Navigation

Supporting Materials

Alternative Scenario Overrides (Optional Extensions)

Alternative Scenario Overrides (Optional Extensions)#

NOTE: V7.4 is optimized for single 8-hour format with Baseline Speed + Type A (Copilots Everywhere) configuration. Alternative scenarios below are optional extensions only.

Overview#

Project Threshold V7.4 default and recommended configuration is Baseline Speed + Type A (Copilots Everywhere). This document provides optional narrative and mechanic overrides for 4 alternative configurations if you want to extend or vary the exercise beyond the standard format.

Alternative Configurations (Optional):

Slow Deployment + Type A (Copilots Everywhere) — Same capability type, slower adoption rate
Baseline + Type B (Agentic Workflows) — Same speed, different AI type (more autonomous)
Fast Deployment + Type A (Copilots Everywhere) — Faster adoption of copilots
Fast Deployment + Type B (Agentic Workflows) — Faster adoption of agentic systems

Important: These alternatives are provided for specialized use cases (e.g., follow-up workshops, deep-dive analysis for specific industries, or research purposes). They are NOT recommended for first-run Project Threshold exercises.

When to Use Alternative Configurations#

Use Baseline + Type A (Default) if:

This is your first Project Threshold exercise
You have participants unfamiliar with AI tabletop exercises
You want to complete exercise in single 8-hour session
You want broad applicability across industries
You are not researching a specific AI scenario

Use Alternative Configurations only if:

You are running multiple Project Threshold sessions and want to contrast different AI futures
You have a specific research question (e.g., "What if agentic workflows mature faster than copilots?")
You have individual participants with advanced strategic expertise comfortable with complexity
You have extended time (2-day format recommended)

Configuration 1: Slow Deployment + Type A (Copilots Everywhere)#

Key Assumption Change#

Foundation models advance on schedule, but enterprise adoption is slowed by organizational friction, regulatory caution, and integration costs. AI tools exist and are capable, but deployment is careful and phased. By 2030, copilot adoption in large enterprises is 40-50% (vs. 70-80% in Baseline).

Round-by-Round Narrative Changes#

Practice Round & Round 1: (Same as Baseline)

Round 2: Emphasis slower enterprise adoption than Baseline. "AI copilots are now available, but adoption rates lag expectations. Enterprise integration challenges are higher than anticipated. Smaller firms especially struggle with change management."

Round 3: "Adoption accelerates slightly but remains below Baseline pace. Regulatory clarity helps somewhat. Winners are firms that invested early in training and change management. Laggards face higher deployment costs in later rounds."

Round 4: "By 2030, copilot adoption has reached 45-50% of large enterprises. Market consolidation is real but less concentrated than Baseline due to longer competitive window. Mid-market players have slightly better survival odds."

Impact on Industry Decisions#

Participants have more time to make strategic pivots before competitive pressure forces action
Regulatory constraints feel more binding (regulatory injects feel more realistic)
Labor displacement is slower and more dispersed
M&A activity is lower (less pressure to consolidate)
Margin expansion is gradual, not rapid
Consulting/Law specific: Slower disruption gives professional services industries more time to redesign business models, but also reduces urgency and may create complacency

Use This If:#

You want to test strategies in a more cautious/regulatory-constrained scenario
You want to see how participants respond to slower-moving competition
You have Healthcare Provider, Law, or Manufacturing participants who want to model realistic deployment timelines

Configuration 2: Baseline + Type B (Agentic Workflows)#

Key Assumption Change#

Instead of copilots (AI assists humans in specific tasks), the scenario evolves toward agentic workflows (AI systems that autonomously execute sequences of tasks with human oversight). This is more disruptive to organizational structures and labor, but offers higher productivity gains.

Round-by-Round Narrative Changes#

Practice Round & Round 1: (Same as Baseline setup, but inject language emphasizes autonomous capability)

Round 2: "AI is now capable of executing full workflows autonomously (e.g., autonomous trading, autonomous customer service, autonomous supply chain optimization). This creates both opportunity and risk. Organizations must decide: deploy with oversight (safer), or deploy autonomously (faster)."

