Certified AI Executive: Chief Data Officer

What You Will Be Able to Do

• Explain what AI can and cannot do, test a team's or vendor's claims, and count the true cost of using it
• Choose where AI creates value, build a business case on measured baselines, and move pilots to scale or stop them
• Set accountability, risk controls and compliance for AI using recognized frameworks and regulation
• Lead the organization through AI: operating model, skills, workforce commitments, change and adoption
• Set a data strategy tied to the AI portfolio, manage key data as owned products, and show what data is worth
• Judge whether data is fit and ready for an AI system, including content, labels, synthetic data and drift
• Decide whether data may be used for AI under privacy, licensing and transfer rules in the US, Canada and the EU and UK
• Choose the data operating model, platforms, funding and skills, and work with the CIO, CTO and CISO to deliver

Modules

1. Part 1: Executive Core

   Part 1 is the same in every executive certification.

2. 1AI Essentials for Executives

   Leaders do not need to build AI, but they do need to judge it. This module explains the main kinds of AI system, what models can and cannot do, why data sets the limits, how AI costs behave, and the vocabulary and tests a leader needs to question a team or a vendor.

   • Rule-based systems, machine learning, generative AI and AI agents
   • What models do and where they fail: errors, bias, drift and explainability
   • Data, economics and the vocabulary for questioning teams and vendors
   • Spotting hype and testing vendor claims on your own data

   Exercise. List the AI tools and features already in use in your area, including those built into software you buy. For each, note whether it is rule-based, machine learning, generative or an agent, what data it uses, and who reviews its outputs before they are acted on.

3. 2AI Strategy, Value and the Business Case

   AI is worth funding only where it changes a measure that matters. This module covers where AI creates value, how to build and rank a portfolio of use cases, when to build, buy or partner, how to write a business case that counts the full cost and tracks realized benefit, and how to move from proof of concept to scale or stop.

   • Where AI creates value and how to measure it
   • Use-case portfolios: value, feasibility and risk; build, buy or partner
   • The business case: baselines, total cost of ownership, payback and ROI
   • Proof of concept, pilot and scale; why pilots stall; stage gates

   Exercise. Take one AI proposal now under discussion in your organization. Write down its baseline measure, list every cost it will carry over three years (including data work, integration, change, monitoring and review effort), name the owner of each benefit, and set the criteria for the next stage gate.

4. 3AI Governance, Risk and Regulation for Leaders

   AI needs clear owners, a view of what is in use and controls that match the risk. This module covers accountability structures, risk tiering, the main frameworks and standards, how AI and privacy law apply in the US, Canada and the EU/UK, and the confidentiality, security, vendor and incident risks that leaders must manage.

   • Accountability, the AI governance committee, RACI and the three lines
   • AI inventory, risk tiering and risk appetite
   • NIST AI RMF, ISO/IEC 42001, and AI and privacy law across jurisdictions
   • Confidentiality, AI security, vendor risk and incident response

   Exercise. List the AI systems and tools used in your area, including AI features inside software you already buy. For each one, name the business owner, assign a risk tier using the questions in this module, and note one control that is missing.

5. 4Leading People and the Organization Through AI

   AI value depends on how people and work are organized around it. This module covers operating models and leadership roles, AI literacy, workforce impact and honest commitments, change management, shadow AI and acceptable use, and how to measure real adoption and lead by example.

   • Operating models and AI leadership roles
   • AI literacy and workforce impact: tasks, redesign and reskilling
   • Leading change: Kotter's eight steps, involvement and resistance
   • Shadow AI, acceptable use, real adoption and communication

   Exercise. Pick one team that has access to an AI tool. Find out how many people used it in the last week, which tasks they used it for and what stops the others, then agree two changes with the team and measure use again a month later.

6. Part 2: Chief Data Officer

   Written for chief data officers.

7. 5A Data Strategy for AI

   Every AI system depends on data, but not all data deserves the same investment. This module looks at data strategy from the chief data officer's seat: deciding which data matters most for the AI portfolio, managing key data as products with named owners, getting business executives to accept ownership, using metadata, lineage and catalogs as tools for speed and trust, and showing what data is worth.

   • Linking the data strategy to the AI portfolio
   • Deciding which data matters and managing it as products
   • Ownership, stewardship and decision rights at executive level
   • Metadata, lineage, catalogs and measuring data value

   Exercise. Take your organization's five highest-priority AI use cases and list the data each one needs. Mark the data sets that serve three or more of them, name the business executive who should own each, and estimate in one line what share of each use case's benefit depends on that data.

8. 6Data Quality and Readiness for AI

   Quality is judged against a purpose, and AI raises the bar. This module covers how a chief data officer decides whether data is ready for an AI system: quality dimensions applied to training and retrieval data, the state of unstructured content, the cost and limits of labels and ground truth, what synthetic data can and cannot do, and how to keep data fit once a system is live.

   • Fitness for purpose and data readiness reviews
   • Quality dimensions for training and retrieval data
   • Unstructured content, labeling and ground truth
   • Synthetic data, data drift and keeping data fit

   Exercise. Pick one AI system in development and run a one-page readiness review: for each of availability, representativeness, accuracy, labels, permitted use and access, rate the data red, amber or green, name the owner of each gap and estimate the cost and time to close it.

9. 7Data Rights, Privacy and Sharing

   Having data is not the same as being allowed to use it for AI. This module covers, at the level of principle, the rights questions a chief data officer must answer before data trains or feeds a model: lawful basis and consent, purpose limitation, de-identification and the risk of re-identification, licensing and buying data, web scraping and training data rights, and moving data across borders, with examples from the US, Canada and the EU and UK.

   • Lawful basis, consent and purpose limitation for AI uses
   • De-identification and re-identification risk
   • Licensing and buying data, web scraping and training data rights
   • Cross-border transfers and recording permitted use

   Exercise. Choose one data set your organization wants to use for AI. Write down how it was collected, what people were told, the lawful basis or consent relied on, any license terms, whether it can identify people, and every country it will be stored or accessed in. Mark each answer as known, assumed or unknown, and name who will resolve the unknowns.

10. 8Leading the Data Organization

    A data strategy succeeds only if the organization behind it can deliver. This module covers how a chief data officer chooses an operating model for data, shapes the platforms that AI depends on, funds shared data work that no single business case will pay for, builds the skills the data organization needs, works with the CIO, CTO and CISO, and measures whether the data organization is making a difference.

    • Central, federated and hub-and-spoke data operating models
    • Data platforms for AI and their costs
    • Funding shared data work and building skills
    • Working with technology and security leaders, and measuring results

    Exercise. Draw your current data operating model on one page: what the central team does, what business domains do, and who decides standards, platforms and access. Then write a one-line RACI for data platform, data classification and access approval across the CDO, CIO, CTO and CISO, and mark any line where two people think they are accountable.