Certified AI Executive: General Counsel

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
• Build and maintain a principles-first map of the company's AI legal obligations across the US, Canada, the EU, the UK and other markets
• Negotiate AI contracts, allocate liability for AI outputs, and check that insurance responds to AI losses
• Protect the company's intellectual property, trade secrets and legal privilege as people use and build AI
• Lead the legal function's own use of AI, meeting professional duties on verification, confidentiality, supervision and fees

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: General Counsel

   Written for general counsel and chief legal officers.

7. 5Mapping AI Legal Obligations Across Jurisdictions

   A general counsel cannot advise on AI one question at a time; the company needs a map of which obligations apply to which systems in which markets. This module shows how to build that map from principles, layer by layer, with examples from the US, Canada, the EU and the UK, and how to keep it current as laws are proposed, delayed, replaced or withdrawn.

   • Building a principles-first map of AI legal obligations
   • The layers: AI-specific, privacy, consumer protection, anti-discrimination and sector rules
   • How the US, Canada, the EU and the UK differ in approach
   • Tracking legal change and deciding when to act on it

   Exercise. Pick three AI systems from your company's inventory and, for each market where they are used, list the obligations that apply under each layer in this module, with an owner and the evidence that shows compliance. Mark every cell that relies on a proposed law rather than one in force.

8. 6AI Contracts, Liability and Insurance

   Most of a company's AI arrives through contracts, and much of its AI liability is settled by them. This module covers the clauses that matter in AI vendor contracts, how indemnities and limitation of liability really allocate risk, who answers for AI outputs to customers and third parties, the move toward treating AI as a product for liability purposes, and how to check whether insurance still covers AI losses.

   • The clauses that matter in AI vendor contracts
   • Indemnities, warranties and limitation of liability
   • Liability for AI outputs and product liability trends
   • Insurance coverage, exclusions and gaps for AI

   Exercise. Take your company's most important AI vendor contract and, for data use, IP indemnity, warranties, audit rights, change notice and limitation of liability, write one line on what the contract says and one line on the exposure it leaves with your company. Then ask your broker which of your policies would respond to a claim caused by that vendor's AI.

9. 7Intellectual Property and Confidentiality

   AI raises intellectual property questions at every stage: the material models are trained on, the outputs they produce, the secrets typed into them and the inventions they help create. This module gives the general counsel a principle-level view of where the law stands, without predicting cases that are still unsettled, and the controls that protect the company's own IP, its confidential information and legal privilege when people use AI.

   • Copyright in training data and in AI outputs
   • Trade secrets and confidential information in prompts
   • Patents and inventorship for AI-assisted inventions
   • Privilege and confidentiality when lawyers and staff use AI

   Exercise. List the three kinds of company information that would do most harm if they leaked through an AI tool, and check which approved tools may receive each. Then pick one important piece of AI-assisted work, such as a design, code module or report, and write down who contributed what, and whether the company could show human authorship if its ownership were challenged.

10. 8AI in the Legal Function and Professional Responsibility

    General counsel lead lawyers who are adopting AI quickly and remain personally responsible for their work. This module covers choosing and measuring legal AI tools, verifying citations and the sanctions courts impose when lawyers do not, professional conduct guidance in the US, Canada and the UK, AI in litigation and e-discovery, and setting expectations for outside counsel.

    • Choosing, testing and measuring legal AI tools
    • Verification and fabricated citations
    • Professional conduct guidance for lawyers using AI
    • E-discovery, litigation and managing outside counsel

    Exercise. Write a one-page verification protocol for AI-assisted legal work in your department, covering authorities, quotations, propositions and sign-off. Then check your outside counsel guidelines and note what they say, or fail to say, about AI tools, confidentiality, verification and billing.