Programs & labs

    Turn AI experiments into adopted ways of working.

    Complete AI adoption programs, diagnostics, and practical labs for teams that need AI to change real tasks, not just generate interest.

    Inquire about this program

    We work with one team, one department, or one cohort at a time. The goal is to move from scattered usage to documented workflows, internal standards, measurable behavior change, and practical outputs people keep using after the program ends.

    Main program

    AI Adoption Sprint

    4 weeks. One team. Real workflows changed.

    In 4 weeks, one team moves from chaotic AI experiments to 3 to 5 documented AI-assisted work practices that are used, measured, and ready to scale.

    This is not a course. It is an adoption sprint.

    "What work no longer makes sense to start manually from scratch?"

    We start with one question: What work no longer makes sense to start manually from scratch? Then we identify the tasks, train the team on their actual work, rebuild selected workflows with AI, document the new way of working, and leave behind examples, rules, templates, and adoption signals.

    Implementation timeline

    01Baseline

    Diagnostic and task mapping

    We start by understanding how the team works now.

    • →Current AI tools and usage level
    • →Repetitive task inventory
    • →Manual vs AI-assisted task comparison
    • →Barriers, confidence, and risk perception
    • →Existing good practices already inside the team
    • →Superuser and champion identification
    02Delivery

    4 live sessions

    Each session moves from awareness to application to reuse.

    • →Session 1: use cases, guardrails, task mapping
    • →Session 2: applied work on real tasks: emails, reports, research, analysis, content, follow-up
    • →Session 3: templates, prompts, reuse patterns, team practice
    • →Session 4: manager recap, adoption plan, next steps
    03Assets

    Concrete deliverables

    The program leaves behind assets the team can keep using.

    • →3 to 5 documented use cases per role or team
    • →Team mini-playbook
    • →Prompt and example library
    • →Before / after adoption report
    • →Safe-use checklist
    • →AI literacy evidence, Article 4 aligned
    • →Recommended next workflows for adoption
    04Continuation

    Adoption support

    If the team wants to keep momentum after the sprint:

    • →Office hours for ongoing AI questions
    • →Manager review sessions
    • →AI champion support
    • →3-month adoption support retainer
    • →Expansion to additional departments
    • →Follow-up measurement
    What the sprint fixes

    The problem is not lack of AI tools. It is lack of adoption structure.

    Most companies already have AI access. But inside teams, usage is uneven:

    • →some people use AI daily, others avoid it
    • →prompts stay personal and undocumented
    • →useful examples do not spread
    • →managers cannot see what changed
    • →AI is used for isolated tasks, not shared workflows
    • →training happens, then behavior fades

    The AI Adoption Sprint turns individual experimentation into team-level practice.

    Example outcome

    What a sprint can produce

    A 12-person team can finish the sprint with:

    • →3 to 5 standardized AI use cases
    • →a shared prompt and example library
    • →documented workflow changes
    • →AI usage growth measured before and after
    • →a mini-playbook for internal reuse
    • →a shortlist of next workflows to adopt
    • →practical AI literacy evidence for governance discussions

    Use this as the model: one team, one sprint, visible behavior change.

    Complete program 02

    AI use-at-work champion builder

    Build internal ownership, not dependency.

    One-off training decays quickly if nobody inside the company owns the next step. The Champion Builder program prepares selected employees to support safe, useful, and repeatable AI adoption inside their teams. These are not technical AI developers. They are practical internal operators who know how to spot use cases, improve prompts, document workflows, and guide colleagues toward better AI use.

    The problem

    Without internal champions, every AI training investment loses force within weeks. People return to old habits. Useful prompts stay hidden. Managers lose visibility. Each department invents its own rules. Internal champions make adoption spread.

    What participants build and do

    • →Complete modules on AI literacy, practical tool use, and responsible use
    • →Build role-based use cases and prompt patterns
    • →Map workflows and identify old work habits
    • →Learn how to check AI output based on risk
    • →Document practical AI use cases for their own department
    • →Complete a final practical project for internal use
    • →Present their use case in an internal showcase

    What the client receives

    • →Trained internal AI champions
    • →Final practical projects and documented use cases
    • →Certificate of completion
    • →Adoption recommendations
    • →Optional internal showcase
    • →Suggested next steps for scaling adoption across teams

    Best for

    Internal AI champions, HR and L&D teams, digital transformation leads, operations managers, department heads, and companies that want AI adoption to continue after the external trainer leaves.

