate across all eleven industries. Example:

Retail participant makes aggressive automation decision
This creates labor market spillover affecting all industries' talent costs
Healthcare Provider and Healthcare Payer face wage pressure as labor becomes scarcer
Big Tech and B2B/B2C SaaS must compete for engineering talent at higher wages
Finance faces pressure to defend junior talent
Consulting sees demand surge for workforce transition advisory
Facilitator notes in share-out: "Retail's aggressive automation has cascaded into the talent market. Notice how all industries are now facing wage pressure?"

Facilitator Guidance:

Explicitly announce industry decisions and their spillovers after each round
Explain the spillover's competitive implications across all industries
Use spillovers as learning moments: "Why did Big Tech face talent constraints after Retail's automation decision? What's the connection?"

Spillover Tracking for Eleven Industries#

Retail Spillovers — Receives From:#

Finance: Capital availability for expansion; payment processing dynamics
Consulting: AI deployment strategy advisory
Manufacturing and Logistics: Supply chain optimization affects customer fulfillment speed
Healthcare Provider and Healthcare Payer: Trust/regulatory dynamics in healthcare set consumer expectations for data security
Big Tech: AI capabilities and pricing available for deployment; competitive platform offerings
B2B/B2C SaaS: AI-embedded tools available for retail operations
CPG: Demand signals; shared supply chain dynamics

CPG Spillovers — Receives From:#

Retail: Demand signals; channel dynamics; shared supply chain infrastructure
Manufacturing and Logistics: Supply chain and distribution optimization
Consulting: AI deployment strategy advisory
Big Tech and B2B/B2C SaaS: AI capabilities for demand forecasting and marketing

Healthcare Provider Spillovers — Receives From:#

Healthcare Payer: Reimbursement dynamics; shared regulatory environment
Finance: Capital availability for clinical AI investment
Consulting: Compliance consulting; AI deployment advisory
Law: Regulatory advisory; malpractice and liability guidance
Big Tech and B2B/B2C SaaS: AI capabilities for clinical decision support; talent competition for data scientists
Retail: Consumer trust in AI affects patient willingness to accept AI-assisted care
Manufacturing and Logistics: Medical device and pharmaceutical supply chain optimization

Healthcare Payer Spillovers — Receives From:#

Healthcare Provider: Clinical outcomes data; shared regulatory environment
Finance: Capital availability; shared regulatory dynamics in financial services
Consulting: Compliance consulting; claims automation advisory
Law: Regulatory advisory; compliance guidance
Big Tech and B2B/B2C SaaS: AI capabilities for claims processing; talent competition

Finance Spillovers — Receives From:#

Consulting: Compliance advisory; AI deployment strategy
Law: Regulatory and litigation advisory
Retail: Customer demand for AI-driven financial services
Healthcare Provider and Healthcare Payer: Regulatory constraints on healthcare create consulting demand
Manufacturing and Logistics: Supply chain disruption affects financial operations
Big Tech and B2B/B2C SaaS: AI capabilities for trading, underwriting, and service delivery; talent competition

Consulting Spillovers — Receives From:#

All Other Industries: Demand for AI advisory, implementation, and compliance consulting
Finance: Demand for AI-driven financial advisory and compliance
Healthcare Provider and Healthcare Payer: Demand for compliance and governance consulting
Law: Shared professional services dynamics; regulatory advisory partnerships
Big Tech and B2B/B2C SaaS: AI tools for delivery augmentation; talent competition

Law Spillovers — Receives From:#

All Other Industries: Demand for regulatory advisory, litigation defense, and compliance review
Finance: Demand for financial regulatory advisory
Healthcare Provider and Healthcare Payer: Demand for healthcare regulatory and malpractice advisory
Consulting: Shared professional services dynamics; partnership on compliance engagements
Big Tech and B2B/B2C SaaS: AI tools for document review and legal research; talent competition

Manufacturing Spillovers — Receives From:#

Logistics: Shared supply chain dynamics; distribution optimization
Retail and CPG: Demand volatility and product mix changes affect production planning
Finance: Capital for infrastructure investment
Consulting: AI deployment advisory for manufacturing operations
Big Tech and B2B/B2C SaaS: AI capabilities for production optimization; talent competition

