Artificial Intelligence: A Board Member and Senior Leadership Workshop

Unlock AI's Potential for Strategic Leadership

AI Literacy for Board Members & Executives

Join us for a transformative workshop designed to equip senior leaders with the essential AI knowledge needed to drive strategic decisions and governance.

Schedule a 15-minute strategy call with our AI CoE experts to see how this workshop can elevate your organization’s AI capabilities.

Schedule your call!
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Empowering Leaders with AI Insights

A.J. Rhem & Associates workshops are crafted to bridge the gap between AI advancements and executive decision-making.

Tailored for corporate board members and senior leadership, they provide a comprehensive understanding of AI technologies, their applications, and governance challenges.

These workshops are structured so that participants gain insights into machine learning, deep learning, and ethical AI considerations, preparing them to navigate the complexities of AI implementation in their organizations.

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ONE-DAY AI WORKSHOP FOR BOARD MEMBERS & SENIOR LEADERSHIP

Objective: Provide foundational understanding of AI concepts, governance, and practical applications to support informed strategic decisions at the board level.

Agenda Overview

9AM - 9:30AM
Welcome & Workshop Goals

Set expectations and discuss the strategic importance of AI for boards.

9:30AM - 10:30AM
AI Fundamentals for Leaders

Overview of AI, machine learning, deep learning, neural networks, Generative AI.

10:30AM - 10:45AM
Break
10:45AM -12PM
AI Agents & Agentic AI

Explore emerging models for AI Agents, Agentic AI, and autonomy in AI systems.

12PM - 1PM
Lunch
1PM -2:15PM
AI Use Cases Across Industries

Vertical-specific applications: healthcare, legal, financial services, government.

2:15PM -3PM
AI Risk, Ethics, and Governance

Understanding ROI, risks, bias, explainability, NIST/IEEE/OECD frameworks.

3PM - 3:15PM
Break
3:15PM -4:15PM
Board-Level Responsibilities in AI Oversight

Case study: flawed talent management system & controversial facial recognition startup

Case study: Industry/Organization specific cases

4:15PM - 5PM
Executive Polling + Panel Discussion

Group discussion and polling on AI investment, strategy, and ethical alignment.

5PM
Wrap-Up & Call to Action

How to lead responsibly and integrate AI Policy & Governance in board and Senior Leadership duties.

Key Takeaways

Participants will gain a comprehensive understanding of AI concepts tailored to board-level needs, enabling strategic decision-making. The workshops provide insights into regulatory frameworks, ethical considerations, and tools for effective oversight of AI initiatives. Attendees will leave equipped with practical strategies to navigate AI’s complexities within their organizations.

AI Literacy for Leaders

Governance Frameworks

Ethical AI Practices

Strategic Oversight Tools

Schedule a 15-minute strategy call with our AI CoE experts to see how this workshop can elevate your organization’s AI capabilities.

Schedule your call!

TWO-DAY ADVANCED AI WORKSHOP FOR BOARD MEMBERS & SENIOR LEADERSHIP

Objective: Deepen understanding of AI’s organizational impact, ethical/legal governance, and operational strategy, with hands-on simulations and in-depth case analysis.

Agenda Overview

Day 1: Strategy, Fundamentals, and Frameworks

9AM - 9:30AM
Welcome & Workshop Goals

Clarify goals for advanced engagement.

9:30AM - 11AM
In-Depth AI Technologies Primer

Advanced understanding of ML, DL, LLMs, Generative AI, Agentic AI.

11AM - 11:15AM
Break
11:15AM -12:30PM
AI Use Cases + Strategic Value Chains

Focused discussions on AI integration across key business functions.

12:30PM - 1:30PM
Lunch
1:30PM -3PM
AI Governance & Regulatory Alignment

Compare NIST, IEEE CertifAIEd, OECD, EU AI Act, US Executive Orders.

2:15PM -3PM
AI Risk, Ethics, and Governance

Understanding ROI, risks, bias, explainability, NIST/IEEE/OECD frameworks.

