AI Adoption Guides
Frameworks, prompts, and tools I use to help companies adopt AI — shared so you can start making progress today.
Work With MeThe AI Adoption Framework
The same 4-phase approach I use with enterprises and startups to move from AI curiosity to production systems that deliver.
Assess
Audit your current tech stack, team capabilities, and business processes to identify where AI can drive the highest impact. Map readiness gaps and quick wins.
Strategize
Build a prioritized AI roadmap with clear business outcomes, governance requirements, and resource plans. Define success metrics before writing any code.
Implement
Deploy production AI systems with proper guardrails — security, monitoring, rollback plans, and team training. Start with high-confidence use cases and expand.
Scale
Measure outcomes, optimize costs, and expand AI across the organization. Build internal capabilities so your team owns the systems long-term.
AI Prompts by Role
Battle-tested prompts I've developed working with engineering teams. Copy them, customize them, and use them with your AI tools.
AI Readiness Assessment
Analyze our current engineering organization for AI adoption readiness. Consider: (1) our tech stack is [describe stack], (2) our team size is [N] engineers, (3) our primary business model is [describe]. Identify the top 3 highest-impact AI use cases, potential governance risks, and a phased 90-day adoption plan. Be specific about tooling recommendations and expected ROI.
Build vs Buy Analysis
We're evaluating whether to build a custom AI solution or use an existing platform for [describe use case]. Our constraints are: budget of [X], timeline of [Y months], team expertise in [list skills]. Compare the total cost of ownership, time-to-value, maintenance burden, and vendor lock-in risks for both approaches. Recommend one path with clear justification.
AI Governance Framework
Draft an AI governance framework for a [industry] company with [N] employees. Include: acceptable use policies, data handling requirements, model evaluation criteria, incident response procedures, and a review cadence. The framework should balance innovation speed with risk management — we want guardrails, not roadblocks.
Tools I Recommend
The tools I actually use and recommend to teams I work with — vetted through real production use, not just demos.
AI Coding
Claude Code
My primary AI coding tool. Excellent for complex refactoring, architecture decisions, and writing production-grade code with proper error handling.
Try Claude Code →
GitHub Copilot
Best-in-class inline code completion. The enterprise tier adds the governance controls (IP filtering, telemetry) that organizations need.
Try GitHub Copilot →
Cursor
AI-native IDE that combines coding assistance with codebase understanding. Great for teams that want a more integrated AI development experience.
Try Cursor →AI Strategy
Claude (Anthropic)
My go-to for long-form analysis, strategy documents, and complex reasoning. The extended thinking capability is invaluable for technical architecture decisions.
Try Claude (Anthropic) →
ChatGPT Enterprise
Strong general-purpose AI with enterprise security controls. Good for organizations that need SOC 2 compliance and admin controls out of the box.
Try ChatGPT Enterprise →
Perplexity
AI-powered research tool with source citations. Essential for competitive analysis, market research, and staying current on AI developments.
Try Perplexity →Infrastructure
Vercel
The best platform for deploying Next.js applications with AI features. Built-in AI Gateway simplifies model routing and cost management.
Try Vercel →
LangSmith
Observability and testing for LLM applications. Critical for monitoring AI system performance, debugging issues, and tracking costs in production.
Try LangSmith →
Terraform
Infrastructure as code for managing cloud resources. Essential for reproducible, auditable AI infrastructure deployments across GCP and AWS.
Try Terraform →Some links are affiliate links. I earn a commission at no extra cost to you.
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