Examples before abstractions.
Participants work through decisions, prompts, risks, and workflows that resemble the situations they face.
AI Education · role-based capability
Not one AI course.
The right one for each room.
Leaders need judgment. Technical teams need architecture and engineering context. People teams need to lead change. Everyone else needs practical skill in the work they already do. Our program teaches each audience what it can actually use.
Shared language.
Different depth.
The organization needs enough common understanding to move together, but not identical content. Every track is tuned to the decisions and work of its audience.
What learning should change
Better questions, safer choices, and more useful work the next morning.
Confidence without credulity.
We teach what AI is good at, where it fails, how to verify output, and when human judgment must stay in control.
Education links to the wider AI pathway.
Training can stand alone or become part of adoption, governance, engineering enablement, and transformation work.
Choose one audience.
Or align the whole system.
Every course is a focused half-day. Tracks can run independently, in sequence, or as a coordinated organization-wide program.
TRACK 01
AI for LeadersThe strategic knowledge to steer AI decisions.
2 courses · half-day each. Designed for the people who make, enable, or live with the decisions this track covers.
Understanding AI: From Hype to Reality
For C-level, Directors, Business Unit Leads
Establish shared vocabulary for all AI work. Demystify AI and separate signal from noise.
- What AI actually is (and isn't) - moving past "it's magic"
- The AI landscape: LLMs, agents, RAG - what the terms mean
- How AI learns, what it can and cannot do
- Realistic expectations: success stories vs. failures
Participants leave with: Shared vocabulary for AI discussions · Ability to spot unrealistic AI claims
Enabling AI Value Creation
For C-level, Directors, Department Heads, IT Leaders
The "now what do we do about it" course: equip leaders to assess, prioritize, and govern AI initiatives with confidence.
- EU AI Act: what it means, board-level timeline, risk categories
- Organizational and technical prerequisites for a successful path towards AI value creation
- People and change: workforce impact, adoption resistance
- Assessing AI opportunities: value, risk, and readiness; Buy vs Build; On-prem vs Cloud vs Hybrid approach
Participants leave with: Understanding of EU AI Act implications and compliance requirements · Framework for workforce impact and change management
TRACK 02
AI for People & CultureA clear understanding of what leading AI adoption really takes.
1 course · half-day. Designed for the people who make, enable, or live with the decisions this track covers.
Leading AI Adoption and Change
For HR, L&D, Change Managers, Transformation Owners
Build a shared understanding of what leading AI adoption actually involves.
- Why AI adoption is a change program, not a tool rollout
- Where fear actually lives: job security, competence, overwhelm
- The manager's pivotal role and why adoption stalls
- HR's terrain: policies, reskilling, role redesign, EU AI Act
Participants leave with: A shared language for AI adoption across HR, L&D, and leadership · The right questions to ask before launching anything
TRACK 03
AI for Technical TeamsThe working knowledge to make the right platform and development calls.
2 courses · half-day each. Designed for the people who make, enable, or live with the decisions this track covers.
AI Platform Fundamentals
For CIO, CTO, CISO, Enterprise Architects, IT Managers
Understand AI platform decisions without deep technical implementation.
- What "AI platform" actually means today
- RAG, agents, LLMOps: what the terms mean for your strategy
- Cloud vs. on-prem vs. hybrid: trade-offs for AI
- Data sovereignty: where does your data go?
Participants leave with: Understanding of AI platform architecture options · Key questions to ask your technical team
AI in Software Development
For Developers, QA, Architects, DevOps
Understand AI's evolving role in your profession - the landscape, not the implementation.
- The AI development landscape: what's available, what works
- AI-assisted coding: where it helps, where it misleads
- Productivity vs. quality: understanding the trade-offs
- Security awareness: prompt injection, data leakage risks
- What to keep doing yourself vs. delegating to AI
Participants leave with: Understanding of AI tools in the development ecosystem · Awareness of risks and limitations (security, quality)
TRACK 04
AI for Everyday WorkHands-on skills for using AI well in the work you already do.
1 course · half-day. Designed for the people who make, enable, or live with the decisions this track covers.
Working With AI
For Legal, Procurement, Finance, HR, Operations, Marketing and more
Build actual AI skills.
- Prompting as a real skill: what makes a good prompt vs. a bad one
- Delegating tasks: structuring requests, providing context, constraints
- Verifying output: catching plausible-wrong answers, knowing when to distrust
- Building personal workflows: small, repeatable AI-assisted processes
Participants leave with: Practical prompting skills for immediate use · Ability to evaluate AI output critically
A program, not
a calendar of lectures.
We shape the sequence, examples, language, and audience mix around the adoption challenge you are trying to solve.
Focused half-day sessions.
Enough time for concepts, discussion, and hands-on work without removing teams from delivery for days.
Croatian, English, or German.
Run a common program across regional teams without forcing one language or losing discussion quality.
At CROZ or with your teams.
Use CROZ offices for a focused learning environment or bring the program into your organization.
Your examples, policies, and tools.
We can adapt exercises and discussion to the organization’s sector, technology choices, and current AI initiatives.
Training is often
the first move, not the last.
Each track can connect directly to a practical next step in the wider CROZ AI offering.
Translate shared understanding into priorities, governance, and a roadmap.
Move from awareness to role-based enablement and managed change.
Turn platform knowledge into engineering decisions and production experiments.
Identify repeatable, safe AI-assisted practices inside real business work.
Who needs to learn what?
Tell us the audiences, current experience, business context, and what should change after the program. We will shape the track mix, examples, format, and sequence.
Design your education program