3PM - 3:15PM
Break
3:15PM - 5PM
Board-Level Responsibilities in AI Oversight

Deep dive: Flawed talent management AI system. Simulation and board decision-making exercise.

4:15PM - 5PM
Case Lab: Governance Failure | Industry specific cases

Group discussion and polling on AI investment, strategy, and ethical alignment.

Day 2: Risk, Ethics, and Executive Oversight

9AM - 10:30AM
AI Risk Assessment Frameworks

Bias, fairness, transparency, explainability, data lineage.

10;30AM - 10:45AM
Break
10:45AM - 12PM
Case Lab: Controversial Facial Recognition Startup | Organization specific cases

Analyze impact, stakeholder backlash, and board response options.

12PM - 1PM
Lunch
1PM - 2:30PM
Building an AI Risk Committee / Oversight Strategy

How to organize internal governance structures.

2:30PM - 3PM
Group Polling & Reflection

Guided peer-learning and confidence polling on AI topics.

3PM - 4:30PM
Future of AI, Board & Corporate Readiness Roadmap

AI futures thinking, next-gen models, and preparing a board-level AI plan.

4:30PM - 5PM
Wrap-Up & Executive Action Planning

Each participant creates a tailored AI oversight action plan.

Advanced Takeaways

Participants will develop an expert-level grasp of AI systems and their enterprise implications. This includes a working knowledge of AI architectures, governance assessment, and regulatory alignment. Attendees will leave with actionable frameworks for strengthening oversight, mitigating risk, and ensuring responsible AI integration at the executive level.

AI Capabilities and Architecture

Governance Failure Analysis

Global AI Regulations

Executive Playbook for Oversight and Alignment

Challenges Facing Corporate Boards

Corporate boards face significant challenges in integrating AI, including understanding its return on investment, managing associated risks, and ensuring ethical deployment. Navigating these challenges requires a clear grasp of AI’s impact on business operations and the ability to incorporate AI into strategic planning effectively.

How can AI impact ROI for businesses?

AI can enhance ROI by optimizing operations, improving decision-making, and creating new revenue streams. However, it requires careful investment and strategic alignment to realize these benefits.

What are the key risks associated with AI adoption?

Key risks include data privacy concerns, algorithmic bias, and potential regulatory non-compliance. Addressing these risks involves robust governance and continuous monitoring.

How do ethical considerations affect AI implementation?

Ethical considerations are crucial as they influence public trust and legal compliance. Implementing AI ethically involves ensuring transparency, fairness, and accountability in AI systems.

What frameworks exist for AI governance?

Several frameworks guide AI governance, including international standards and industry-specific guidelines. These frameworks help organizations align AI initiatives with best practices and regulatory requirements.

How can boards ensure effective oversight of AI projects?

Boards can ensure effective oversight by fostering AI literacy, establishing clear governance structures, and engaging in continuous education on AI advancements and implications.

Reserve your organization’s workshop slot today – Limited spots are available!

Schedule a 15-minute strategy call with our AI CoE experts to see how this workshop can elevate your organization’s AI capabilities.

Contact Us

Join Our AI Workshops

Take the next step in advancing your organization’s AI capabilities. Contact us today for more information and secure a spot in this transformative learning experience.

Find Out More

Artificial intelligence creates value only when organizations can trust how it is selected, designed, developed, deployed, used, and monitored. AJRA helps organizations move beyond high-level ethical statements and isolated policies to establish an operating model for AI governance—one that supports innovation while protecting people, the enterprise, and the communities the organization serves.

Our approach integrates AI strategy, risk management, ethics, data and knowledge governance, technology architecture, regulatory readiness, organizational change, and independent assurance. Governance is embedded across the AI lifecycle rather than added after a system is already in production.

What Makes the AJRA Approach Different

Enterprise-focused

We connect AI governance to business strategy, operating priorities, risk tolerance, and measurable value—not technology alone.

Knowledge-grounded

We address the quality, ownership, provenance, context, and flow of the organizational knowledge used by AI systems, including generative and agentic AI.

Lifecycle-based

Governance begins with ideation and intake and continues through design, testing, deployment, monitoring, change, retirement, and incident response.

