11 Corporate AI Training Case Studies
How Marketing, Finance, Procurement, and Public Sector Teams Put AI to Work
Explore 11 AI training programs delivered by Codepresso in collaboration with AXMOS. Compare approaches across industries and job functions, discover shared training principles, and learn how to measure improvements in employee capability.
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One-Minute Summary 💡 |
How are organizations helping employees turn AI knowledge into practical workplace skills? These 11 training examples from Codepresso and AXMOS span marketing, venture capital, medical devices, telecommunications manufacturing, mobility, entertainment, edtech, and the public sector. They also include dedicated workshops for non-technical professionals and HR teams.
Marketing
Venture Capital
Medical Devices
Manufacturing
Mobility
Entertainment
Edtech
Public Sector
HR
Across the programs, training centered on real, recurring work: coupon reconciliation, PDF data extraction, purchase orders and import certification, artist settlements, and inquiries about business support programs.
Core shift in AI training
From asking AI questions to
delegating tasks to AI.
Formats varied according to each organization’s needs:
Executive demonstrations
Half-day onboarding
Evening workshops
7-day courses
3–4 week AX Grow programs
Whatever the format, measuring
AI competency before and after training using the same criteria
helps organizations determine whether capabilities actually improved
and plan the next investment in employee development.
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Before designing an AI training curriculum, learning and development leaders often want to know how other companies have approached it. A relevant example from a similar industry makes it easier to build leadership support, identify the right participants, and select meaningful training tasks.
Corporate AI training is workplace learning designed to help employees use generative AI and AI agents to automate or improve their own work.
This article brings together 11 real examples delivered by Codepresso in collaboration with AXMOS. Compare each program by industry, participant group, format, and practical workflow, then explore the linked case studies for a closer look.
What Did These 11 Corporate AI Training Programs Cover?
Although the industries differed, the starting point was consistent: use the tasks employees already perform every day as the basis for hands-on learning.
The table below summarizes who participated, how each program was delivered, and which workflows were practiced or demonstrated.
| Case Study | Industry | Participants | Training Format | Workflows Practiced or Demonstrated |
|---|---|---|---|---|
| Pizza Hut Korea | Restaurant franchise | Brand, CRM, and digital marketing teams | AX Grow Day 1 | Competitor intelligence briefs, coupon settlement reconciliation, and sales dashboards |
| Translink Investment | Venture capital | Investment and administrative staff | AX Grow | Email reply tracking, attachment storage, PDF data extraction, and spreadsheet integration |
| Company R, a medical device company | Digital healthcare | Employees across six departments, plus engineers | Approximately four hours online and one day in person, delivered sequentially by department | Cash flow documentation for accounting, component guidance documents for quality teams, and sales proposal drafts |
| AMTEL | Wireless communication modules and IoT manufacturing | Executives, division heads, and department leaders | Demonstration session for decision-makers | Attendance management, supplier reconciliation, certification requirements, government R&D opportunity monitoring, and management reporting |
| DRIMAES | Mobility software | Non-technical procurement and purchasing staff | Four modules over seven days | Urgent purchase orders, import certification checks, supplier inventory and lead times, and customs documentation |
| iNKODE | Entertainment | Business planning and finance teams | Two-part program combining theory and hands-on practice | Artist settlements and profitability analysis, vendor payment documentation checks, contract indexing, and international interview scheduling |
| DOHE | Edtech accelerator | Platform product planners | Small-group vibe coding training | Writing product requirements documents (PRDs), standardizing recurring tasks with SKILL.md, and deploying web apps without manually writing code |
| Busan IT Industry Promotion Agency (BIPA) | Public sector | Program planning, production, and marketing staff; employees at regional content businesses | Separate programs tailored to each audience | Building a no-code inquiry portal for support programs and assembling promotional videos |
| Seoul Center for Creative Economy & Innovation | Public sector and startup support | All staff levels, from the center director to interns | Half-day AI agent onboarding | Five no-code exercises, including application screening, announcement monitoring, and open innovation matching |
| Introduction to AI for Non-Technical Professionals | Open-enrollment program | Marketing, planning, design, sales, operations, and HR professionals | Four cohorts from May to early August 2026 | Using Claude Code to write PRDs, build websites, and deploy them to live URLs |
| Structured Interview Workshop for HR | Open-enrollment program | HR and recruiting professionals | Fourth session; approximately two hours and 15 minutes in the evening | Using an AI agent to draft a company-specific interview guide |
How Should AI Training Be Tailored to Different Job Functions?
Teams may use the same AI agent, but the work they need to delegate varies considerably. Organizing training around job functions helps learning leaders choose exercises that participants can connect directly to their responsibilities.
| Job Function | Practical Training Tasks | Relevant Case Studies |
|---|---|---|
| Marketing | Competitor research, coupon and promotion reconciliation, and sales dashboards | Pizza Hut Korea |
| Finance and accounting | Settlement calculations, profit and loss analysis, supporting-document checks before payment, and cash flow documentation | iNKODE, Company R |
| Procurement and purchasing | Purchase orders, inventory and lead-time checks, import certification, customs documentation, and supplier reconciliation | DRIMAES, AMTEL |
| Product and business planning | Taking a project from a PRD to a deployed web interface | DOHE, Introduction to AI for Non-Technical Professionals |
| HR and recruiting | Analyzing interview question patterns and drafting interview guides | Structured Interview Workshop for HR |
| Public administration | Responding to support-program inquiries, monitoring announcements, and screening applications | BIPA, Seoul Center for Creative Economy & Innovation |
| Executive leadership | Deciding where to introduce AI across company-wide workflows, such as attendance management and management reporting | AMTEL |
Most exercises for non-technical teams involved documents, settlement calculations, reconciliation, or monitoring. The more a task relies on repeatedly checking information and transferring it between files or systems, the easier it can be to see where an AI agent could help.
