Textstone Labs — AI Strategy Field Guide: from business problem → deployment → adoption
AI strategy is hard. Most guides are either too theoretical (academic papers) or too vendor-specific (marketing materials). Product and operations leaders need practical, actionable guidance that focuses on patterns, not products. This guide bridges that gap by providing proven frameworks, ready-to-use templates, and real-world examples that help teams go from identifying AI opportunities to successful deployment and adoption. Whether you're building your first AI project or scaling your tenth, these resources eliminate guesswork and accelerate your path to measurable business value.
Who it's for: product/ops leaders and ICs shipping real outcomes (not demos).
What's inside: use‑case selection, lifecycle & governance, GenAI patterns, eval harnesses, change management, and checklists/templates.
MIT‑licensed. Vendor-neutral. No proprietary content. PRs welcome.
┌─────────────────────────────────────────────────────────────┐
│ TEXTSTONE LABS │
│ AI Strategy Field Guide │
│ │
│ From business problem → deployment → adoption │
│ Practical patterns, not vendor lock-in │
└─────────────────────────────────────────────────────────────┘
- Product leaders who need to prioritize AI opportunities and measure outcomes
- Operations leaders who want to scale processes with AI while maintaining quality
- Individual contributors who need practical patterns for AI implementation
- AI/ML teams who want to align with business stakeholders
- Start with the overview (01) to understand AI types and value framing
- Score your use cases (02) using the impact vs effort matrix
- Pick your team structure (03) based on your organization's maturity
- Follow the lifecycle (04) with governance gates for safe deployment
- Choose GenAI patterns (05) that fit your use case
- Evaluate platforms (06) based on your constraints
- Plan change management (07) for successful adoption
- Use the templates (08) to accelerate project planning
- Build evaluation harnesses (09) for quality assurance
- Follow BizML deployment (10) for business-led success
- 01 AI Overview
- 02 Use Cases & Value
- 03 Team Structures & Roles
- 04 Lifecycle & Governance
- 05 GenAI Patterns & Prompting
- 06 Platforms & Trade‑offs
- 07 Change Management & Rollout
- 08 Checklists & Templates
- 09 Evaluation Harness
- 10 BizML Deployment
- /resources (books, tools, case studies, glossary)
- /diagrams (Mermaid sources)
Open an issue with a proposed addition; include sources and a quick rubric/test if relevant.
Here's how it all comes together for a typical AI project:
1. Value Goal → Customer service agents spend 15+ minutes reading email threads before responding
2. Use Case Scoring → High impact (24% time savings), medium effort (summarization pattern), high data readiness
3. Lifecycle Gates → Problem brief approved, data governance cleared, technical design validated
4. Prompt Card → Structured output schema for decisions/actions/deadlines, with safety controls
5. Evaluation Harness → Golden set of 150 tickets, faithfulness ≥0.95, adversarial test pass ≥0.9
6. Rollout → Pilot with 10 agents, feedback collection, gradual expansion to full team
This end-to-end flow demonstrates how each section builds on the previous one, creating a systematic approach to AI implementation.
License: This project is licensed under the MIT License - see the LICENSE file for details.
Vendor Neutrality: All content focuses on patterns and principles, not specific vendors or products. This ensures the guide remains relevant regardless of technology choices.
Contribution Guidelines:
- ✅ Public sources and general knowledge only
- ✅ Vendor-neutral patterns and frameworks
- ✅ Practical, actionable guidance
- ❌ No proprietary company content
- ❌ No vendor-specific implementations
- ❌ No confidential case studies
See CONTRIBUTING.md for detailed guidelines.