Risk-proportionate

Oversight and controls are scaled to the potential impact of each AI use case, system, model, agent, vendor, and decision pathway.

Evidence-driven

We define the documentation, metrics, testing results, approvals, logs, and monitoring evidence leaders should expect before trusting an AI system.

Human-centered

Accountability remains with people. Human oversight, contestability, accessibility, fairness, privacy, transparency, and safety are designed into governance decisions.

Standards-aligned

The governance model can be mapped to recognized frameworks and obligations, including the NIST AI RMF, ISO/IEC 42001, IEEE CertifAIEd™, and applicable laws and sector requirements.

AJRA's Governance Philosophy

AI governance should not become policy theater or an innovation bottleneck. It should create the decision rights, evidence, controls, and accountability needed to use AI responsibly and at scale.

The AJRA AI Governance Lifecycle

AJRA applies a structured, six-stage lifecycle that translates governance principles into repeatable management practices and operational controls.

01

Discover and Establish the Baseline

Identify AI use cases, models, algorithms, generative AI tools, agents, data sources, vendors, business owners, users, and decision impacts. Assess AI readiness and maturity, surface shadow AI, document dependencies, and establish a fact-based view of current capabilities and risks.

02

Define Governance and Accountability

Establish the governance charter, decision rights, roles, committees, escalation pathways, risk ownership, acceptable-use boundaries, and executive and board reporting. Align AI governance with enterprise risk, cybersecurity, privacy, legal, compliance, procurement, data governance, model risk management, and knowledge management.

03

Classify Risk and Assess Impact

Apply a risk-tiering method based on purpose, autonomy, affected stakeholders, decision criticality, data sensitivity, legal exposure, model complexity, and potential harm. Conduct AI impact, ethics, privacy, security, bias, transparency, and vendor assessments proportionate to the risk level.

04

Design and Implement Controls

Embed policies, standards, review gates, testing requirements, human oversight, data and knowledge controls, documentation, traceability, access controls, prompt and agent safeguards, approval criteria, procurement requirements, and incident procedures within the AI lifecycle and existing workflows.

05

Validate, Approve, and Assure

Determine whether governance is working through evidence—not policy existence. Review model and system documentation, evaluation results, risk treatment, accountability, monitoring plans, and residual risk before deployment. Where appropriate, conduct independent or IEEE-aligned third-party assessments.

06

Monitor, Improve, and Report

Track performance, drift, bias, hallucination, misuse, security, privacy, incidents, human overrides, vendor changes, regulatory developments, and realized value. Provide leadership with meaningful indicators and continuously improve the AI governance management system.

Governance Across the Enterprise

Effective governance requires more than model controls; it requires coordinated management across eight interconnected domains.

Strategy and Value

Business alignment, use-case prioritization, risk appetite, value realization, and responsible innovation.

Representative outputs

AI strategy, roadmap, use-case portfolio, value and risk criteria.

Leadership and Accountability

Board and executive oversight, decision rights, ownership, escalation, and organizational accountability.

Representative outputs

Governance charter, RACI, committee model, reporting structure.

Policy and Compliance

Enterprise policies, regulatory mapping, standards alignment, and audit readiness.

Representative outputs

AI policy suite, compliance matrix, control library, evidence requirements.

Data, Information, and Knowledge

Data quality, provenance, privacy, metadata, knowledge sources, content authority, and retrieval grounding.

Representative outputs

Data and knowledge controls, source requirements, lineage and provenance rules.

Model, System, and Agent Risk

Validation, robustness, bias, transparency, autonomy, human oversight, security, and lifecycle management.

Representative outputs

Risk tiering, impact assessment, model and system cards, testing and approval gates.

Third-Party and Procurement

Vendor due diligence, contractual protections, transparency, performance, monitoring, and exit planning.

Representative outputs

Vendor assessment, procurement standards, contract control requirements.

People and Change

AI literacy, role redesign, competence, responsible use, adoption, and culture.

Representative outputs

Training, communications, role-based guidance, change and adoption plan.

Monitoring and Assurance

Metrics, incidents, drift, control effectiveness, audits, and continual improvement.

