Teach a Mini-Course: 'AI for Execution, Humans for Strategy' — Curriculum and Lesson Plans

Hook: Stop Overworking for AI and Start Teaching the Right Division of Labor

Educators, coaches and instructional designers: if your learners are overwhelmed by competing priorities, cleaning up AI outputs, or unsure when to trust a tool versus their own judgment, this mini-course will change how they work. In 2026 most teams use AI for execution but still hesitate to trust it with strategy — that gap is your teaching opportunity. This ready-to-teach curriculum shows how to train learners to make AI the execution engine while people own strategy, intuition and ethical judgment.

Why teach "AI for Execution, Humans for Strategy" in 2026?

By early 2026, industry reports show clear patterns: a large majority of B2B leaders treat AI as a productivity tool—excellent for generating drafts, code, and structured content—yet very few trust it with high-level positioning or long-term strategy. That division of labor is the real skill. Learners who can orchestrate AI for repeatable execution while leading strategic thinking become immediate multipliers.

Key trends to reference in class:

• MoveForward Strategies and MarTech (2026): ~78% of B2B marketers use AI primarily as a productivity engine; only ~6% trust AI with positioning decisions (MarTech, Jan 2026).
• Practical adoption risks: teams waste time “cleaning up after AI.” Best practice now is guardrails and human-in-the-loop workflows that avoid repetitive cleanup (ZDNet, Jan 2026).
• Leadership and intuition matter: industry leaders like Bozoma Saint John emphasize trusting intuition and leading without permission—exactly the human skills that should drive strategy while AI handles execution (Adweek, 2025).

Course Overview (6 sessions — 8–10 hours total)

This mini-course is designed for students, teachers and lifelong learners. It combines demonstration, live practice, reflective strategy sessions, and a capstone project. Each session includes learning objectives, activities, sample prompts, assessment tasks, and rubrics.

Learning outcomes

• Differentiate tasks best suited to AI (execution) versus tasks that require human strategy and intuition.
• Design human-in-the-loop workflows that minimize cleanup and maximize trust.
• Create prompts, guardrails and evaluation metrics to produce reliable AI outputs.
• Apply ethical frameworks to strategic decision-making that AI should not own.
• Deliver a capstone: a strategy document and an AI-powered execution plan with assessment rubrics.

Session-by-Session Lesson Plans

Session 0: Orientation (30–45 minutes)

Goal: Set expectations and surface learner experiences with AI. Establish norms for human-AI collaboration.

• Icebreaker: share one AI win and one AI cleanup pain.
• Mini-lecture: 2026 adoption patterns and why strategy is still human-led (3–5 min).
• Activity: Quick audit — learners map recent tasks to "AI for Execution" vs "Humans for Strategy."
• Assessment: Short reflection (200–300 words) on one task they will hand to AI and one they will keep.

Session 1: What AI Does Best — Execution Patterns (60–75 minutes)

Goal: Teach model strengths: pattern completion, rewriting, summarization, code skeletons, A/B draft generation.

• Lecture demo: Use a popular LLM or multi-modal tool to produce three execution outputs (content outline, email sequence, pull request template).
• Hands-on lab: students run a standardized prompt and compare outputs. Prompt templates provided.
• Activity: Rapid iteration exercise — students refine a prompt in 3 rounds to reduce cleanup time.
• Assessment: Deliver a 1-page short report showing original vs. improved prompt and measured cleanup time.

Session 2: What Humans Do Best — Strategy, Intuition and Ethics (60–75 minutes)

Goal: Practice strategic framing, intuition-building and ethical judgments that inform AI use.

• Case study: Bozoma Saint John’s leadership lessons—build intuition through everyday decisions. Discussion on decision habits that machines cannot replicate (Adweek).
• Activity: Strategic question workshop — students turn a tactical prompt into strategic framing (e.g., instead of “write a landing page,” ask “who is the competitor-ignorant audience we must win and why?”).
• Mini-assessment: 2–3 minute pitch justifying a strategic choice; peers use a critical-thinking checklist to score.

