Overview

Lecture URL: https://skill.re/learn/manager/ai-governance-frameworks.php

AI FOR MANAGERS CERTIFICATION

Strategic AI Leadership (Level 5) | Governance and Policy

LECTURE: AI Governance Frameworks

Lesson 2.1 | Estimated Duration: ~25 minutes

Welcome to the AI for Managers certification program. I am your instructor, and today we are covering one of the essential lessons in the Governance and Policy module: AI Governance Frameworks.

This is Lesson 2.1 in Level 5, the Strategic AI Leadership track. Whether you are joining us as a new manager finding your footing, a seasoned director refining your approach, or a VP setting strategic direction for your organization, the material in this session is designed to meet you where you are and give you something immediately actionable.

In our previous lesson, we covered Communicating AI Strategy Upward. Today we build directly on that foundation. If any of those concepts feel uncertain, I would encourage you to revisit that material before we go further.

Before we begin, let me set expectations. This is not a passive lecture. I will ask you to think, to challenge assumptions, and to connect what we discuss to your own work. The managers who get the most out of this program are those who pause, reflect, and apply. So I encourage you to have a notepad ready, whether physical or digital, and to jot down ideas as they come to you.

Let us get started.

Lesson 01: AI Governance Frameworks

Title

AI Governance Frameworks: Understanding Governance Models and the Manager's Role

Purpose

This lesson introduces established AI governance frameworks and helps you understand the manager's role within them. You'll learn about major frameworks (NIST AI Risk Management Framework, EU AI Act, organizational models), how they work, and how to implement them at the domain level. The focus is not on compliance-as-burden, but on governance as enabling responsible innovation.

Why This Matters for Managers

AI governance has moved from "nice to have" to essential. Without governance:

• Teams use AI without clear decision authority or escalation paths
  - Risk incidents happen and you have no protocol for response
  - Fairness, bias, and ethical issues emerge with no clear ownership
  - Regulatory violations accrue and you face legal/compliance consequences
  - You can't scale AI confidently because you lack controls and visibility

With governance:

• Clear decision authority and accountability
  - Systematic risk management and incident response
  - Proactive identification of fairness and ethical issues
  - Regulatory compliance built in, not bolted on
  - Confidence to scale AI because controls are in place

For you as a manager: Understanding governance frameworks helps you build systems for responsible AI use in your domain. It's not something IT or compliance "does to you"--you're building governance as part of your strategy.

Core Concepts

Governance vs. Compliance

Compliance: Following rules set by someone else (regulators, corporate policy, standards body). It's reactive. "What are we required to do?"

Governance: Establishing decision-making processes, roles, and controls that enable responsible action. It's active. "How do we make good decisions about AI?"

Good governance usually satisfies compliance. Bad governance often violates compliance.

As a manager, you're building governance (how do we decide, who decides, what controls are in place), which then ensures compliance (we follow the rules).

Major AI Governance Frameworks

1. NIST AI Risk Management Framework (AI RMF)

The U.S. National Institute of Standards and Technology released the AI RMF in 2023 to provide a framework for managing AI risks across organizations.

Core concepts:

• AI systems have inputs, processes, and outputs. Risks can emerge at any point.
  - Risk categories: Safety, security, fairness/bias, transparency, accountability
  - Governance approach: Inventory your AI systems, assess risks, implement controls, monitor

How it works:

1. Map: Understand your AI systems and their purpose

• What AI are we using?
  - What's it doing?
  - What could go wrong?

1. Measure: Assess risks

• What's the likelihood something goes wrong?
  - What's the impact if it does?
  - How confident are we in the model?

1. Manage: Implement controls

• How do we prevent or mitigate risks?
  - Who's accountable?
  - What's our escalation process?

1. Govern: Establish decision-making and accountability

• Who approves new AI systems?
  - How do we monitor ongoing use?
  - How do we respond to incidents?

For managers: NIST provides a structured approach to inventory, assess, and manage AI in your domain. You don't need to implement all of NIST; you can adapt it to your scale and context.

1. EU AI Act

The European Union's AI Act (enforced in phases through 2026) establishes legal requirements for AI use and deployment.

