Diablo_ClaudeMD_Ricing_example/skills/context-builder/references/frameworks.md

Consulting Frameworks

Selectively include in generated prompts based on consulting focus. Not all frameworks apply to every engagement.

Always Include

BCG 10/20/70 Rule

• 10% algorithms/models, 20% technology/data, 70% people/processes/culture
• Go narrow and deep: pick few high-value workflows, rethink entirely

Andrew Ng's Playbook

• Start with pilots, not strategy. Strategy comes from experience.
• Pilot -> Build team -> Train broadly -> Develop strategy -> Communicate

Include for Existential Strategy Focus

The "But" Heuristic

• When someone says "AI can automate everything, BUT..." -- the "but" is where current human value lives
• These "buts" may shrink over time, but they're the most reliable near-term moat
• Build strategy around making your "buts" bigger, not just optimizing what's automatable

The "Metro Newspaper" Test

• Don't orient on your filtered bubble's headlines; orient on what mainstream people know
• Most people still don't know what ChatGPT is -- you're in the avant-garde
• The real adoption curve is much earlier than tech Twitter suggests
• Practically: your competitors are probably not as AI-advanced as their press releases claim

Curiosity > Fear

• Teams driven by interest/curiosity adopt AI 10x more effectively than those driven by fear
• Internal reward (I explored something cool) is more sustainable than external pressure
• Find and amplify team members who genuinely enjoy experimenting
• Don't build AI strategy around anxiety; build it around capability and opportunity

Palantir Integration Model

• Don't just sell services; become the integration layer between client data + AI agents
• Offer AI-augmented workflows that combine proprietary data with domain expertise
• Human-in-the-loop at high-value decision points, automation for everything else
• Clients pay for the system, not the headcount

Include for Automation Focus

Value Stream Mapping (AI-adapted)

• Map current state -> Identify bottlenecks -> Design future state -> Monitor
• AI tools automate data collection and real-time updating
• Focus on cycle time reduction and handoff elimination

AI Readiness Canvas

• Strategic Imperative (Why): Vision, Value Proposition, Use Cases
• Foundational Capabilities (How): Data, Infrastructure, Skills, Governance

Deloitte AI Maturity Levels

• Starters -> Pathseekers -> Transformers
• Avoid "Underachiever" trap: many deployments, low outcomes
• Focus on few high-impact deployments rather than spraying AI everywhere

Include for Product Development Focus

The 15-Person Company Thought Experiment

• "What would a 15-person version of this company generating the same revenue look like?"
• Forces thinking about what's truly core vs what's operational overhead
• AI + small team often beats large team without AI on speed and cost

Fine-Tuning Economics

• Fine-tuning small models on domain data is becoming cheap ($10s-$100s)
• A fine-tuned small model can outperform GPT-4 on narrow tasks
• Proprietary training data is the moat, not the model architecture
• Consider: what training data do you have that nobody else has?

Distribution Advantage

• "Knowing how to sell and distribute is the skill AI can't vibe-code"
• Companies with distribution expertise + AI tools beat pure-AI startups
• If you can reach customers, you can test products rapidly
• Marketing/sales knowledge compounds; technical knowledge depreciates faster

Include for Regulated Industries

Compliance-First Automation

• Map regulatory boundaries before automating
• Some automations require certification or regulatory approval
• Build audit trails into every automated process
• Human sign-off at compliance checkpoints, automation everywhere else