Session 3: Human-in-the-Loop Systems & Guardrails (75–90 minutes)

Goal: Build workflows that reduce cleanup and increase trust in AI outputs.

• Lecture: Guardrails, prompt templates, tool choice (fine-tuned models vs. open LLMs), evaluation loops and red teaming (2026 best practices).
• Activity: Design a 3-step workflow for a task (e.g., weekly newsletter production) with checkpoints and acceptance criteria.
• Practice: Use a tool to generate content and then apply a rubric to approve, edit or reject outputs.
• Assessment: Submit the workflow with examples and a metric plan (KPIs like edit time saved, accuracy rate).

Session 4: Prompt Engineering & Advanced Strategies (75 minutes)

Goal: Teach advanced prompt patterns: chain-of-thought, role prompting, few-shot with constraints, and retrieval-augmented generation (RAG).

• Demo: Show how RAG can reduce hallucination and provide sources for fact-sensitive tasks.
• Activity: Students craft a multi-step prompt that includes context, constraints, evaluation criteria and a post-processing instruction (e.g., JSON output for automation).
• Assessment: Peer-review prompts for clarity and safety. Grade: clarity, reproducibility, minimal cleanup required.

Session 5: Capstone Workshop & Assessment (90–120 minutes)

Goal: Produce a capstone project: a one-page strategic brief and an AI-driven execution package (prompts, workflows, evaluation rubric).

• Project: Choose a real problem (e.g., a class’s recruitment campaign, departmental knowledge-base update, product launch micro-campaign).
• Deliverables:
  • One-page strategic brief (human-led): objective, audience, key insights, risks, decision milestones.
  • Execution pack (AI-led): 3–5 prompts, sample AI outputs, a human review checklist, and a testing plan.
  • Assessment rubric filled out for the project.
• Assessment: Present to peers + instructor. Use scoring rubrics below.

Practical Activities and Sample Prompts

Give students a set of repeatable exercises they can use in class and in labs. Make these downloadable templates.

Sample prompt templates (execution-focused)

• Content draft: "You are a content assistant. Produce a 400-word draft for [audience] about [topic] with 3 sections: problem, solution, CTA. Use a friendly tone and include one statistic. Output JSON: {title, intro, bullet_points, full_text}."
• Summarization with sources: "Summarize the following article into three takeaways and include exact quoted phrases and the source URL."
• Code scaffold: "Create a skeleton Python function that ingests a CSV and outputs a cleaned DataFrame. Include comments and edge-case notes."

Sample prompt templates (strategy-focused scaffolds)

• Strategic framing question for discussion: "What is the non-obvious customer behavior we must change to win, and why? List assumptions and how we'd test them."
• Decision checklist for leadership: "For each strategic option, list risks, required capabilities, timeline, and indicators of success/failure."

Assessment Rubrics

Use transparent rubrics so learners know what excellence looks like. Below are two scalable rubrics you can adapt.

Rubric A — Strategic Brief (25 points)

• Clarity of objective (5): Objective is explicit, measurable and time-bounded.
• Audience insight (5): Clear persona, pain points and evidence cited.
• Strategic logic (8): Coherent choice of approach, alternatives considered and risk mitigation.
• Actionability (4): Decision milestones and responsibilities defined.
• Ethical and inclusive considerations (3): Bias, privacy and equity accounted for.

Rubric B — AI Execution Pack (25 points)

• Prompt clarity and reproducibility (8): Prompts include context, constraints and expected format.
• Quality of sample outputs (6): Outputs are relevant, accurate and require minimal cleanup.
• Workflow design (6): Human checkpoints and acceptance criteria minimize rework.
• Evaluation plan (5): Metrics are defined (accuracy, time saved, edit rate) and testable.

