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June 12, 2026 · Last updated on September 11, 2026

OpenAI Academy courses: Champion deployment guide

# Champions

# Deployment & Adoption

How to successfully deploy courses inside of your organization

What are OpenAI Academy courses?

OpenAI Academy courses help people build practical AI skills and apply them to their work. At OpenAI, we view learning as part of deployment: people need the skills to use the technology, build with it, and lead adoption.

The expanded portfolio combines a shared Foundations pathway with focused learning for developers, leaders, educators, and college students. Organizations can build common AI fluency while helping people develop the skills their roles require.

For Champions and learning teams, the courses provide a foundation for onboarding, technical training, leadership development, and AI adoption programs. Use this guide to choose relevant learning, introduce it across your organization, and help people put it into practice.

Explore OpenAI Academy courses

How courses support Champion initiatives

Build shared skills: Give people a practical foundation for using AI and a way to continue learning as their work develops.

Match learning to responsibilities: Add focused learning for people building AI-powered products, leading adoption, or using AI in teaching and learning.

Support leadership priorities: Connect course recommendations to the capabilities your organization needs to achieve its AI goals.

Focus Champion support: Use courses for shared learning so your sessions can address local workflows, policies, questions, and adoption barriers.

Plan the next step: Combine learner feedback and examples of application with available participation and usage data to decide where more support is needed.

How to use this guide

Use the five steps below to plan an organization-wide course rollout:

Activate — make learning easy to access and recommend relevant courses.

Engage sponsors — connect learning to organizational priorities and secure leadership and manager support.

Launch — introduce the portfolio broadly, with specific recommendations for each audience.

Reinforce and measure — support practice, follow up with learners, and assess the signals available.

Share — recognize progress and exchange examples and lessons within your organization and with other Champions.

1. Activate

Make the portfolio available broadly, then help people choose the learning most relevant to their experience and responsibilities.

Use Foundations to build shared skills across your workforce. Add Builder courses for developers and technical teams, AI Leadership for people guiding strategy and adoption, and education courses where relevant.

Broad access does not mean everyone needs the same curriculum. You can focus initial outreach and supported practice on teams closest to your organization’s AI priorities while keeping the courses available to others.

Prepare access and support

Coordinate with your learning, communications, IT, and change management partners to:

Confirm how learners will access the courses.

Add direct course links to your internal learning hub and AI resource pages.

Keep enrollment simple and remove avoidable internal approval steps.

Identify a channel or contact for questions and support.

Agree a launch date, learning window, and time people should set aside for their recommended course.

Help people choose where to begin

The Foundations pathway includes three courses. The additional offerings provide focused learning for different roles.

Course or offering	Recommended audience	What learners practice
AI Foundations	People new to AI or strengthening their core skills	Giving clear instructions, supplying context, checking responses, and using AI responsibly.
Applied AI Foundations	People ready to apply AI to recurring work	Breaking work into steps and developing repeatable workflows with review points.
Agents and Workflows	People ready to direct more structured work with agents	Defining outputs and boundaries, providing context, reviewing results, and improving workflows.
Builder courses	Developers and technical teams using Codex or building with the OpenAI API	Using Codex in software development and designing, evaluating, deploying, and operating AI-powered products.
AI Leadership	People responsible for AI strategy, adoption, change, and workforce transformation	Connecting an initiative to business priorities, assessing opportunities, establishing ownership and governance, and developing a roadmap and initial AI strategy draft.
AI for Educators	K–12 and higher-education educators	Using permitted teaching materials to plan lessons, develop activities and assessments, and prepare communications while retaining educator judgment.
AI for College Students	College students	Using AI for study planning, assignments, group projects, and career preparation while checking outputs and taking responsibility for the final work.

Recommend a starting point for each audience, with a direct course link and a practical reason to take it. Foundations can provide a common baseline; learners with relevant experience can begin with the learning that fits their needs.

2. Engage sponsors

Choose an executive sponsor who can explain how AI learning supports the organization’s strategy and make time for learning a visible priority. Managers should help employees understand what is relevant to their team and where to apply it.

Connect the rollout to a clear goal

Examples include preparing new employees to use AI, improving everyday work, developing technical capability, or supporting an existing AI adoption or leadership program.

Agree which learning to recommend for each audience and what work it should support—for example, employee onboarding, an AI-powered product, or an organization-wide adoption initiative.

Ask your sponsor to

Introduce or endorse the courses and explain their connection to the organization’s AI priorities.

Encourage managers to protect learning time and discuss what employees apply.

Recognize progress and share an example of AI use from their own work.

Equip sponsors and managers

Provide a short course overview, recommendations by audience, launch timing, and verified links. Include a clear employee action and two or three examples connecting the learning to business priorities.

Give managers brief talking points they can adapt for team meetings, along with a question such as: “What could you apply from the course to work we are doing now?”

Explain the value

Built with OpenAI expertise: The courses draw on the teams developing OpenAI’s technology and guidance.

Practice with real work: Learners apply concepts to tasks and initiatives relevant to them.

Learning that develops with the technology: Courses are updated as OpenAI models, products, and guidance change.