Key concepts:

• Risk-based approach: AI systems are categorized by risk level (prohibited, high-risk, limited-risk, minimal-risk)
  - Prohibited AI: Systems that fundamentally violate human rights (e.g., mass surveillance, emotion recognition for hiring/education decisions, social credit systems)
  - High-risk AI: Systems with significant potential for harm (e.g., hiring systems, credit decisions, medical diagnosis, law enforcement). These require extensive documentation, testing, human oversight
  - Limited-risk AI: Systems with some transparency concerns (e.g., chatbots, recommendation systems). These require transparency and disclosure
  - Minimal-risk AI: Systems with no significant risks

For managers: If you operate in EU or have EU customers, understand AI Act requirements. If you're outside EU, the Act is still valuable as a standard for responsible AI governance (other regions are adopting similar frameworks).

Key takeaway: Some uses of AI are not acceptable, and high-risk uses require significant governance and oversight.

1. Organizational Governance Models

Different organizations structure governance differently. Common models:

Decentralized (Department-Level) Governance:

• Each department/function manages AI use within their domain
  - Central governance sets standards and audit
  - Pros: Responsive to local context, faster decisions
  - Cons: Inconsistent standards, harder to share learning, risk of governance gaps

Centralized (AI Center of Excellence) Governance:

• Central team (AI CoE, Data Governance Office) approves all AI use
  - Single authority for standards and decisions
  - Pros: Consistent standards, central learning, clear accountability
  - Cons: Slower decisions, might miss domain-specific context, can become a bottleneck

Federated Governance:

• Central standards and principles; local implementation
  - Central team sets boundaries and principles; each domain implements within those bounds
  - Pros: Consistency with local responsiveness; learning shared across domains
  - Cons: Requires more communication and coordination

For managers: Understand which model your organization uses, and operate within it. If it's decentralized, you have more authority and responsibility. If it's centralized, you need to work with the central team. Either way, you're implementing governance at your level.

Core Elements of AI Governance

Most governance frameworks include these core elements:

1. Inventory and Assessment

Know what AI systems you're using or considering.

• What AI are we using? (Tool names, use cases)
  - What's the risk level? (Low, medium, high)
  - What data does it use?
  - What decisions or actions does it inform?
  - Who uses it?

Manager's role: Maintain an inventory of AI systems in your domain. Understand the basics.

1. Risk Management

Identify risks and implement controls.

Risk categories:

• Safety: Could this AI cause harm to people or systems?
  - Security: Could this system be compromised or misused?
  - Fairness and Bias: Does this system treat people equitably?
  - Explainability: Can we explain why the AI made a decision?
  - Data Quality: Is the data accurate, complete, and representative?
  - Human Autonomy: Does this preserve meaningful human control?

Manager's role: For each significant AI use, assess these risk categories and implement controls (data quality checks, bias testing, escalation protocols, etc.).

1. Decision Authority and Escalation

Who can approve AI use? What requires escalation?

Example escalation framework:

• Green (low risk, auto-approve): AI for internal productivity (coding assistants, writing helpers)
  - Yellow (medium risk, manager approval): AI affecting business operations (customer routing, content moderation) - requires assessment and manager sign-off
  - Red (high risk, executive/governance committee approval): AI affecting people's lives (hiring, medical decisions, loan approvals) - requires full risk assessment, bias testing, compliance review

Manager's role: Establish escalation paths in your domain. Know which AI uses require approval vs. can proceed with guidelines.

1. Monitoring and Incident Response

Systems in place to detect problems and respond.

• How do we know if the AI is working as intended?
  - What's our process if we discover a problem?
  - How do we learn from incidents?

Manager's role: Build monitoring into AI deployments. Create an escalation protocol. Make it safe to report problems without blame.

1. Training and Accountability

People understand governance and their roles in it.

• Do teams know the governance framework and their responsibilities?
  - Do decision-makers know when to escalate?
  - Who's accountable if something goes wrong?

Manager's role: Train your team on governance expectations. Make accountability clear.

Practical Managerial Use Cases

Use Case 1: Building Domain-Level AI Governance

Scenario: You lead a 40-person data analytics function. You're adopting multiple AI tools for data prep, analysis, visualization, and insights. You need to establish governance without bureaucracy slowing down useful work.

