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Open source · Python

← Guides> Topic

AI for leaders

Leaders are asked to decide about AI before the results are in: which use cases first, which tools, how to bring a team along, and how to tell whether it worked. Vendor demos and headline statistics make those decisions harder, not easier.

These guides give you the plain explanation and a tool to decide with. Each one ends with something you can copy: a use-case scorecard, a build-versus-buy decision table, a 90-day rollout plan or an ROI worksheet. Statistics are sourced and quoted with what they actually measured.

[n.01/02]> 8 guides, in order
  1. 01AI Agents Explained for Business Leaders: What They Can DoAI agents explained for business leaders: what agents can and can't do today, chatbot vs copilot vs agent, real examples, risks and what to ask your team.Beginner · 13 minOct 20, 2026
  2. 02How to Choose AI Use Cases: A Scorecard and Worked ExampleHow to choose AI use cases and prioritize them: a copyable scorecard for value, feasibility, risk and data readiness, a worked example and a pilot plan.Beginner · 13 minOct 20, 2026
  3. 03Build vs Buy AI Agents: Decision Table and Costs (2026)Build vs buy AI agents: a decision table, the total cost factors on both sides, when each option wins, hybrid options and a worked three-year cost example.Beginner · 13 minOct 20, 2026
  4. 04How to Roll Out AI to Your Team: A 90-Day Adoption PlanHow to roll out AI tools to employees: a 90-day adoption plan with phases, training, champions, usage rules for data you already classify, and metrics.Beginner · 13 minOct 20, 2026
  5. 05How to Measure the ROI of AI: Metrics and a WorksheetHow to measure the ROI of AI and the success of AI and prompt engineering work: baselines, metrics by use case, full costs and a worked ROI worksheet.Beginner · 14 minOct 20, 2026
  6. 06AI Maturity Model: 5 Stages from Experiments to AI AgentsAn AI maturity model for the agent era: five stages from first experiments to AI agents in production, what each stage needs, and a self-assessment.Beginner · 12 minOct 20, 2026
  7. 07AI Agent Governance: How to Govern AI Agents in PracticeAI agent governance in practice: owners, approvals, limits, logging, review cadence and incident steps for AI agents, with templates. Operational only.Intermediate · 13 minOct 20, 2026
  8. 08What Is an AI Agent? Definition, How It Works, ExamplesWhat an AI agent is: a model that calls tools in a loop until a goal is met. How the loop works, a real example, agents vs chatbots and workflows, FAQs.Beginner · 7 minOct 18, 2026
[n.02/02]> go further

Other topics

  • Building AI agents in Python→
  • Models and providers→
  • Tools and the Model Context Protocol (MCP)→
  • Memory, context and RAG→
  • Agent reasoning patterns→
  • Multi-agent systems→
  • Guardrails and security→
  • Human approvals (human-in-the-loop)→
  • Observability and debugging→
  • Cost control and rate limits→
  • Testing and evals→
  • Runtime, triggers and deployment→
  • Frameworks and comparisons→
  • AI at work→
  • Enterprise AI agents→

Follow the AI for Leaders path.

Seven guides in order, from what an agent is to measuring results and governing agents in practice.

Start the pathAll guides