Certified AI Executive: Chief Operating 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
• Redesign end-to-end processes with AI, deciding where to automate, augment or keep people in charge, and check the effect with capacity math
• Judge and run AI in forecasting, maintenance, quality inspection and customer service against the measures customers feel
• Run AI reliably in production, with clear measures, escalation, a tested fallback and control of outsourcing partners that use AI
• Lead frontline adoption, redesign workload safely, measure productivity honestly and scale what works across sites

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 Operating Officer

   Written for chief operating officers.

7. 5Redesigning Operations and Processes with AI

   AI pays off in operations only when the work around it is redesigned. This module looks at that redesign from the COO's seat: mapping how work really flows, deciding where to automate, where to augment and where to leave people in charge, designing human review that works, placing AI agents safely inside workflows, and using throughput and capacity math to check that a change will move the result.

   • Mapping processes and finding the real constraint
   • Automate, augment or leave alone
   • Human-in-the-loop design and AI agents in workflows
   • Throughput, capacity and queue math for redesign

   Exercise. Pick one end-to-end process your organization runs at volume. Map its steps, the time each item spends being worked on and waiting, and the step with the least capacity. Then name one step to automate, one to augment and one to leave with people, and estimate the effect of each on total lead time.

8. 6Supply Chain, Service and Quality Operations

   Some of the clearest returns from AI in operations come from planning, maintenance, inspection and customer service. This module covers how to judge AI demand forecasts and their effect on inventory, when predictive maintenance pays, how to set and test AI quality inspection, and how to run AI in customer service without hiding poor service behind good-looking measures.

   • Demand forecasting, forecast error and inventory
   • Predictive maintenance and equipment effectiveness
   • AI quality inspection and the cost of each error
   • Customer service operations and service levels

   Exercise. Choose one planning, maintenance, inspection or service process that uses or plans to use AI. Write down the measure customers or the business actually feel (stock-outs, downtime, escapes or unresolved contacts), its current baseline and the one operational measure that could make the AI look better than it is.

9. 7Running AI in Production and Staying Resilient

   Once AI is part of daily operations, the COO is accountable for it working every day and for the business carrying on when it does not. This module covers the operational measures for AI-enabled work, monitoring live performance, handling exceptions and escalation, operational resilience and business continuity when AI fails, including the expectations of financial regulators in the UK, Canada, the EU and the US, and managing outsourcing and BPO partners that use AI.

   • Operational KPIs and live performance monitoring
   • Exceptions, escalation and the authority to stop
   • Operational resilience and continuity when AI fails
   • Outsourcing and BPO partners that use AI

   Exercise. Take one important service that depends on AI. Write down the longest disruption it could bear before customers or the business suffered serious harm, the fallback that would run if the AI stopped, the staff and hours that fallback would need, and the last date it was tested.

10. 8The Frontline Workforce, Safety and Scale

    Most of an operation's AI value is earned or lost on the front line: in warehouses, plants, branches, contact centers and field teams. This module covers frontline adoption, redesigning workload and jobs so that AI does not simply speed people up, workplace health and safety duties when AI directs work or works beside people, measuring productivity honestly, and scaling what works across many sites.

    • Frontline adoption on shifts, sites and in the field
    • Workload, pacing and job redesign
    • Workplace health and safety with AI
    • Honest productivity measures and scaling across sites

    Exercise. For one AI tool used by frontline staff, compare output per paid hour, quality and recorded injuries or near misses before and after it was introduced, at a site that uses it and at one that does not. Write down how much of the change you can fairly attribute to the tool, and what the comparison cannot tell you.