AI Governance and Responsible Adoption: The 4Ds Framework

The problem this course solves

Almost every organization is already "using AI" in some form: employees trying ChatGPT, Copilot or Claude on their own, without shared guidelines for confidential data, intellectual property, customer information or human review.

The result is fragmented adoption:

• Executives who know AI matters but have not defined what it should achieve for the organization.
• Teams using public AI tools without clear rules for responsible use.
• Business areas with plenty of ideas but no method to decide which ones are worth pursuing.
• No shared framework to decide what is delegated to AI, what is always reviewed, and who is accountable for the result.

Traditional tool or prompting training does not solve this: it teaches people to use AI, but it does not produce governance decisions, a prioritized initiative portfolio or an implementation roadmap. This course closes that gap by turning AI governance into a practical, co-created exercise rather than a generic policy document.

Who it is for

No advanced technical AI experience is required — but decision-making authority and a willingness to work on the organization's real context are.

• Executives and leaders with strategic decision authority (Executive Sponsors).
• Managers and area leads who will manage use-case implementation (Senior Management).
• Technical or innovation professionals who will take proofs of concept into practice (Innovators).
• Risk, legal, security or compliance teams involved in defining usage boundaries.

Prerequisites

• Basic experience using a generative AI tool such as ChatGPT or Claude.
• Willingness to work with real organizational information during the workshops.
• Ideally, joint participation of all three levels — executive, management and innovation — so the resulting framework represents the whole organization.

What the organization takes away

The course does not end with concepts. It ends with deliverables the organization can use the next day.

• An organizational AI Mandate: ambition, principles and strategic direction for AI adoption.
• A map of roles and responsibilities following the pyramid approach (Executive Sponsor, Senior Management, Innovators).
• An inventory of use cases evaluated and prioritized with a structured framework.
• A governance framework and a responsible AI use policy of your own, not a generic template.
• Autonomy and approval levels defined by role.
• An adoption roadmap for the short, medium and long term, with at least one prioritized proof of concept ready to execute.

Learning objectives

By the end of the course, participants will be able to:

1. Understand the pyramid approach to organizational AI adoption and the roles of Executive Sponsor, Senior Management and Innovators.
2. Align AI adoption with clear strategic objectives.
3. Identify and prioritize AI use cases using structured evaluation frameworks: strategic value, economic impact, feasibility, data, risk and time to value.
4. Apply the AI Fluency Framework (4Ds) — Delegation, Description, Discernment, Diligence — as the basis for responsible individual and organizational AI use.
5. Design a governance framework and a responsible AI use policy of their own.
6. Build an adoption roadmap with prioritized use cases and a proof of concept ready for implementation.
7. Present a governance proposal and implementation plan at executive level.

Course content

1. Foundations and strategic approach to AI governance

   Introduces the pyramid approach to adoption (Executive Sponsor, Senior Management, Innovators) and positions governance as a strategic rather than technical decision. Participants define the strategic objectives that will guide the rest of the course and build the map of roles and responsibilities for their own organization.

2. Strategic identification and prioritization of use cases

   Builds the ability to identify use cases genuinely aligned with business objectives, using creative thinking and a six-dimension evaluation framework: strategic value, economic impact, feasibility, data availability, risk and governance, and time to value. Closes with a prioritized portfolio and the business case for the first candidate.

3. The 4Ds framework and governance design

   Goes deeper into the 4Ds (Delegation, Description, Discernment, Diligence) and brings them to the organizational level: roles, approvals, autonomy levels and controls. Participants co-create the governance framework and draft their organization's responsible AI use policy, including approved, restricted and prohibited usage scenarios.

4. Implementation: roadmap, proof of concept and action plan

   Closes the course by moving governance and prioritized use cases toward execution. Participants build a short, medium and long-term adoption roadmap, prepare the context for the prioritized proof of concept and present an integrated implementation plan to the group.

Methodology

Continuous mentoring from the instructor throughout all practical workshops.

Duration and format

• Duration: 24 hours.
• Suggested format: 4 sessions of 6 hours.
• Delivery: virtual, by videoconference.
• Tools: ChatGPT and Claude as primary support (free tier as a baseline; Plus/Pro optional).
• Availability: as a closed program for a single organization, or as an open program for groups from different companies.

About the instructor

Jorge Domínguez is a systems engineer with more than 18 years of experience in software development, solution architecture and technology innovation. He holds an MBA from Universität Leipzig, Germany, focused on innovation and technology adoption, and is a graduate of the MIT xPRO executive program in AI product and service design and development (2025). He is certified in Generative AI Fundamentals by Databricks and in Teaching the AI Fluency Framework by Anthropic.

He has led curriculum design and delivery of specialized AI training at Universidad de los Andes, and has trained more than 60 companies and institutions — including Bancolombia, Comfandi and Universidad de los Andes — across the three levels of organizational adoption: C-Level (AI mandate and strategic governance), Senior Management (use-case prioritization) and Innovators (proofs of concept and scaling).

He is a certified instructor of Anthropic's AI Fluency Framework, which he applies at the center of his governance and responsible AI programs.

Next step

If the next step is turning prioritized cases into prototypes, continue with Generative AI Innovation. If you need to enable technical teams, see Practical AI Adoption.