Logistics Spillovers — Receives From:#

Manufacturing: Production scheduling and output affect logistics planning
Retail and CPG: Demand volatility and fulfillment requirements
Finance: Capital for fleet and infrastructure investment
Consulting: AI deployment advisory for logistics operations
Big Tech and B2B/B2C SaaS: AI capabilities for route optimization and demand forecasting; talent competition

Big Tech Spillovers — Receives From:#

All Other Industries: Demand for cloud, enterprise AI platforms, ads services, and device ecosystem capabilities
Retail and CPG: Consumer demand for personalization and optimization capabilities
Healthcare Provider and Healthcare Payer: Regulatory requirements for clinical AI; talent competition
Finance: Demand for AI in trading, underwriting, and compliance workflows
Consulting and Law: Demand for AI in professional service delivery
Manufacturing and Logistics: Demand for logistics and production optimization AI
B2B/B2C SaaS: Competitive dynamics in enterprise software; shared talent pool

B2B/B2C SaaS Spillovers — Receives From:#

All Other Industries: Demand for AI-embedded software products
Retail and CPG: Consumer demand for AI-powered tools
Healthcare Provider and Healthcare Payer: Demand for clinical and claims management AI; compliance requirements
Finance: Demand for AI in financial workflows
Consulting and Law: Demand for AI in professional service delivery
Manufacturing and Logistics: Demand for production and logistics optimization tools
Big Tech: Competitive dynamics in enterprise software; platform dependency; shared talent pool

Round-by-Round Integration Checklist#

After Each Round, Facilitator Should:#

During Share-Out Phase:

Listen for first, second, and third-order effects participants mention (or miss)
Note which participants recognize spillovers from other industries
Note which participants ignore Collective Bonus feedback (warning sign of weaker strategy)
Track cross-industry dynamics: How does one industry's decision affect others?

During Scoring Phase:

Assign higher scores to participants that anticipate spillovers
Assign lower scores to participants that ignore cross-industry dynamics
Explicitly call out in feedback: "You scored well on Strategy, but missed the labor market consequence of your automation decision. That impacts Execution Risk across all industries."

Between Rounds:

Assess industry health trajectory based on all Round X decisions
Note which industries are outperforming/underperforming expectations
Prepare injects for Round X+1 that emphasize spillovers from Round X decisions
Track how one industry's decision creates constraints or opportunities for others

In Debrief (After Round 4):

Ask participants: "What surprised you about how your industry's Round 1 decision cascaded through Rounds 2-4?"
Ask: "How did cross-industry spillovers change your strategy?"
Highlight participants that adapted well (recognized spillovers, pivoted strategy)
Highlight participants that missed spillovers (got blindsided by consequences)

Key Themes to Emphasize in Debrief#

Cross-industry spillovers are real: In the exercise, as in reality, decisions don't stay siloed. Your industry's AI deployment strategy affects talent market, regulatory landscape, consumer trust, market consolidation, and all other industries.
Adaptation is required: Participants that made one decision and stuck with it underperformed. Participants that adapted strategy based on new information (injects, competitor moves, Collective Bonus feedback, cross-industry impacts) outperformed.
Governance pays off: Participants that invested in transparency, governance, and stakeholder management in early rounds had easier time adapting in later rounds and faced less regulatory/labor friction.
Execution beats strategy: A good strategy executed poorly scored lower than a mediocre strategy executed well. The gap was Execution Risk scoring.
Peer accountability matters: Participants that ignored Collective Bonus feedback were blindsided when cumulative score penalties compounded. Participants that listened to peer nominations and adapted their strategies built competitive advantages.
Cross-industry awareness: Participants that recognized how their decisions cascaded to other industries (labor markets, talent competition, customer demand, regulatory pressure) and coordinated or adapted accordingly outperformed those that made isolated decisions.

Document Version: Project Threshold V7.4 — Cross-Round Integration Guide Last Updated: March 2026 Format: Operational guidance for facilitators; emphasizes 11-industry dynamics, Collective Bonus accountability, and cross-industry spillovers