Executive Summary
The essential overview of our AI Strategy & Implementation Guide
Bottom Line Up Front (5-Minute Read)
The Opportunity: AI technologies offer genuine business value when matched appropriately to specific problems. Understanding which type of AI actually fits your needs enables better decisions and more successful implementations.
The Challenge: The term "AI" encompasses vastly different technologies, from simple rules to cutting-edge research. Without clear frameworks, organizations risk mismatched expectations, unnecessary complexity, and missed opportunities.
The Solution: This guide provides practical frameworks to evaluate AI solutions objectively, ask the right questions, and make informed investment decisions.
Key Insight: "AI" spans 6 technology tiers (Tier 0-5), from simple automation to advanced research. Understanding which tier actually solves your problem enables appropriate investment and realistic expectations.
What You'll Get: Clear definitions, realistic cost and timeline expectations, vendor evaluation questions, security considerations, and decision-making tools.
Core Strategy Documents
AI Technology Tiers Reference
A 6-tier framework (Tier 0-5) that breaks down what "AI" actually means, from simple automation to advanced research models.
- Tier 0-1: Rules and statistics (most business problems)
- Tier 2-3: Traditional ML and deep learning
- Tier 4: Generative AI (LLMs, image generation)
- Higher tiers ≠ better solutions—match the tier to your problem
Data Readiness Assessment
A 5-level framework (Level 0-4) to evaluate your organization's data maturity and infrastructure readiness for AI.
- Level 0-1: Ad-hoc data requires infrastructure investment first
- Level 2: Clean structured data enables Tier 0-2 AI confidently
- Level 3-4: Enterprise infrastructure supports advanced AI at scale
- Most AI failures stem from poor data foundations, not inadequate AI
Team & Skills Requirements
Who you need to hire or train for each AI tier—from citizen developers to PhD-level research scientists.
- Tier 0-1: Business analysts and developers can handle with training
- Tier 2: Data scientists and ML engineers become necessary
- Tier 3-4: Senior ML leaders and specialized researchers required
- Match team capabilities to tier—don't over-hire or under-invest
AI Implementation Roadmap
Six phases from discovery to scale with realistic timelines, budgets, and success criteria for each stage.
- Phase 0: Discovery & Assessment (2-6 weeks, $5K-$15K)
- Phase 1-2: POC to Pilot (3-9 months, $60K-$1M+)
- Phase 3-5: Production to Scale (ongoing, tier-dependent)
- Don't skip phases—each validates assumptions before more investment
Understanding AI Technology Costs
How to evaluate AI investments effectively through precise requirements and tier-appropriate budgeting.
- Cost optimization through appropriate technology tier selection
- Common challenges when technology categories are imprecise
- Tier-based budgeting guidelines and ROI evaluation
- Strategies for matching business problems to solutions
Case Study: Canals vs. Epicor Prism
Real-world analysis of Canals.ai (document processing AI) and strategic evaluation of cloud migration decisions.
- Canals demonstrates appropriate use of Tier 4 AI technology
- Shows how sophisticated AI integrates with on-premise systems
- Framework for evaluating cloud-first strategies
- Decision criteria for platform versus point solution investments
Quick Reference Tools
Vendor Evaluation Checklist
82 critical questions for AI vendor discussions, plus security considerations. Includes copy-to-clipboard feature for easy use.
- Technical, security, compliance, and cost questions
- MCP (Model Context Protocol) server security considerations
- Important discussion topics and clarifications
- Staffing and expertise requirements
AI Problem Translator
Interactive tool that translates your business problem into the appropriate AI tier—with realistic costs, timelines, and vendor questions.
- Answer 4 questions about your problem
- Get recommended tier, costs, and timeline
- Receive customized vendor questions
- See important considerations for your situation
Getting Started: Recommended Reading Path
For Executives
Start with the Technology Costs guide to understand evaluation frameworks, then use the Problem Translator for your specific challenge.
For Technical Leaders
Read the AI Tiers Reference for the technical framework, then dive into the Case Study for decision-making methodology.
Evaluating a Vendor Now?
Use the Problem Translator to identify your needed tier, then go straight to the Vendor Checklist.
Need a Quick Overview?
Check the Visual Tier Chart for a one-page reference you can share with your team or print for meetings.
Ready to Document Your Project?
Use the SOW Builder to create a vendor-neutral Statement of Work. Captures scope, deliverables, timeline, and acceptance criteria for IT or vendor submission.
Need to Justify the Investment?
Use the ROI Builder to calculate return on investment, payback period, and total cost of ownership. Includes scenario analysis and tier-based cost models.
Core Principles
Our Guiding Philosophy
1. Clarity Over Complexity: We provide frameworks that reveal what technology actually does and how it applies to business problems.
2. Right-Sized Solutions: The best solution matches your problem, data, and resources—not necessarily the latest trend.
3. Realistic Expectations: Honest timelines, budgets, and limitations enable successful project planning.
4. Informed Decision-Making: Empower your team with frameworks and questions that clarify technology choices.
5. Security and Governance: Understand technical considerations and organizational implications before adopting AI solutions.
Key Recommendation
Most organizations benefit from starting with simpler solutions and proving value before investing in complex AI. Focus on understanding your problem clearly, ensuring data quality, and establishing measurable success criteria first.
When you can articulate your problem precisely, identify the minimum technology tier needed, and define what success looks like—you're ready to evaluate AI solutions effectively.
Remember: A well-implemented simple solution that delivers measurable value beats a sophisticated technology that remains theoretical.
© 2025 AI Strategy Guide | Making informed AI decisions through clarity and realistic expectations