AI at Work

• Welcome & Introduction

  Meet your instructor, understand the program structure, and learn why AI fluency is the most critical leadership skill of this decade.

• Introduction to AI & The Transformer Architecture

  Build a rock-solid understanding of what AI actually is, how it evolved, and the transformer architecture that powers the current revolution.

• GenAI Capabilities & The Art of Prompting

  Understand what generative AI can actually do today, then master the skill of prompting, the new interface between humans and AI.

• GenAI Strategy

  Move from experimentation to execution. Build an AI strategy that aligns with business objectives and creates sustainable competitive advantage.

• Agentic AI

  The next frontier, AI systems that can plan, reason, and take autonomous action. Understand agentic architectures and what they mean for your organization.

• AI Use Case Development

  Learn how to identify, evaluate, and prioritize AI use cases that drive real business value, not just impressive demos.

• Data Content Quality

  AI is only as good as its data. Understand data quality, content pipelines, and the foundational infrastructure that determines AI success or failure.

• Governing GenAI

  Build the governance frameworks, policies, and oversight structures required to deploy AI responsibly at enterprise scale.

• Business Transformation & Reskilling

  AI changes how organizations operate and how people work. Lead the transformation in culture, skills, and operating models.

• Bringing It All Together

  Synthesize the full program into an actionable framework. Your AI journey starts now, this training only works if you take action.

Architecture & Technical

• Enterprise AI Architecture

  How AI systems are built and integrated inside a large organization. Key architectural decisions every leader should understand before committing to a platform.

• AI & Privacy

  What privacy means in an AI context, where data goes, how models are trained, and what regulators expect from organizations deploying AI with personal data.

• Evaluating AI Vendors

  The questions every organization must ask before signing with an AI vendor. How to evaluate capability claims vs. marketing and spot red flags before they become problems.

• Vibe Coding & AI-Assisted Development

  What vibe coding is and why it is changing software development. What non-technical leaders need to know to oversee AI-assisted development teams.

Leadership & Governance

• AI & the Board

  What board members are responsible for when it comes to AI oversight. The five questions every board should be asking management, and the fiduciary dimensions of AI risk.

• AI for CEOs

  The CEO's five decisions that no one else in the organization can make. Why AI transformation fails when treated as a technology project, and how to lead it right.

• AI for Managers

  Practical guidance for leading a team through the shift to working with AI: setting expectations, coaching direct reports, and managing the change in how work gets done.

• AI for Champions

  Deeper enablement for the ambassador network that drives day-to-day adoption: coaching prompting, spotting use cases, and helping colleagues put AI to work.

• Working in the Age of AI

  The doing-to-directing shift, what AI is taking over, where human judgment grows in value, and what actually stays human. Includes a function-by-function breakdown, a self-audit framework, and five moves for professionals whose roles are at risk.

• AI & Ethics in Practice

  Moving from principles to policy, how organizations build ethical AI in practice. Covers bias, transparency, accountability, and building governance that holds up under scrutiny.

• AI & Cybersecurity

  What every leader needs to understand about the threats AI creates and the defenses it enables. Covers AI-enabled attacks, new attack surfaces, the insider threat redefined, and the minimum security posture every organization needs before deploying AI broadly.

• Measuring AI ROI

  How to build the business case, establish the right KPIs, and hold your organization accountable for capturing real value. Covers the baseline imperative, the cost/revenue/risk framework, attribution, time horizons, and the governance model that makes measurement stick.

By Role: Function Tracks

• AI for Finance

  How AI is changing financial planning, analysis, and reporting. CFO oversight responsibilities, model risk, and where AI creates real efficiency versus where human judgment remains essential.

• AI for Marketing

  AI's impact on content creation, personalization, and campaign management. How to use AI to generate customer insight at scale while maintaining brand integrity.

• AI for Administrative Professionals

  How AI tools are reshaping scheduling, document management, and coordination. Practical use cases that save real time, without requiring a technical background.

• AI for Risk & Compliance

  The risk and compliance implications of AI at enterprise scale. Model risk, algorithmic bias, and how to build a compliance posture that enables AI adoption rather than blocking it.

• AI for Auditors

  How AI changes the audit landscape, both as a tool auditors use and as a subject of audit review. Covers AI-assisted audit techniques and evaluating AI systems for risk.

• AI for Lawyers

  Legal implications, risk exposure, and opportunities for legal professionals. Contract analysis, due diligence, regulatory research, and what lawyers need to know about AI liability.

• AI for HR

  Talent strategy, workforce planning, and employee experience in an AI organization. How HR leaders should think about reskilling, hiring, and building a culture that embraces AI.

• AI for Sales

  How AI augments the sales process from prospecting to close. Pipeline management, forecast accuracy, personalization at scale, and where AI makes salespeople more effective, not replaceable.

• AI for Executive Search Professionals

  How AI is reshaping executive search, from candidate research and LinkedIn mapping to client intelligence and search strategy. Covers the six core use cases and two emerging service lines that separate AI-enabled firms from the field.

Financial Advisor Edition

• AI for Practice Management & Operations

  How AI gives advisors more time with clients. Covers meeting prep, post-meeting summaries, email personalization at scale, and portfolio review generation, attacking the overhead that keeps advisors from the work only they can do.

• AI for Prospecting & Business Development

  How to research prospects in minutes instead of hours, personalize outreach at scale with compliance embedded in the prompt, mine your existing book for referral opportunities, and manage a pipeline that doesn't rely on memory.

• AI & Financial Planning Workflows

  Using AI to surface planning gaps, optimize Social Security and retirement income sequencing, flag estate planning triggers, and run tax planning conversations, faster. AI generates the strategic framework; your planning software runs the numbers; you make the recommendation.

• AI for Investing

  How to use AI as a research analyst, risk diagnostic, and client communication engine, without crossing the line into portfolio management. Covers research synthesis, manager due diligence, earnings transcript analysis, portfolio risk diagnostics, and market volatility communication.

• Making the Most of Your Firm's AI Infrastructure

  How to get dramatically more out of every platform your firm already pays for using the Export → Upload → Prompt workflow. Covers CRM optimization, portfolio analytics, financial planning software, firm research, and navigating compliance restrictions.

• AI for Practice Analytics & Business Optimization

  How to use AI to understand your practice as a business. Covers book segmentation, revenue and profitability analysis, attrition risk scoring, growth pattern analysis, and capacity planning, all from a single quarterly spreadsheet.

• The AI-Native Advisory Practice

  What the AI-native practice looks like, what's changing in the profession, and what's not. A 90-day implementation roadmap, prompt library structure, team adoption framework, and the closing argument for why the best advisors will use AI to become more human, not less.