Grading and Feedback Strategies for Instructors

• Use peer feedback rounds for early drafts — students often learn more by critiquing than by instructor-only feedback.
• Track one objective metric across all projects (e.g., estimated edit time saved) and use it as a class KPI; celebrate improvements.
• Include a reflective element: learners submit a 300-word "why human strategy mattered here" note with each project.

Advanced Strategies and 2026 Best Practices

After learners master core skills, introduce advanced topics now relevant in 2026:

• Retrieval-augmented generation (RAG): Combine vector stores and verified sources to reduce hallucinations for fact-sensitive strategic briefs.
• Model selection and cost control: Teach when to use smaller, cheaper models for drafts and larger models only for high-sensitivity tasks. See discussions about open-source vs proprietary tradeoffs.
• Red teaming and adversarial testing: Run a simple adversarial prompt to surface biases or risky outputs before deployment.
• Privacy-first prompt design: Remove or mask PII and use synthetic data for demos when appropriate.
• Continuous learning loops: Log edits and use them to create fine-tuning datasets or prompt libraries.

Sample Class Case Study (Week-long, cross-session)

Problem: The university alumni office needs a 2-week drip campaign to increase event attendance by 20% among early-career alumni.

1. Students produce a strategic brief: target segments, key insight (what motivates them), and risks.
2. They design an AI-powered execution pack: email templates, social posts, A/B subject lines, and an automation checklist.
3. Run prompts, evaluate outputs, iterate. Collect metrics on quality and edits.
4. Present final campaign and reflect on which parts remained human-led and why.

Classroom Materials & Tools

Recommended simple toolset (2026):

• One accessible LLM provider (with developer API for RAG if possible).
• Shared notebook (Google Docs, Notion or an LMS) for collaboration and versioning.
• Vector store or RAG demo (Pinecone, Weaviate, or hosted demo) for advanced sessions.
• Spreadsheet for tracking KPIs and rubrics.

Instructor Notes & Pitfalls to Avoid

• Don’t treat AI as a black box. Make invisible errors visible: require students to show sources, assumptions and edit logs.
• Avoid over-reliance on AI for novel strategy. Use the course to strengthen intuition-building practices (decision journals, rapid experiments).
• Keep ethical conversations front and center: bias, accessibility and stakeholder impact should be part of every rubric.

"Trust yourself first" — a central lesson from Bozoma Saint John: build intuition through everyday decisions, then use authority to lead change. In this course, encourage learners to practice that intuition in strategic framing while using AI to scale execution.

Measuring Success — Class KPIs

Track these metrics across the cohort to show impact to administrators or stakeholders:

• Average edit time per AI output (before vs. after course)
• Project pass rate by rubric (percent meeting "actionable" standard)
• Learner confidence in leading strategic decisions (pre/post survey)
• Number of repeatable workflows created and shared in class repository

Extensions & Next Steps

For learners who finish early or for extended professional development:

• Build a small automation pipeline using the execution pack (e.g., trigger email drafts from a spreadsheet).
• Create a mini-research project that measures how AI-augmented teams perform on strategic tasks vs. control groups.
• Run a guest session with a leader (invite a product manager, CMO, or a leader like Bozoma—focus on intuition and leadership).

Final Takeaways — What Educators Should Emphasize

• AI is an execution engine, not a strategy owner. Teach learners to design the decision, not just the output.
• Human skills matter more than ever: intuition, framing, ethical judgment and stakeholder alignment.
• Design for minimal cleanup: guardrails and human checkpoints save time and build trust.
• Make assessment concrete: rubrics focused on strategy clarity and AI reproducibility produce better learning outcomes.

Call to Action

Ready to run this mini-course in your classroom or workshop? Download the editable lesson templates, prompt library and printable rubrics from our resources page and adapt them to your learners. If you want a custom version for your organization or coaching program, reach out for a guided adaptation session.

Teach AI for execution—but train humans for strategy. Start this term and make your learners the leaders organizations need in 2026.

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