Approach:

Step 1: Inventory and Categorize

Create a simple inventory:

• Tool 1: AI-powered data preparation (internal productivity use, low risk)
  - Tool 2: AI-powered exploratory analysis (internal analytical use, low-medium risk)
  - Tool 3: Proposed: Customer churn prediction model (predicts which customers will leave; affects retention strategy, medium-high risk)
  - Tool 4: Proposed: Pricing optimization AI (recommends pricing; directly affects revenue, high risk)

Step 2: Define Escalation Framework

• Green (auto-approve): Internal productivity tools (data prep, visualization helpers); no governance review needed beyond standard IT security
  - Yellow (manager review): Analytical tools affecting business decisions (churn prediction, forecasting); requires: data quality assessment, bias testing if predictions affect people, documentation of model performance, clear hand-off to business stakeholder
  - Red (governance committee): Tools affecting external stakeholders or with high risk (pricing optimization, if it could discriminate); requires: full risk assessment, regulatory review, bias audit, escalation path for edge cases

Step 3: Implement Controls

For each category:

• Green: Standard security + annual refresher training on responsible AI
  - Yellow: Manager assessment checklist + data quality review + bias testing report + model performance dashboard + business owner sign-off
  - Red: Full governance review + legal/compliance review + external audit + implementation controls + quarterly monitoring

Step 4: Train and Communicate

• Brief your team on the framework
  - Make it clear: "Here's how we make decisions about AI. Here's what each level requires. Here's how to escalate."
  - Make it safe to ask: "Not sure if this needs escalation? Ask me."

Step 5: Monitor and Iterate

• Quarterly: Review incidents, escalations, and learnings
  - Adjust controls based on what you learn
  - Share learnings across the organization

Result: You have governance that's proportional to risk, enables responsible innovation, and doesn't require a separate governance team.

Use Case 2: Implementing NIST Framework

Scenario: Your organization is adopting the NIST AI RMF. You're responsible for implementing it in your function (customer service). You have limited budget and staff.

Approach:

Step 1: Map (Know Your Systems)

Create a simple map of AI systems in customer service:

• AI chatbot: Handles routine inquiries, escalates complex ones to humans
  - AI-powered response drafts: Assists agents by suggesting responses
  - Sentiment analysis: Flags emotionally distressed customers for priority attention
  - Knowledge base search: Helps agents find information

For each:

• Purpose: What's it doing?
  - Inputs: What data? From where?
  - Outputs: What decisions or actions?
  - Risks: What could go wrong?

Step 2: Measure (Assess Risks)

For each system, assess risk categories:

AI Chatbot:

• Safety: Low (it doesn't make final decisions)
  - Security: Medium (stores customer data; could be compromised)
  - Fairness: Medium-high (might treat different customer types differently; could provide worse service to certain groups)
  - Explainability: Low (customers don't need to understand why chatbot made a decision)
  - Data Quality: Medium (relies on clean training data)
  - Human Autonomy: High (unclear when human takes over from bot; causes frustration)

Overall risk: Medium -> requires controls and monitoring

Step 3: Manage (Implement Controls)

For the chatbot, implement controls:

• Fairness: Test for bias quarterly; monitor escalation rates by customer demographics; adjust if disparities emerge
  - Security: Encrypt customer data; limit who can access; audit access regularly
  - Human Autonomy: Clear escalation criteria; customer can always reach human; measure escalation rates and customer satisfaction with escalations
  - Data Quality: Regular training data quality reviews; catch drifts in customer requests

Step 4: Govern (Establish Accountability)

• Owner: Customer Service Manager (you) is accountable for chatbot governance
  - Decision authority: You decide when to adjust chatbot, when to escalate to governance committee
  - Monitoring cadence: Monthly automated checks; quarterly manual review; annual full assessment

Result: You've implemented NIST's "map, measure, manage, govern" framework at a scale appropriate for your function.

Use Case 3: Navigating Regulatory Requirements

Scenario: Your organization operates in EU and uses an AI hiring tool (resume screening, interview scoring). The EU AI Act categorizes hiring AI as high-risk. You need to understand and implement the requirements.

