The Learning Lab experience is supported by both asynchronous and synchronous components. Each part includes a set of resources, an asynchronous discussion, and an interactive live session, all of which culminate in the development of a project to apply learning to local and specific contexts in support of the learning objectives.
Schedule
Part 1: Strategic Synergy: Aligning AI and Data Management in Higher Education
February 3, 2026 | 11:00 a.m.–12:30 p.m.ET
In this foundational session, Dr. Bas van Gils will start us out by presenting a high-level overview of relevant concepts to create a level playing field. These include the notion of AI, data strategy, data management strategy, and data governance. The DAMA DMBOK® will be used as the industry reference model for this discussion. An expanded version of the Business Model Canvas is introduced as the backdrop for practical assignments that help participants to create a strategic AI framework for their own organizations. The framework will be used and expanded in the following sessions as well. Becky Frieden will illustrate the learned concepts with practical use cases of AI in higher ed.
Learning Outcomes:
- Understand (the history) of AI – Explain that AI is neither new nor a silver bullet. There are many benefits to using AI, but also important challenges and ethical considerations.
- Explore the DMBOK framework and its relevance to AI integration – Identify the key functional areas in the DAMA DMBOK data strategy and data management strategy in order to position AI integration in higher education.
- Start creating a strategic framework for AI in your own organization – Learn the fundamentals of (an expanded version of) the Business Model Canvas to create a strategic framework for AI in your organization.
- Build a shared strategic vocabulary for institutional alignment – Articulate a common language and vision that supports the alignment of AI and data strategies to promote student success, operational efficiency, and institutional agility.
Part 2: Explore Strategic Options for AI Use
February 6, 2026 | 11:00 a.m.–12:30 p.m.ET
Building on the foundational concepts introduced in the first session, this hands-on workshop guides participants through the process of exploring strategic options for AIuse. Becky Frieden will kick off the session with practical examples to show how AI use can help organizations. The examples will show that AI can be used to capitalize on strengths and seize strategic opportunities or to deal with the threats associated with weaknesses in the organization. Dr. van Gils will show the relation between these examples and good data management practices. We will also show that sometimes it is more important to focus on intelligence augmentation than on artificial intelligence per se.
Learning Outcomes:
- Explore use cases with the SWOT methodology – The SWOT methodology is used to analyze the expanded BMC. This is the basis for identifying use cases for artificial intelligence (AI) or for intelligence augmentation (IA).
- Map use cases on the expanded BMC – The expanded Business Model Canvas is the core of the framework. Use cases are mapped on this framework to see where value can be added.
- Rank use cases with feasibility and value – Explore how a portfolio of use cases can be created, as there are always more use cases than funding/ability to implement. This analysis also includes an assessment of required data management capabilities.
Part 3: Data Management Enablers & Governance
February 10, 2026 | 11:00 a.m.–12:30 p.m.ET
Effective AI implementation does not happen automatically. Data management enablers also take work. Strong guardrails in the form of policies, controls, and governance mechanisms are required to create and facilitate effective AI enablement. The governance structure in turn should be embedded in the overall governance structure of the organization. This session is also about data management and data management governance. Dr. van Gils will present an overview of different governance mechanisms and strategies that can be used based on COBIT® and DAMA DMBOK. Becky Frieden will illustrate their use with real-world examples of governance structures. The outcome of the analysis is once again plotted on the expanded BMC.
Learning Outcomes:
- Explore data management enablers – Present a high-level overview of data management capabilities as enablers for AI integration.
- Explore governance structures and mechanisms – Present the latest insights in governance and application of these theories to AI/data management governance.
- Design governance framework – Design the AI/data management governance framework for your organization.
Part 4: Ethics and Roadmap
February 17, 2026 | 11:00 a.m.–12:30 p.m.ET
As AI becomes more deeply embedded in higher education, institutional leaders must ensure its use aligns with a clear roadmap, ethical standards, institutional values, and responsible data stewardship. In this session, participants will explore both the development of an effective roadmap and the ethical dimensions of AI. These include issues of data consent, privacy, algorithmic bias, and transparency. Through case studies and collaborative exercises, participants will develop draft guidelines for responsible AI use that reflect their institution’s mission and values. The session emphasizes the role of leadership in fostering a culture of accountability, inclusivity, and trust in AI-driven practices. Additionally, a two-speed development approach (tight/loose control) is introduced. This is the basis for designing an effective roadmap.
Learning Outcomes:
- Identify key ethical considerations in AI use – Examine issues such as data consent, privacy, algorithmic bias, and transparency in the context of AI applications in higher education.
- Develop guidelines for responsible AI implementation – Create draft institutional guidelines that promote ethical AI use aligned with data governance standards and institutional values.
- Explore gap analysis and roadmapping – A roadmap is designed to include (a) explorative proof of concepts and (b) well-defined projects. It is based on a careful gap analysis to move to the desired future in an effective manner. The roadmap should include (a) implementation of use cases, (b) required data management capabilities, (c) governance structures, and (d) policies to address ethical concerns.
Lab Project/Assignments
AI-Integrated Data Strategy Canvas: A 4-Part Activity Series
Activity 1: Fill in the expanded BMC for your own organization.
Goal: Establish an overall framework for an AI strategy and roadmap.
Instructions: Participants map out the overall business model for their institutions. Based on institutional strategy and priorities, brainstorm 2–3 goals for data/AI use and data management.
Canvas Section: Mission/Vision/Strategy Business Priorities Data Priorities Expanded BMC
Activity 2: SWOT and strategic options for AI use
Goal: Evaluate institutional readiness to derive value from data using AI.
Instructions: Using a SWOT framework, participants assess:
- Strengths (e.g., strong data governance)
- Weaknesses (e.g., siloed data systems)
- Opportunities (e.g., predictive analytics for retention)
- Threats (e.g., bias in algorithms, lack of consent protocols)
- Create options for AI/data use and rank them based on value + feasibility. Canvas Section: SWOT Options (+ priorities)
Activity 3: Data management enablers and data management governance
Goal: Get a good understanding of the required data management enablers and governance structures needed for an AI roadmap that enables data/AI value.
Instructions: List the data management enablers (based on the functional areas in DAMA DMBOK) that enable AI integration. Design a data management governance framework by listing relevant controls and governance structures (based on DAMA DMBOK- and COBIT-).
Canvas Section: Data Management Enablers Data Governance
Activity 4: Ethics and Roadmap
Goal: Define ethical guidelines to safeguard human values in AI integration and design a roadmap for achieving AI/data priorities.
Instructions: List the ethical concerns and draft key ethical principles for addressing them. Then design a roadmap that ties together (1) use cases, (2) data management enablers, (3) data management governance, and (4) the human factor.
Canvas Section: Ethical Principles → Roadmap