By the end of the program, your organization will achieve:
Business Transformation Impact
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Translate data patterns into executive-ready insights that drive ROI
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Build confidence in evaluating AI opportunities and risks
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Integrate ethical and regulatory considerations (e.g., EU AI Act) into business strategy
Workforce Enablement
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Equip managers to interpret models, dashboards, and KPIs responsibly
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Apply Excel-based exploratory data analysis (EDA) to example datasets
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Develop and refine generative AI prompts to improve productivity and decision quality
Organizational Readiness
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Establish governance models and ethical frameworks for AI adoption
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Evaluate vendors and technology solutions with structured checklists
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Build cultural readiness for evidence-based, data-driven decision-making
The curriculum is structured across three days, blending data fluency, machine learning, and generative AI with governance and organizational culture.
Day 1 — Data Foundations: From Excel to Enterprise
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Data strategy & ROI across business functions
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Translating technical terms into business language
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Data lifecycle, ownership, and governance risks
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Hands-on Excel exploratory data analysis (EDA)
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Statistics for decision-making (confidence, correlation, pitfalls)
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Data storytelling and insight slides
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Big data awareness: scaling from spreadsheets to enterprise systems
Day 2 — Predict to Decide: The Manager View of ML
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Where ML fits into business decisions
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Supervised learning (regression, classification) with business metrics
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Unsupervised learning (clustering, dimensionality reduction)
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Case activity: interpreting confusion matrices & feature importances
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A/B testing and causal logic
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NLP applications and risks (sentiment, topics)
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Ethics and explainability frameworks
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Vendor evaluation workshop (checklists and role-play)
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Case sprint: predictive pipeline → Data Decision Brief
Day 3 — Generative AI & Governance
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Prompt engineering workshop: practical patterns and role-based prompting
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GenAI capabilities and risks: hallucinations, retrieval-augmented generation
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AI agents and automation with human-in-the-loop safeguards
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Responsible AI governance: transparency, risk registers, stakeholder roles
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Culture change: embedding evidence-based decision-making
- EU AI Act primer: compliance, classification, and global implications
Managers and directors in operations, product, engineering, finance, HR, customer experience, risk/compliance, and IT leadership who sponsor data/AI initiatives, evaluate proposals, or oversee teams and vendors. Mixed cohorts work well to align vocabulary, expectations, and governance. While optional, this course can be ideal for analysts and technical staff who can engage in applied labs for deeper practice and professional development.
Typically three days (~24 hours) delivered onsite at your location, live online, or hybrid. Formats include consecutive days or spaced sessions to allow reflection between modules.
Is there any hands-on coding?
No. The program is designed for decision-makers. Activities are discussion- and case-based with light demos.
Will we use our company’s data?
No. This course uses public or synthetic examples to avoid data-handling concerns. If you want a follow-on workshop using enterprise data under approved protocols, we can scope that separately.
How is the program tailored to our context?
We adapt cases, examples, and discussions to your industry, functions, and typical decisions—without requiring access to proprietary data.
What do participants produce?
Simple “Data Decision Briefs” and governance checklists for educational and internal planning purposes.
What background is required?
No technical prerequisites. Comfort with basic business metrics and spreadsheets is sufficient.
How do we measure impact?
We align objectives and simple KPIs (e.g., time-to-decision, pilot pass rates, risk flags avoided) and provide take-home guides for managers.
Is there a certificate?
Yes. This non-credit professional program awards a Caltech CTME certificate upon successful completion (participation-based).
