Certified AI Executive: Chief Human Resources 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
• Turn task-level analysis of AI into a workforce plan, and choose between redeployment, reskilling and reduction on full costs and kept commitments
• Keep AI used in hiring, promotion and performance decisions fair, tested for adverse impact, explained and open to human review
• Set limits on AI-enabled employee monitoring that are necessary, proportionate and transparent, consult employees and their representatives, and protect wellbeing
• Run AI in HR services and employee data governance well, adapt policies and performance management, and lead HR's own adoption

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 Human Resources Officer

   Written for chief human resources officers.

7. 5Workforce Planning for AI

   AI changes how much work each role needs and which skills the organization will need next. This module looks at workforce planning from the CHRO's seat: turning task analysis into a plan for capacity, using a skills taxonomy, choosing between redeployment and redundancy, designing reskilling programs and measuring their return, and writing workforce commitments into the plan so that they can be kept.

   • From task-level analysis to a workforce plan
   • Skills taxonomies and skills-based planning
   • Redeployment versus redundancy, and reskilling programs and their return
   • Workforce commitments built into the plan

   Exercise. Pick one job family of at least 50 people that AI is likely to affect. List its ten main tasks with the share of time each takes, rate each as automate, assist or untouched, and convert the result into a range of full-time equivalents freed. Then write down where those people could go, what reskilling it would take and what you would commit to them.

8. 6AI in Hiring, Promotion and Performance Decisions

   Decisions about who is hired, promoted, rated or dismissed are among the highest-stakes uses of AI, and discrimination law applies to them whoever or whatever makes them. This module covers how AI tools can discriminate, how to test for adverse impact, what notice, explanation and human review candidates and employees should get, the main rules in the US, Canada, the EU and the UK, and how to buy and oversee HR decision tools.

   • How AI tools can discriminate, and who is liable
   • Bias testing, adverse impact and the four-fifths rule
   • Notice, explanation and meaningful human review
   • Rules in the US, Canada, the EU and the UK, and buying HR tools

   Exercise. List every tool that screens, ranks, scores or assesses candidates or employees in your organization, including features inside larger HR platforms. For each, record whether it has been tested for adverse impact in the last year, what candidates are told, and who can review or override its output.

9. 7Employee Monitoring, Privacy and Wellbeing

   AI makes it cheap to watch, score and draw conclusions about employees, and what is cheap tends to spread. This module covers what AI changes about workplace monitoring, the principles that keep it lawful and trusted, the main rules in the US, Canada, the EU and the UK, consultation with employees, works councils and unions, and the CHRO's duty to protect wellbeing and psychological safety as work changes.

   • What AI changes about workplace monitoring
   • Necessity, proportionality, transparency and the main rules
   • Consultation with employees, works councils and unions
   • Wellbeing, psychological safety and fair use of monitoring data

   Exercise. List every system in your organization that collects data about how employees work, from badge readers and call recording to productivity analytics inside office software. For each, write the purpose, who sees the data, whether employees have been told, and whether the data has ever been used in a decision about an individual.

10. 8HR Operations, People Data and Culture

    HR is both a steward of AI's effect on the workforce and a user of AI in its own work. This module covers AI in HR service delivery, the governance of employee data, policies for staff use of AI that HR must apply fairly, how performance management and recognition should change, how the CHRO leads HR's own adoption, and the measures that show whether the people side of AI is working.

    • AI in HR service delivery
    • Employee data governance and staff AI policies
    • Performance management and recognition for AI-assisted work
    • Leading HR's own adoption and measuring the people side of AI

    Exercise. Take the ten most common questions employees bring to HR. For each, decide whether an AI assistant could answer it from approved policy, whether it must always go to a person, and what accuracy you would need before letting the assistant answer it unaided. Then list the employee data that the assistant would need and who should be able to see it.