Round 3: "Agentic deployment is accelerating in competitive industries. Organizations that deployed with strong oversight (human-in-loop) are capturing benefits safely. Organizations that deployed fully autonomously are seeing both wins and failures. Regulatory scrutiny is intense."

Round 4: "Agentic workflows are now standard in Finance, Big Tech, and Logistics. Labor displacement is visible and politically sensitive. Healthcare Provider and Law are lagging due to regulatory constraints on autonomous systems."

Impact on Industry Decisions#

Higher upside if participants bet on aggressive autonomous deployment (higher productivity, more risk)
Labor displacement is more severe and concentrated (triggers stronger labor/regulatory injects)
Governance and oversight become critical risk factors
Tail risk injects (major AI system failure) hit harder
Consulting specific: Agentic workflows could automate entire engagement workstreams, making the talent pipeline disruption even more acute
Law specific: Autonomous legal analysis raises unauthorized practice of law concerns in a way copilot-assisted work does not; malpractice liability is more severe
Healthcare Provider specific: Autonomous clinical decision-making faces much stricter FDA scrutiny than physician-assisted AI

Use This If:#

You want to stress-test governance and risk management capabilities
You want to emphasize labor displacement and policy responses
You want to explore more radical AI scenarios with longer-term implications

Configuration 3: Fast Deployment + Type A (Copilots Everywhere)#

Key Assumption Change#

Foundation models advance rapidly, and enterprise adoption is faster than Baseline. By 2028, copilot adoption in large enterprises reaches 85%+. Competitive pressure is extreme and relentless.

Round-by-Round Narrative Changes#

Practice Round & Round 1: (Same as Baseline)

Round 2: "AI copilot adoption is outpacing expectations. 60%+ of large enterprises now have AI copilots in production. Competitive pressure is extreme. Laggards are falling behind rapidly."

Round 3: "Adoption has reached 75%+ of large enterprises. Market consolidation is accelerating. Mid-market players that have not adopted are under existential pressure."

Round 4: "By 2030, 85%+ of large enterprises have deployed AI copilots. Market structure has consolidated dramatically. Winners and losers are clearly defined."

Impact on Industry Decisions#

Time pressure is intense from the start
Industries that don't move fast early are essentially eliminated by Round 3
M&A consolidation happens earlier and at larger scale
Labor displacement is more severe and more rapid
Regulatory backlash injects hit harder (political pressure visible earlier)
Consulting specific: Client demand for AI advisory surges but so does pricing pressure; first-mover consulting firms capture outsized share
Law specific: Firms that adopt early gain massive efficiency advantage in contract review and due diligence; laggards lose competitive bids

Use This If:#

You want to emphasize speed-to-market and execution risk
You want to create high-pressure competitive dynamics
You want to show how first-mover advantage compounds over time

Configuration 4: Fast Deployment + Type B (Agentic Workflows)#

Key Assumption Change#

Agentic workflows mature rapidly and are adopted aggressively across industries. This is the most disruptive scenario: autonomous AI systems executing business processes with minimal human oversight.

Round-by-Round Narrative Changes#

Practice Round & Round 1: (Same as Baseline setup)

Round 2: "Agentic workflows are emerging faster than expected. Early adopters (Finance, Big Tech) are seeing dramatic productivity gains (50%+ in some functions). But failures are also visible. Regulatory uncertainty is high."

Round 3: "Agentic deployment is accelerating in competitive industries. Market consolidation is extreme. Organizations that navigated governance successfully are pulling away. Organizations that over-estimated safety are facing crises."

Round 4: "By 2030, agentic workflows are standard in unregulated industries but heavily restricted in regulated industries. Market structure is highly bifurcated: winners have 5-10x productivity; laggards have exited or been acquired."