Approach:

Understand the Requirements (EU AI Act, High-Risk Category):

• Documentation: Maintain detailed documentation of system design, training data, performance, and testing
  - Testing and Validation: Conduct and document bias testing, accuracy testing, and performance assessment
  - Risk Assessment: Document foreseeable risks and mitigation
  - Transparency: Candidates must know they're being evaluated by AI and have right to challenge
  - Human Oversight: Human reviewers must be involved in hiring decisions; candidates can request human review
  - Data Quality: Training data must be representative and non-discriminatory
  - Record-keeping: Maintain records demonstrating compliance

Implementation Approach:

1. Audit Current System:

• Is the system documented? (Likely not to EU standards)
  - Has bias testing been done? (Probably not formally)
  - Are there controls around human oversight? (Probably minimal)
  - Does the vendor provide compliance support? (Maybe partial)

1. Build Compliance Plan:

• Month 1-2: Documentation audit; create risk assessment report
  - Month 2-3: Bias testing; accuracy testing; fairness audit
  - Month 3-4: Implement transparency (candidate disclosures; appeal process)
  - Month 4+: Ongoing monitoring and quarterly compliance reviews

1. Establish Governance:

• HR and Legal own compliance
  - Manager (you) implements in hiring process
  - Escalation: Any bias or fairness concerns -> legal/compliance review before taking hiring action

1. Train Your Team:

• Recruiters understand that AI is a tool, not a decision-maker
  - They must know the appeal process
  - They must be prepared to explain decisions if challenged

Result: You've moved from "using an AI hiring tool" to "using an AI hiring tool in a compliant way, with proper controls."

Anti-Patterns & Misuse Risks

Anti-Pattern 1: Governance as Obstruction

The problem: Using governance as a reason to block innovation.

Example: "We can't try that AI tool because it's not on the approved list, and getting approval takes 3 months."

Why it fails:

• Teams find workarounds (shadow IT; tools used outside governance)
  - Innovation stalls
  - Governance loses legitimacy ("It's just red tape")
  - Risk actually increases (unsanctioned use is less controlled)

Better approach: Governance should enable, not block. Design for speed.

• Low-risk tools: Auto-approved with just IT security review
  - Medium-risk tools: Fast-track approval (1-2 weeks)
  - High-risk tools: Full review, but clear timeline

Speed + governance beats slow governance every time.

Anti-Pattern 2: Governance Without Teeth

The problem: Beautiful framework that people ignore because there's no accountability.

Example: AI governance policy exists, but no one's enforcing it. Teams use AI however they want. No one gets in trouble.

Why it fails:

• Governance doesn't actually govern anything
  - Risk incidents happen and you have no framework to respond
  - Compliance violations accumulate
  - When regulators ask "What's your AI governance?" you have a document, not a practice

Better approach: Governance requires accountability.

• Make it clear: governance policy is mandatory
  - Audit compliance quarterly
  - If someone violates policy, address it (coaching, escalation, consequences)
  - Make it safe to report incidents, not safe to hide them

Anti-Pattern 3: One-Size-Fits-All Governance

The problem: Same governance and approval process for low-risk and high-risk AI.

Example: Using an AI writing assistant for internal emails requires the same governance and approval as an AI hiring system.

Why it fails:

• Bureaucracy for low-risk work
  - Slow-rolling high-risk approvals
  - Frustration and workarounds

Better approach: Risk-based governance.

• Low-risk AI (internal tools, productivity helpers): Fast track or auto-approve
  - Medium-risk AI (business operations, analytics): Standard review, manager approval
  - High-risk AI (decisions affecting people, regulatory): Full governance review

Proportional oversight; faster decisions overall.

Anti-Pattern 4: Ignoring Fairness and Bias

The problem: Governance that focuses on security, privacy, and compliance but ignores fairness and bias.

Example: "The hiring AI is secure, compliant with GDPR, well-documented. We haven't tested it for bias because that's too hard."

Why it fails:

• Bias issues emerge and create PR/legal problems
  - Regulatory bodies increasingly expect fairness assessments
  - Team morale suffers if AI is perceived as discriminatory
  - You miss opportunity to build trust in AI

Better approach: Fairness is part of governance.

• Test AI systems for bias before deployment
  - Monitor for fairness issues over time
  - Have a clear escalation path if bias is discovered
  - Be transparent: "We've tested for bias; here are the results and limitations."

Anti-Pattern 5: Governance Without Learning

The problem: Governance system that documents but doesn't learn.

Example: You have a governance framework, you've escalated incidents, but you're not analyzing patterns or improving the framework based on what you learn.

Why it fails:

• Same problems happen repeatedly
  - Framework becomes ossified
  - Opportunity to improve is missed

Better approach: Learning loop.

• Quarterly: Review incidents, escalations, near-misses
  - Analyze patterns: What types of risks are we encountering most?
  - Adjust framework: Do we need new controls? Different escalation paths? More training?
  - Share learnings: What can other teams learn from our experience?