Assessments and recognition: Every course includes an assessment to help learners check their understanding. Learners who pass the assessment will earn the associated badge, which they can share on social platforms.

3. Launch

Introduce the portfolio across your organization, then follow up with recommendations for specific teams. Each message should explain which course is relevant, why it matters to that audience, and how learners can apply it to work already underway.

Plan a visible introduction and follow-up communications across the channels employees already use.

Prepare a coordinated launch package

A sponsor announcement and an organization-wide email or internal post.

A learning-hub entry with course links and recommendations by audience.

Manager talking points, an internal AI community post, and a newsletter or intranet feature.

A suggested learning window or calendar reminder, plus clear access and support information.

Make the next action clear

Every audience should know why the learning matters, which course to begin with, how to enroll, and when to complete it. Include the expected time commitment, where to ask questions, where to share examples or badges, and what support or learning comes next.

Internal email template

Subject: Build AI skills for [team’s work]

We’re introducing OpenAI Academy courses to support [organization’s AI priority] and help you apply AI to your work.

For [team/audience], start with [course and link] to [practical outcome]. Please set aside [learning time] during [completion window].

Bring a task you’re permitted to use, follow our AI and data-handling policies, and try one approach from the course.

Find other recommended courses at [internal learning page]. Share questions, examples, and badges you earn in [internal channel].

4. Reinforce and measure

Keep the initial invitation broad. Use follow-up messages and support to address the needs of specific audiences.

Remind learners about their recommended course and agreed learning window.

Use team discussions, office hours, or application sessions to help people practice.

Share useful examples, recognize course completion, and celebrate badges learners earn.

Identify access, confidence, or workload barriers and offer appropriate support.

Recommend further learning as people’s responsibilities and needs develop.

Suggested follow-up cadence

Timing	Action
Launch day	Publish the sponsor announcement and organization-wide invitation, with recommendations by audience.
Week 1	Ask managers to reinforce the invitation; share course recommendations in the internal AI community.
Week 2	Send a reminder featuring a learner example, useful takeaway, or course-completion milestone.
Week 3	Hold an office hour, team discussion, or session where learners apply what they learned.
Week 4	Share participation and application signals, recognize progress, and explain the next learning opportunity.

Help learners apply the courses

Connect follow-up support to work that matters to each role. For example:

A Foundations learner might improve a recurring task and explain where human review is needed.

A developer might apply a Builder course lesson to a software change or an evaluation for an AI-powered product.

An AI Leadership learner might bring their roadmap and initial strategy draft into a discussion with the people responsible for the initiative.

An educator or student might share how they checked and improved an AI-assisted result against their teaching or assignment requirements.

Use these examples to guide discussion and support, not as additional course-completion requirements. Completion shows participation; examples of application help you understand what changed in people’s work.

Use the signals available to you

Agree what you can collect before launch. Depending on your organization, evidence may come from course reporting, workspace usage data, communications channels, or direct feedback from learners and managers.

Signal	What it helps you understand	Possible source
Participation and completion	Whether learners are enrolling and completing their recommended learning	Ask your OpenAI account team about available reporting; use voluntary learner updates where appropriate.
Awareness and reach	Whether each audience received and understood the invitation	Communication reach, link engagement, questions, or manager feedback.
Application	What learners have tried and what changed in their work	Task examples, workflow changes, learner feedback, team discussions, and office hours.
Adoption	Whether AI use is changing alongside the learning effort	Workspace adoption metrics available to your organization, interpreted alongside other changes.
Progression	What learners are ready to do next	More consistent workflows, technical work, leadership plans, or other examples relevant to their responsibilities.

For enterprise reporting, encourage learners to use their work email domain or Sign in with ChatGPT using their enterprise account. An enterprise admin must enable Sign in with ChatGPT for that option to be available.

Contact your OpenAI account team to understand organizational reporting availability and access.

OpenAI Academy courses Help Center article

Use the evidence to decide which audiences need another invitation, which teams need practical support, what is getting in the way, and what learning should follow. Do not treat a change in usage alone as proof that the courses caused it.

5. Share

Make participation and practical application visible. Recognize course completion and badges earned, and share examples of what people have learned or changed in their work.

Useful updates can include:

How many people were reached or completed learning, where that information is available.

Examples of improved tasks, workflows, technical work, or leadership plans.

Reflections from learners, managers, and sponsors.

Recurring questions, barriers, and the support planned in response.

The next stage of your organization’s learning plan.

Share through internal AI channels, newsletters, team meetings, leadership updates, or all-hands sessions. Invite learners who have useful examples to help their peers.

Deployment summary template

Share a summary after 8–12 weeks, or include it in a monthly update:

Organization or audience reached:

Executive sponsor:

Launch channels:

Courses promoted, intended audiences, and the work each recommendation was designed to support:

Participation or completion signals:

Examples of learning applied:

What worked:

What was difficult:

What we will do next:

Share feedback with the OpenAI team about where additional learning or support would help your organization.

Learn from other Champions

Compare approaches with other Champions: which recommendations resonated, how you supported practice, what got in the way, and what you would change next time.

Share your deployment approach in the Champion Community forum