E X E C U T I V E E D U C A T I O N
Data Leadership for AI-Enabled Companies
A private executive education program that helps leadership teams build the data capabilities, decisions, and operating practices required for reliable AI and measurable business value.
Format: Private, in-person program
Duration: Two 8-hour days
Pre-requisites: None
THE ELEPHANT IN YOUR AI
Data has always been important to organizational performance, but AI is changing how data must be selected, structured, integrated, governed, and used. Many organizations are pursuing AI opportunities while relying on data strategies, architectures, and operating practices designed primarily for historical reporting and analytics.
Data initiatives are often disconnected from specific business questions, workflows, and measurable value-creation priorities. And leaders may not recognize which capabilities, decisions, and investments are most critical.
Fragmented systems, inconsistent definitions, limited reuse, and unclear ownership can increase costs while reducing the speed, reliability, and scalability of AI solutions.
Technical, business, risk, and operating decisions are frequently addressed separately, even though their interdependencies determine whether data can support sustainable AI-enabled operations.
Traditional data courses commonly emphasize technologies or functional practices without providing leadership teams with an integrated approach to enterprise planning and action. Good thing we’re not traditional.
DATA LEADERSHIP FOR SMARTER OPERATIONS
Our Data Leadership for AI-Enabled Companies program is a two-day private executive education program that helps leadership teams develop the strategic direction and operating priorities needed to build data capabilities for reliable, scalable, and value-producing AI. Using proven practices and your own organizational priorities, participants work through planning domains spanning data strategy, leadership, use-case framing, sourcing, modeling, utilization, quality, control, and sustainable operations.
Integrated: Company leaders connect business priorities, data requirements, architecture, governance, operating practices, and value creation within an integrated planning model designed for supporting AI, insights, and automation opportunities.
Modernized: Participants examine how modern AI changes traditional assumptions about data access, structure, semantics, reuse, quality, infrastructure, and operational support.
Applied: Executives use organizational priorities and selected use cases to assess readiness, identify reusable data capabilities, characterize risks and requirements, and clarify ownership topics.
Executive Focused: Leaders are able to concentrate on the decisions, investments, trade-offs, and organizational capabilities without turning the program into a technical workshop.
Actionable: Leadership teams leave the program with a preliminary Enterprise Data Leadership Plan including priority capabilities, accountable owners, unresolved decisions, and immediate action plans.
WHO SHOULD ATTEND
Cross-functional leadership teams responsible for major functions, products, or services. Typical job titles include:
CEO, business-unit president, and executive team leaders such as COO, CFO, CHRO, CSO, CIO, CTO
Chief Data, Analytics, or AI Officers
CISO or senior security leader
General Counsel, privacy, compliance, or risk leadership
Product and operational business owners responsible for selected use cases
Up to 7 leaders can attend. A minimum of 3 is required.
WHAT YOU WILL LEARN
DAY 1
What is a data strategy and why is it critical to AI success and business performance?
How do modernized, AI-oriented data approaches differ from historical data management practices?
How should leaders prioritize and manage the economic tradeoffs associated with data-related investments?
How do choices about data impact the cost, speed, quality, reliability, and scalability of AI solutions?
What are the most common gaps and risks that emerge in enterprise data strategies?
DAY 2
How do organizations curate data for AI operations?
How do leaders drive greater data quality, consistency, timeliness, and trust that AI and analytical solutions can leverage?
How do leaders proactively address data privacy, regulatory compliance, and intellectual property protections?
What functions, roles, and processes are needed to support modern data operations?
What are the immediate next steps your organization can pursue to advance data and AI readiness?
WHY THIS SESSION IS DIFFERENT
Many data sessions focus on tools, trends, or technical concepts. This session focuses on the data leadership and teamwork required to create trusted insights and AI solutions. That means asking harder leadership questions:
How does AI enablement and adoption change the way leaders need to approach enterprise data?
How do we reduce repeated, one-off data work by developing reusable, high-quality, semantically consistent data assets?
How do leadership teams design and implement new governance models and business processes that support stronger data assets?
As organizational adoption of AI grows, what is the relationship between data, AI, operating costs, and value creation?
Where are the data-related risks in AI and how can they be proactively mitigated?
How can our leadership team gain more informed perspectives, confidence, and coordination in data-related plans and decisions?
Takeaways
Teams will learn and apply proven disciplines for developing trusted, scalable, AI-ready data capabilities while collaboratively creating a preliminary Enterprise Data Leadership Plan that includes:
Company-specific data strategy direction, goals, and value creation targets
A preliminary assessment of current data readiness, capabilities, constraints, and priority gaps
Initial design and scalability requirements for reusable data assets aligned to priority AI use cases
Design, curation, quality, infrastructure, utilization, protection, and scalability requirements and considerations
Leadership plans guiding ongoing decisions, ownership, competencies, and operating requirements
A concrete action plan of next steps
ABOUT THE INSTRUCTOR
Jason Burke is an industry executive, advisor, educator, and author focused on AI, analytics, data strategy, innovation, and enterprise growth.
He has served as Chief AI and Strategy Officer for a life sciences and healthcare consulting firm, founding Chief Analytics Officer for UNC Health, and a senior strategy and product leader for health and life sciences at SAS. He is the author of Health Analytics: Gaining the Insights to Transform Healthcare and has advised organizations across healthcare, life sciences, private equity, venture capital, and academia.
His executive education program at the Duke Fuqua School of Business has been cited by Forbes as one of ten "courses that should be on every executive’s radar."
