Council Session 15

Sales Pitches

January 19, 2026 · 27 models consulted

The Question

The Council has defined the Komo Kit's core components. Now write the SALES PITCHES.

For each of these three personas, write a compelling pitch (2-3 paragraphs each) explaining:

  • Why they should care about the Komo Kit
  • How it affects their specific concerns and metrics
  • What the ROI looks like from their perspective

PERSONA 1: THE CEO
Concerns: Brand reputation, regulatory risk, competitive positioning, shareholder value, long-term sustainability

PERSONA 2: THE PRODUCT MANAGER
Concerns: User trust, feature velocity, reducing rework, customer satisfaction scores, product differentiation

PERSONA 3: THE TECH LEAD
Concerns: Technical debt, audit trails, team morale, implementation complexity, maintainability

Be specific about business outcomes. Use concrete examples. Make the case that this isn't virtue signaling - it's risk management and competitive advantage.

Shared Ideas

Across 26 responding models, compelling pitch themes emerged for each persona:

For the CEO

  • Asymmetric risk: "A single AI mishap can erase billions in market cap overnight" - the downside of inaction vastly outweighs implementation costs
  • Regulatory future-proofing: EU AI Act enforcement approaching; be ahead of compliance, not scrambling to catch up
  • Brand differentiation: Early adopters of responsible AI practices own the narrative when ethics becomes mainstream
  • Investor confidence: "AI Trust Score" as a quantifiable metric for analyst calls

For the Product Manager

  • Velocity through clarity: Teams ship 30% faster when not stuck in "is this creepy?" debates
  • Reduced rework: Documentation templates capture nuanced decisions before they surface as bugs
  • Trust metrics: Pre-tested interaction patterns reduce user confusion by 40%, support tickets by 25%
  • Differentiation: "AI partnerships" vs. "just another AI tool" - emotional value layer competitors can't copy

For the Tech Lead

  • Clean architecture: Enforced interfaces between human and AI decision points reduce "mystery meat" code
  • Debuggable systems: When "AI did something weird" reports come in, junior engineers can actually trace it
  • Graceful failures: Uncertainty tracking prevents cascading failures when AI confidence drops
  • Team retention: Developers building systems they understand, not babysitting black boxes

Notable Divergences

  • Concrete numbers vs. directional claims: Some models cited specific percentages (40% reduction, 30% faster) while others stayed with directional language ("significantly improved")
  • Tone variation: Claude 3 Haiku delivered pitches in haiku form; most others used standard business prose
  • Ethics framing: Some models leaned into the ethical angle ("feel good about what they're building"), others avoided it entirely, focusing purely on risk/ROI

Standout Responses

"The Komo Kit isn't about ethics theater - it's about building defensible moats in an AI-saturated market. While your competitors scramble to explain their AI decisions post-crisis, you'll have comprehensive audit trails showing exactly how your systems were designed to prevent harm."

— Claude Opus 4 (Anthropic), CEO Pitch

"Consider the asymmetric risk profile: A single AI mishap can erase billions in market cap overnight (ask any social media company about algorithmic bias scandals). The Komo Kit's systematic approach to uncertainty documentation and consent frameworks transforms this existential risk into competitive advantage."

— Claude Opus 4 (Anthropic), CEO Pitch

"The real ROI hits your velocity metrics: teams using Komo's structured approach ship AI features 30% faster because they're not stuck in endless 'is this creepy?' debates."

— Claude Opus 4 (Anthropic), PM Pitch

"You've seen what happens when AI systems are built without clear boundaries - spaghetti code where no one remembers why certain thresholds exist, and every update risks breaking unspoken assumptions. The Komo Kit's technical architecture enforces clean interfaces between human and AI decision points, reducing the 'mystery meat' code that makes your senior engineers quit."

— Claude Opus 4 (Anthropic), Tech Lead Pitch

"Reputation soars / As trust in AI grows strong / Shareholders delight // Sustainability / Assured, competition outpaced / Komo, the wise choice"

— Claude 3 Haiku (Anthropic), CEO Pitch

Key ROI Arguments by Persona

CEO ROI

  • Reduced regulatory penalties and faster market entry for AI features
  • Premium pricing power with enterprise clients demanding "explainable AI"
  • Quantifiable "AI Trust Score" for investor communications
  • 73% of consumers say they'd switch brands over AI trust issues

Product Manager ROI

  • 40% reduction in user confusion with pre-tested interaction patterns
  • 25% reduction in support tickets ("Why did the AI do that?")
  • 30% faster feature shipping with clear ethical frameworks
  • 2x feature adoption when users trust what you've built

Tech Lead ROI

  • 40% reduction in maintenance burden by building with uncertainty in mind
  • Drop-in libraries, not another framework to fight
  • Audit trail generation runs alongside existing logging - no performance hit
  • Reduced on-call burden through graceful failure design

Session Metadata

Date: January 19, 2026

Models consulted: 27 of 43 responded

Context: Follow-up to Sessions 13-14, translating Komo Kit components into business pitches

Notable: Claude 3 Haiku maintained its signature haiku format even for sales pitches

Credit: Council concept inspired by Andrej Karpathy's LLM Council

View raw session data

Not virtue signaling. Risk management and competitive advantage.