Representative outputs

Dashboards, KRIs and KPIs, audit plan, monitoring and improvement backlog.

How AJRA Helps

Engagements are tailored to the organization's maturity, regulatory environment, technology portfolio, and business objectives. Services may be delivered independently or combined into an integrated AI governance program.

AI Readiness and Maturity Assessment

Evaluate governance, leadership, workforce, process, data, knowledge, technology, risk, and operational capabilities; identify gaps and prioritize an actionable roadmap.

Responsible AI Strategy and Operating Model

Define how the organization will pursue AI value responsibly, including governance structure, decision rights, funding, accountability, and integration with enterprise management processes.

AI Policy, Standards, and Procedures

Create practical policy and control requirements covering acceptable use, generative AI, agentic AI, model and system lifecycle, data and knowledge use, human oversight, procurement, monitoring, and incidents.

AI Inventory, Risk Tiering, and Impact Assessment

Establish an authoritative inventory; classify systems and use cases; assess ethical, legal, operational, privacy, security, workforce, and societal impacts.

AI Audit and Independent Assurance

Evaluate whether governance controls are designed appropriately, operating effectively, and supported by defensible evidence. Provide findings, risk ratings, remediation actions, and leadership-ready reporting.

IEEE-Aligned AI Ethics Assessments

Perform third-party assessments aligned with the IEEE CertifAIEd™ framework to examine accountability, transparency, privacy, algorithmic bias, and other ethical criteria applicable to the system.

AI Vendor and Procurement Assessment

Assess vendor claims, model transparency, data practices, security, performance, contractual risk, monitoring, and organizational fit before purchase or renewal.

Governance Training and AI Literacy

Build role-based competence for boards, executives, governance bodies, risk and compliance teams, technology teams, business users, and AI system owners.

Implementation and Operationalization

Translate recommendations into workflows, review gates, templates, dashboards, control evidence, governance routines, and continuous-monitoring practices.

Aligned with Recognized AI Governance Frameworks

AJRA does not force every organization into a single framework. We create an integrated control environment that uses the most relevant standards, laws, and industry practices for the organization's context.

NIST AI Risk Management Framework

Supports a structured approach to GOVERN, MAP, MEASURE, and MANAGE AI risk and trustworthiness across the lifecycle.

ISO/IEC 42001

Provides management-system requirements for establishing, implementing, maintaining, and continually improving an Artificial Intelligence Management System.

IEEE CertifAIEd™

Provides an applied ethics assessment approach addressing accountability, transparency, privacy, algorithmic bias, and related ethical criteria.

EU Artificial Intelligence Act

Informs risk classification, prohibited and high-risk practices, transparency, documentation, human oversight, AI literacy, and other obligations where applicable.

Sector and Enterprise Requirements

Integrates relevant financial services, insurance, healthcare, public-sector, cybersecurity, privacy, procurement, records, and model-risk obligations with existing governance.

What Clients Can Expect

A clear and current inventory of AI systems, use cases, models, agents, vendors, owners, data and knowledge dependencies, and risk classifications.

A governance operating model with accountable roles, decision rights, review forums, escalation paths, and executive and board oversight.

Policies and controls that are usable within real development, procurement, deployment, and business workflows.

Risk and impact assessments that distinguish acceptable experimentation from unmanaged enterprise exposure.

Documented evidence that supports regulatory inquiries, internal audit, customer due diligence, third-party assurance, and leadership decisions.

Metrics that show whether AI is trustworthy, compliant, adopted, controlled, and producing intended business value.

A prioritized improvement roadmap that balances immediate risk reduction with long-term governance maturity.

The Result

An AI governance capability that helps the organization innovate faster, because leaders, employees, customers, regulators, and partners can see how AI decisions are governed and why the organization's controls can be trusted.

Build Governance That Works in Practice

Whether your organization is beginning its AI journey, scaling generative AI, deploying autonomous agents, evaluating third-party solutions, or preparing for regulatory and assurance requirements, AJRA can help you establish governance that is practical, proportionate, and aligned with your mission.

Schedule an AI Governance Consultation