Certified AI Executive: Chief Information 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
• Bring AI into the organization's enterprise systems through sound integration, architecture and an approved tool catalog
• Run AI as a managed IT service, from intake and governance to support, capacity and spend
• Control enterprise AI use through identity, data permissions, records retention and vendor terms
• Modernize the systems and data AI depends on and keep IT running when AI services fail

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 Information Officer

   Written for chief information officers.

7. 5AI Across Enterprise Systems

   Most AI reaches staff through the systems the CIO already runs: SaaS suites, ERP and CRM platforms, collaboration tools and new copilots. This module looks at that spread from the CIO's seat: how to review AI features that vendors switch on, how to choose and roll out copilots, when to use a platform's built-in AI rather than a general assistant or a custom build, how to integrate AI with enterprise systems, and how enterprise architecture and an approved tool catalog keep the estate coherent.

   • AI features arriving inside SaaS, ERP and CRM platforms
   • Choosing and rolling out copilots for staff
   • Integration patterns and enterprise architecture for AI
   • Building and running an approved tool catalog

   Exercise. List the ten business applications your organization spends most on and, for each, note which AI features the vendor has released in the last year, whether each feature is on or off, who decided, and which data it can reach. Mark any feature that is on without a recorded review.

8. 6The IT Operating Model for AI

   AI tools must be requested, approved, supported, paid for and run like any other IT service, with some new demands. This module covers intake and demand management for AI, IT governance and decision rights, service management and support for AI tools, platform and capacity choices, and managing spend on AI licenses and usage.

   • Intake, demand management and IT governance for AI
   • Service management and support for AI tools
   • Platform, infrastructure and cloud capacity choices
   • Managing IT spend on AI licenses and usage

   Exercise. Trace the last three requests for an AI tool from the moment someone asked to the moment they could use it (or were refused). Note each step, who decided, how long it took and what was recorded. Then list your three largest AI costs and say whether each is priced by seat or by usage and who watches it each month.

9. 7Controlling Enterprise AI Use

   The CIO turns AI policy into controls that work in daily use. This module covers finding and responding to shadow AI, making acceptable use operational, identity and access for AI tools and agents, the oversharing risk when copilots can search everything, records retention and e-discovery for AI content, and vendor terms on data use, with examples from the US, Canada and the EU/UK.

   • Discovering shadow AI and making acceptable use operational
   • Identity and access for AI tools and agents
   • Data permissions and the oversharing risk of copilots
   • Records retention, e-discovery and vendor terms on data use

   Exercise. Pick one copilot or enterprise assistant your organization uses or plans to use. List the content sources it can search, and for each, estimate what share of items can be opened by everyone in the organization. Then check whether prompts and outputs from the tool are covered by your retention schedule and can be placed on legal hold.

10. 8Modernization and Resilience

    AI depends on the systems, data and integrations underneath it, and adds new ways for IT to fail. This module covers legacy systems and AI, data and integration readiness, keeping IT running when AI services fail, managing concentration on a few AI and cloud vendors, building IT skills for AI, and working with the CTO, CISO and CDO.

    • Legacy systems, modernization and AI
    • Data and integration readiness
    • IT continuity and vendor concentration
    • IT talent and working with the CTO, CISO and CDO

    Exercise. Pick the AI service your organization would miss most if it stopped for a day. Write down what would happen to the business process, how staff would work without it, how long they could do so, and which vendors the service depends on, including vendors behind your vendors.