Human Judgment Checkpoints

Checkpoint 1: The Risk Assessment Test

For each significant AI system in your domain, can you answer:

• What risk level is it? (Low, medium, high)
  - What are the top 3 risks?
  - What controls are in place?
  - Who's accountable if something goes wrong?

If you can't answer these, you need stronger risk assessment.

Checkpoint 2: The Escalation Path Test

If you discovered a significant problem with an AI system (bias issue, security vulnerability, model performing poorly), could you clearly describe:

• Who you'd escalate to?
  - What information you'd provide?
  - What you'd expect to happen next?

If you're unclear, your escalation framework needs work.

Checkpoint 3: The Compliance Reality Test

For your regulatory context (GDPR, AI Act, industry regulations, etc.), does your governance address:

• Documentation requirements?
  - Transparency requirements?
  - Testing/validation requirements?
  - Human oversight requirements?
  - Data rights (right to appeal, right to explanation)?

If you're missing any, that's a compliance gap.

Checkpoint 4: The Team Clarity Test

Ask 3 random team members: "If you wanted to try a new AI tool, what would you do?" Do they know:

• What the approval process is?
  - What level of review it requires?
  - How long it takes?

If they don't know, communication needs improvement.

Checkpoint 5: The Fairness Reality Check

For AI systems affecting people (hiring, customer service routing, content moderation, etc.), ask:

• Have we tested for bias?
  - Did we find any disparities?
  - What are we doing about it?

If you haven't tested, that's a governance failure.

Responsible AI Considerations

Fairness and Bias in Governance

Your governance framework should explicitly address fairness:

• Testing for bias before deployment (is the system fair to different groups?)
  - Monitoring for fairness issues over time (are we maintaining fairness as the system operates?)
  - Clear escalation for fairness issues (if we discover bias, what happens?)
  - Transparency about limitations (can we explain how the system might be unfair?)

Transparency and Explainability

Governance should include requirements for transparency:

• Can we explain why the AI made a specific decision?
  - Can stakeholders understand how the AI works?
  - Are people told they're interacting with AI?
  - Do they have recourse if they disagree?

Human Autonomy and Accountability

Governance must preserve meaningful human control:

• Humans don't abdicate decision-making to AI
  - Humans remain accountable for AI-supported decisions
  - Escalation paths exist for edge cases and disagreements
  - People can challenge or appeal AI decisions

Data Governance

AI governance intersects with data governance:

• Is data accurate, complete, and representative?
  - Who has access to data?
  - How long is data retained?
  - Is data handling compliant with privacy regulations?

Practice & Reflection Prompts

Prompt 1: AI Governance Inventory

List all AI systems and tools currently in use or planned in your domain:

• Tool name
  - Use case
  - Risk level (low, medium, high)
  - Current governance (if any)
  - Gaps in governance

Prompt 2: Risk Assessment Framework

For your highest-risk AI system:

• Identify risks in each category: safety, security, fairness, explainability, data quality, human autonomy
  - For each risk, assess: likelihood (low, medium, high) and impact (low, medium, high)
  - Define controls to mitigate top risks
  - Identify who's responsible for each control

Prompt 3: Escalation Path Design

Create a simple escalation framework:

• What AI decisions/situations require escalation?
  - To whom?
  - What information do they need?
  - What's the expected response time?
  - What happens after escalation (investigation, adjustment, pause, etc.)?

Prompt 4: Fairness Assessment

For AI systems affecting people (if applicable):

• What groups might be affected?
  - How could the system be unfair to any group?
  - How will you test for bias?
  - What metrics will you monitor over time?
  - How will you respond if you discover fairness issues?

Prompt 5: Governance Communication Plan

Write a plan to communicate governance to your team:

• What does each person need to understand?
  - How will you train them? (Documentation, workshop, ongoing, embedded in process)
  - How will you keep governance alive? (Regular reminders, quarterly reviews, incident learning)
  - How will you get feedback? (Safe channel to ask questions, suggest improvements)

