Certified AI Executive: Chief Technology 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
• Choose models and architectures for AI products, including retrieval, fine-tuning and agents, while keeping the option to change provider
• Run evaluation, release, monitoring and cost control for AI at scale, and measure engineering productivity honestly
• Build AI into products to clear safety, oversight, transparency, accessibility, legal and licensing bars
• Shape the technology organization for AI: platforms, sourcing, skills, research and work with the CIO, CISO and CDO

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 Technology Officer

   Written for chief technology officers.

7. 5AI Architecture Choices for Products

   Every AI feature rests on a few architecture decisions: which model, whether to ground it in the company's own content, whether to tune it, how much freedom to give it to act, and how to keep the option to change course. This module covers those decisions from the CTO's seat, with the cost, latency and control trade-offs each one brings.

   • Starting from the product requirement and the simplest design that meets it
   • Model selection: proprietary versus open-weight, size, cost and latency
   • Retrieval-augmented generation, fine-tuning, agents and tool use
   • Designing for portability and avoiding lock-in

   Exercise. Pick one AI feature your teams have shipped or are building. Write down its quality target, its latency limit and its cost per request at today's volume and at ten times that volume. Then list every part of the design that would need rework if you had to change model provider within 90 days.

8. 6Engineering and MLOps at Scale

   AI features fail in ways ordinary software does not: they can be wrong while appearing to work, and they can change when nothing in the company's code has changed. This module covers how a technology organization tests, releases, monitors and pays for AI at scale, how to measure the real effect of AI coding assistants, and how to keep technical debt under control.

   • Offline and online evaluation, and red-teaming before and after launch
   • Monitoring, drift, reliability and release management
   • Cost per request and unit economics at scale
   • AI coding assistants measured honestly, and technical debt

   Exercise. For one AI feature in production, list every component that could change its behavior: model version, prompt, retrieval content, tools and configuration. Mark which ones are versioned, which are covered by an automated evaluation before release, and which could change without anyone in your organization noticing.

9. 7Building AI into Products Safely

   Customers meet a company's AI through its products, so product design is where safety, quality and trust are won or lost. This module covers launch bars for AI features, human oversight designed into the product, transparency and accessibility for users, the product-related obligations that apply in the US, Canada and the EU/UK, and open-source licenses and intellectual property in code and models.

   • Product safety and quality bars, and who signs off at launch
   • Human oversight and transparency designed into the product
   • Accessibility, and product obligations in the US, Canada and the EU/UK
   • Open-source licenses and intellectual property in code and models

   Exercise. Choose one AI feature that customers use. Write its launch bar as three to five measurable criteria, including the harms it must not cause. Then check its user interface: does it tell users they are dealing with AI, show where answers come from, give a clear route to a person, and work with a screen reader?

10. 8Leading the Technology Organization

    AI changes how a technology organization should be shaped, staffed and run. This module covers platform teams that give product teams a paved road for AI, build, buy or partner choices for individual components, the talent and skills the organization needs, the balance between research and delivery, and how the CTO works with the CIO, CISO and CDO.

    • Platform teams and a paved road for AI
    • Build, buy or partner for AI components
    • Talent, skills and the balance of research and delivery
    • Working with the CIO, CISO and CDO

    Exercise. List the AI capabilities that more than one of your product teams has built separately, such as model access, evaluation, retrieval or cost tracking. For each, estimate the engineering time spent across teams in the last year, and decide whether it belongs on a shared platform, with a vendor, or with the teams.