Impact on Industry Decisions#

Governance and risk management are make-or-break decisions
Tail risk injects are frequent and severe
Labor displacement is dramatic and politically visible
Regulatory bifurcation between industries is extreme
Healthcare Provider and Law face strategic paralysis: Can't move fast without regulatory approval; can't get approval without moving fast
Consulting faces existential question: If agentic workflows can execute full consulting engagements, what is the firm's value proposition?
Finance may lead: Trading and risk management agentic systems create massive competitive advantage for early movers

Use This If:#

You want to explore the most transformative AI scenario
You want to emphasize governance, tail risk, and regulatory bifurcation
You want to test leadership's appetite for real transformational risk

Industry-Specific Override Notes (V7.4 Additions)#

Consulting Overrides#

Across all alternative configurations, Consulting faces amplified versions of the baseline dynamics:

Slow Deployment: More time to redesign business model, but risk of complacency; clients may reduce advisory spend if AI adoption is slow
Agentic Workflows (Type B): Existential threat to leverage model; entire engagement workstreams could be automated; need to articulate post-agentic value proposition
Fast Deployment: Advisory demand surges but pricing collapses; first movers capture outsized share; talent pipeline disruption accelerates

Collective Bonus Override for Consulting: In alternative configurations, expect Collective Bonus dynamics to shift for Consulting. In Fast + Type B, peers may consistently nominate Consulting as a "risky strategy" (business model under existential threat). In Slow + Type A, Consulting may receive "strong strategy" nominations (stable advisory demand with time to transform).

Law Overrides#

Across all alternative configurations, Law faces unique regulatory complexity:

Slow Deployment: Bar associations have more time to issue guidance; malpractice case law may begin to develop, reducing uncertainty
Agentic Workflows (Type B): Autonomous legal analysis raises unauthorized practice of law concerns; malpractice liability for AI-generated legal advice becomes acute; some jurisdictions may ban agentic legal AI entirely
Fast Deployment: Firms that adopt early gain massive efficiency advantage; bar rule arbitrage (deploying in permissive jurisdictions first) becomes viable strategy

Facilitator Note for Law: Law industry dynamics are primarily partnership-based (not public equity). Facilitator Market Shock constraints and Collective Bonus patterns for Law should reflect the industry's unique economic trajectory. Consider whether the alternative scenario benefits or threatens the billable hour model when selecting Market Shock targets in Round 2.

Implementation Guidance for Alternative Configurations#

If using an alternative configuration:

Modify Round situation slides — Update narrative language in Rounds 2-4 to match configuration
Adjust inject timing and intensity — Some injects become more relevant, others less
Emphasize different scoring dimensions — Slow configs emphasize Strategic Fit; Fast configs emphasize Execution Risk; Agentic configs emphasize Tail Risk
Brief participants explicitly — Make clear which configuration you're running. Example: "In this version, we're modeling Fast Deployment + Agentic Workflows. This is a more transformative scenario."
Adjust Facilitator Market Shock intensity — In more extreme scenarios, the facilitator may impose additional constraints or target more industries in Round 2

Do NOT:

Mix multiple alternative configurations in a single exercise (this creates confusion)
Use alternative configurations without clear participant briefing (surprises undermine learning)
Expect alternative configurations to finish in standard 8-hour time (some may run long)

Recommended Pairings (if running multiple exercises)#

Exercise 1: Baseline + Type A (Default)

Best for first-time facilitators and participants
Broadest applicability across industries
Completes in 8 hours comfortably

Exercise 2 (Follow-up): Baseline + Type B (Agentic Workflows)

Assumes participants already familiar with Project Threshold format
Explores more transformative scenario
Emphasizes governance and tail risk
Good for C-suite or risk committees
Particularly illuminating for Consulting and Law participants

Exercise 3 (Research): Fast Deployment + Type A or B

For strategy teams wanting to explore high-pressure competitive dynamics
Can be condensed to 6 hours or extended to 10 hours depending on emphasis

Document Version: Project Threshold V7.4 — Alternative Scenario Overrides (Optional) Last Updated: March 2026 Note: Alternatives are provided for reference only. Default Baseline + Type A is strongly recommended for all standard Project Threshold exercises.