Key Takeaways

1. Governance is about decision-making and control, not just compliance. Good governance enables responsible innovation; bad governance just blocks things.
2. Risk-based governance is proportional. Low-risk AI is fast-tracked; high-risk AI gets full review. This accelerates decision-making overall.
3. NIST AI RMF provides a practical framework (map, measure, manage, govern) that works at any scale. You don't need to implement all of it; adapt it to your context.
4. Regulatory requirements are real and increasing. If you operate in EU, under GDPR, in healthcare, or other regulated domains, AI governance includes regulatory compliance. Build it in from the start.
5. Governance isn't one team's job; it's a shared responsibility. You (manager) are accountable for governance in your domain. You partner with legal, security, compliance, and IT, but you're not outsourcing responsibility.
6. Fairness and bias must be part of governance. Not an afterthought. Test before deployment; monitor over time; escalate if issues emerge.
7. Escalation paths are critical. When problems occur (and they will), you need to know who to contact and what happens next. Make this clear and safe to use.
8. Governance evolves as you learn. Quarterly reviews of incidents and escalations help you improve the framework. Static governance becomes irrelevant; learning governance gets better.

Terms & Glossary

AI Governance: Decision-making processes, roles, and controls that ensure responsible AI use.

NIST AI Risk Management Framework: U.S. standard framework for identifying, assessing, and managing AI risks (map, measure, manage, govern).

EU AI Act: European regulation establishing legal requirements for AI systems, categorized by risk level.

Risk Level: Classification of AI systems by potential for harm (low, medium, high).

Risk Categories: Types of risks in AI systems (safety, security, fairness, explainability, data quality, human autonomy).

Escalation Path: Process for reporting and responding to problems or concerns.

Bias / Fairness Testing: Assessment of whether AI system treats different groups equitably.

Human Oversight: Requirement that humans remain involved in decision-making, not fully automated by AI.

Related Lessons

• Lesson 02: Developing Team and Department Policies - Operationalizes governance framework into specific policies
  - Lesson 03: Risk Management and Escalation - Details how to manage risks and escalate problems
  - Lesson 04: Ethical Leadership in AI Adoption - Governance is one part of ethical leadership; leadership modeling is another
  - Chapter 01, Lesson 03: Measuring AI Impact and ROI - Measurement is part of governance (monitoring that systems are working)
  - Chapter 03, Lesson 02: Building Organizational AI Culture - Culture supports governance (people want to do the right thing)

Next: Move to Lesson 02 to develop practical policies for your team and department.

[SYNTHESIS AND APPLICATION]

Let us step back and look at the bigger picture of what we have covered in this session on AI Governance Frameworks.

The concepts here are not abstract frameworks meant to sit in a binder on your shelf. They are practical tools for the decisions you make every day as a manager. Whether you are leading a small team or a large department, whether you work in technology, finance, healthcare, education, or any other sector, the principles we discussed apply to your work right now.

Here is what I want you to take away from this session:

First, the conceptual understanding. You now have a clearer mental model of ai governance frameworks and how it fits into the broader landscape of AI-augmented management. This mental model is what allows you to make good decisions rather than reactive ones.

Second, the practical application. We walked through specific scenarios, examples, and frameworks that you can apply in your work this week. Not next quarter. This week. I want you to identify one specific situation in your current work where you can apply what we discussed today.

Third, the judgment dimension. Perhaps most importantly, we discussed when and how to exercise human judgment. AI is a powerful tool, but it requires an informed, thoughtful manager at the helm. That is you. Your judgment, your context awareness, your understanding of your team and your organization, those are irreplaceable.

[REFLECTION EXERCISE]

Before we close, I would like you to spend two minutes, just two minutes, on this reflection:

Think about your work this past week. Identify one task, one decision, one communication where the concepts from today's lesson would have changed your approach. What would you have done differently? What would the outcome have been?

Write that down. That connection between concept and practice is where real learning happens.

[CLOSING REMARKS]

In our next lesson, we will explore Developing Team and Department Policies, which builds directly on what we have covered today. I would encourage you to complete the reflection exercises before moving on, as they will prepare you for the next set of concepts.

This has been Lesson 2.1: AI Governance Frameworks, part of the Governance and Policy module in Level 5: Strategic AI Leadership of the AI for Managers certification.

Remember: the goal is not to know more about AI. The goal is to be a better manager because of how you use AI. Those are very different things, and this program is designed for the latter.

Thank you for your time, your attention, and your commitment to growing as a leader in an AI-transformed workplace. I look forward to our next session together.

END OF TRANSCRIPT

AI for Managers Certification Program

Level 5: Strategic AI Leadership | Governance and Policy | Lesson 2.1

A SkillsClinic initiative by No Worker Left Behind and The Work Company.

Duration: ~25 minutes | Word Count: ~3865