Scpr Framework

SCPR Framework

A structured approach to problem-solving and executive communication used in management consulting.

Framework Components

S - Situation: Current state of the market/business

• What is the lay of the land?
• Establish baseline context
• Describe the stable environment before changes

C - Complication: Recent shift or change

• What has changed recently?
• New market dynamics (AI boom, regulatory changes, competitive threats)
• The catalyst that creates urgency

P - Problem: Crisp question to solve

• What specific strategic question must be answered?
• Common examples: "How to grow revenue?", "How to enter new market?", "How to reduce costs?"
• Must be specific and answerable

R - Recommendation: Proposed actions

• What should be done and by when?
• Priority actions to address the problem
• Can be structured as issue tree branches (doesn't have to be only high-priority items)
• Specific, actionable, time-bound

Core Principles

MECE (Mutually Exclusive, Collectively Exhaustive)

• Recommendations should not overlap
• Together they should cover all necessary actions
• Each recommendation addresses distinct aspect of the problem

Clarity

• Each section should be concise
• Problem statement must be answerable
• Recommendations must be actionable

Example: Tech Startup Product Pivot

Situation Series B SaaS startup with $15M ARR selling project management software to creative agencies and marketing firms. Product focuses on task management, resource allocation, and client collaboration. 200 agency customers with average contract size $75K. Historically strong product-market fit with 25% YoY growth and 90% gross retention.

Complication AI-powered tools like ChatGPT, Notion AI, and Claude emerging as workflow automation alternatives. Customer usage metrics declining 15% over last 6 months. Exit interviews reveal agencies using AI for project briefs, status updates, and resource planning - core features of current product. Three enterprise deals ($500K pipeline) paused citing "evaluating AI-first solutions."

Problem How should we reposition the product and business model to return to 25%+ growth within 12 months while competing against general-purpose AI tools?

Recommendations

1. Product: Launch AI-native workflow engine by Q2 2025

• Integrate LLM for automated project scoping and task breakdown
• AI-powered resource matching based on skills and availability
• Differentiate on agency-specific context (brand guidelines, client history, creative workflows)

1. Positioning: Shift from "project management" to "AI-augmented agency operations" by Q1 2025

• Rebrand messaging around AI that understands agency workflows
• Emphasize integration advantages over general tools
• Target gap: ChatGPT lacks agency-specific memory and processes

1. Pricing: Introduce usage-based AI tier by Q2 2025

• Base platform remains flat fee ($75K)
• AI features charged per automation/generation
• Capture value from high-usage customers, protect downside

Usage Patterns

When creating SCPR structure:

1. Start with Situation (establish baseline)
2. Identify Complication (what changed?)
3. Frame Problem as specific question
4. Develop MECE Recommendations with timeline

When analyzing existing content:

1. Extract facts into S/C/P/R categories
2. Test Problem for specificity
3. Verify Recommendations are MECE
4. Add timelines if missing

When reviewing SCPR:

• Is Situation necessary context only (not exhaustive)?
• Is Complication recent and urgent?
• Is Problem answerable and specific?
• Are Recommendations mutually exclusive and collectively exhaustive?
• Does each Recommendation include "by when"?

Common Mistakes to Avoid

• Situation too detailed: Keep to essential context only
• Complication = Problem: They're different. Complication is "what changed", Problem is "what question to solve"
• Vague Problem: "Improve business" is too broad. "Increase revenue 40% in 12 months" is specific
• Overlapping Recommendations: Ensure MECE structure
• No timelines: Always include "by when" in Recommendations

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: sruthir28
• Source: sruthir28/enterprise-ai-skills
• License: MIT

Install and usage instructions live in the source repository linked above.