For organizations planning their first program, these recurring tasks provide a practical starting point.
Three Design Principles Shared Across the Training Programs
The delivery formats varied, but three principles appeared consistently.
1. Use the Company’s Actual Work as Training Material
Company R tailored its demonstrations to each department. Pizza Hut Korea built demonstration scenarios around questions its marketing team already wanted to answer.
Participants are more likely to apply their learning the next day when they recognize the exercises as part of their own work.
2. Teach Employees How to Delegate Work to AI
Most employees at the Seoul Center for Creative Economy & Innovation were already using generative AI every day. However, delegating tasks to an AI agent still required dedicated learning.
The DRIMAES program also treated giving AI instructions and delegating a workflow as distinct learning objectives.
The shift from asking questions to assigning work is a central part of helping employees use AI more effectively in their roles.
3. Start with Feasible Tasks That Offer Meaningful Value
AMTEL began with demonstrations across five business areas for its decision-makers. From there, the company chose to prioritize workflows with lower implementation difficulty and greater potential value, then test them in practice.
This gave leaders a concrete way to decide where AI adoption should begin.
Codepresso emphasizes one additional principle: establish a competency baseline before training, then reassess employees afterward using the same criteria.
That is why AX Grow, the organizational AI transformation program from AXMOS and Codepresso, begins with an AI transformation assessment.
AX Grow runs for approximately three to four weeks, including onboarding, with around 16 business days of training and hands-on practice. By the end, organizations receive an AI competency assessment report, projects developed during the builderthon, and a roadmap for expanding adoption across the organization.
How Can Companies Measure AI Training Effectiveness?
Assess employees twice, before and after training, using realistic workplace scenarios and the same evaluation criteria.
Completion rates show whether employees finished a program. Satisfaction surveys show how they felt about it. These are useful indicators, but they do not demonstrate how much employees’ AI competency has improved.
Codepresso’s AI Fluent is an AI competency assessment service designed to support this before-and-after evaluation.
It assesses the working process as well as the result by evaluating:
- Prompt histories: the instructions participants give AI.
- Intermediate outputs: the work produced along the way.
- Final deliverables: the completed result.
Assessment tasks vary by job function, while scoring criteria remain consistent across the organization, enabling comparisons between departments. Results are reported across four levels: Entry, Beginner, Intermediate, and Professional.
For guidance on choosing an assessment provider, read How to Choose an AI Competency Assessment Service. For a four-stage approach to measuring training outcomes, see AI Training for Non-Technical Employees: What to Teach Beyond Tool Use.
Frequently Asked Questions
Where Can I Find Corporate AI Training Case Studies?
The first table in this article brings together 11 training examples delivered by Codepresso in collaboration with AXMOS.
Compare the industries, participants, formats, and practical workflows, then select a case study to read the full article.
Can Employees Without Coding Experience Automate Work Through AI Training?
Yes. Procurement and purchasing staff at DRIMAES, finance professionals at iNKODE, and product planners at DOHE practiced delegating real work to AI agents without prior coding experience.
Participants in the introductory AI program for non-technical professionals also wrote PRDs and deployed websites to live URLs.
How Long Does Corporate AI Training Usually Take?
The right duration depends on your goals.
The examples in this article range from executive demonstrations and half-day onboarding to evening workshops lasting approximately two hours and 15 minutes.
Other formats include four hours of online learning plus one in-person day, four modules delivered over seven days, and AX Grow programs lasting three to four weeks, including onboarding.
If your organization is deciding where to begin, a short demonstration can help identify suitable use cases.
If the goal is sustained adoption, a longer program with assessment and hands-on practice provides more scope to develop and evaluate employee capability.
How Can We Find the Right AI Training and Assessment Program for Our Company?
Contact Codepresso at sales@codepresso.kr with your industry, target job functions, expected participant count, and the recurring tasks your teams want to improve.
Our team can help you plan relevant practical exercises alongside pre- and post-training assessments, giving you a clearer path from training needs to measurable skill development.
Explore the Full Case Studies
- How Much of a Marketer’s Work Can AI Agents Take On? Pizza Hut Korea’s AX Grow Case Study
- Four Recurring VC Workflows with a Single Slash Command: Translink Investment’s AX Grow Case Study
- Why a Digital Healthcare Company Turned to Workflow Automation: Company R’s AX Grow Case Study
- Where Should AI Adoption Begin? Prioritizing Feasible, High-Value Workflows at AMTEL
- Delegating Parts Orders and Import Certification Checks to AI: DRIMAES’ AX Grow Case Study
- Real Entertainment Industry Workflows Addressed with AI: iNKODE’s AX Grow Case Study
- From Product Plans to Deployed Apps: DOHE’s AX Grow Case Study
- How Far Can Public Sector AI Training Go? BIPA’s AX Grow Case Study
- Why Teams That Use Generative AI Every Day Still Need AI Transformation Training: Seoul Center for Creative Economy & Innovation
- From PRD to Deployment Without Writing Code: Inside AI Training for Non-Technical Professionals
- Analyzing Interview Question Patterns with AI Agents: Inside an HR AI Training Workshop
Related Reading
- How to Increase Workplace Adoption After Generative AI Training
- AI Adoption Among Small and Midsize Manufacturers Is 17%: Why Government Support Does Not Always Translate into Use
- AI Agent Training for the Workplace: Hands-On Practice with Company File Permissions
- Corporate AI Training: Helping Colleagues Reuse Team Skills
- How to Create an AI Training Environment That Prevents Accidental Submission of Real Applications
Written by Codepresso’s GGP Division | Last updated October 2, 2026