    Inquire about this program →
    Entry point

    AI work standards audit

    Know where adoption is stuck before running another training.

    Before any sprint or training, the company needs to know where it actually stands. The AI Work Standards Audit is a rapid, low-friction diagnostic that maps how AI is currently used, where the strongest opportunities are, and which standards should be introduced first.

    What is checked and delivered

    • 01Intake call and short team survey
    • 02Map of 5 to 10 tasks where AI can change work
    • 03Manual vs AI-assisted comparison for key tasks
    • 04Risk assessment: data, quality, verification, confidentiality
    • 05Superusers and existing good practices identified
    • 063 AI work standards that can be applied immediately
    • 0730-day adoption plan
    • 0860-minute debrief session

    Commercial logic

    Creditable toward the full AI Adoption Sprint if the client starts within 7 days. Designed as a fast decision product, with low procurement friction and a direct path into implementation.

    Inquire about the audit →

    Practical Labs · Applied Modules

    Build something real in the same day.

    Labs are focused implementation sessions. They are best used after a diagnostic, inside a sprint, or as a practical event format when a team needs fast proof. No abstract planning. No long theoretical setup. A real proof of concept, workflow, or prototype is built in the room.

    Applied Module · Lab 01Half-day or full-day

    Rapid AI prototyping lab

    Many companies talk about AI use cases, but do not build anything tangible. They lose time in meetings, slides, and abstract planning without producing a working output anyone can evaluate. This lab turns one idea into a working proof of concept.

    What participants build and do

    • →Internal assistant or form-based tool
    • →AI-assisted workflow
    • →Customer support helper
    • →Lead qualification tool
    • →Content research assistant
    • →Internal process configurator
    • →Simple app prototype built around a real business need

    What the client receives

    • →One working prototype or proof of concept
    • →Use-case definition and prototype documentation
    • →Adoption and risk notes
    • →Next-step recommendation for implementation
    Inquire about this lab →
    Applied Module · Lab 023-4h or full-day

    AI agent management lab

    AI agents are often introduced without clear roles, instructions, boundaries, or evaluation criteria. That creates unreliable output, inconsistent behavior, and low trust. This lab teaches teams how to define, brief, test, and manage AI agents as work roles, not toys.

    What participants build and do

    • →Define an AI agent role
    • →Brief the agent with context and boundaries
    • →Test output quality
    • →Review performance
    • →Decide what the agent can and cannot do
    • →Create one agent brief for immediate use

    What the client receives

    • →Agent brief template
    • →One agent role designed per participant or team
    • →Testing checklist
    • →Governance notes
    • →Safe agent adoption recommendations
    Inquire about this lab →
    Applied Module · Lab 032-3h demo plus practical exercise

    Hermes Agent demo lab

    Most people talk about AI agents abstractly. This lab shows how agent-assisted work can support research, content, follow-up, commercial workflows, and operational routines. The point is to make the difference visible: A chatbot answers. An agent follows a work process.

    What participants build and do

    • →Watch a live demonstration of agent-assisted business workflows
    • →Design one simple agent workflow for their own work
    • →Discuss risks, governance, and practical use cases
    • →Decide which workflows are realistic now and which should wait

    What the client receives

    • →Live demo and guided exercise
    • →Example workflows
    • →Agent workflow canvas
    • →Recommended next step
    Inquire about this lab →

    How to choose

    Start with the right entry point.

    If the company needs
    Start with
    Quick view of adoption gaps
    → AI Work Standards Audit
    One team to change real work
    → AI Adoption Sprint
    Internal ownership after training
    → AI Use-at-Work Champion Builder
    Fast proof of concept
    → Rapid AI Prototyping Lab
    Agent readiness and governance
    → AI Agent Management Lab
    Agentic workflows demonstration
    → Hermes Agent Demo Lab

    What makes the programs different

    The output is not attendance. The output is changed work.

    Standard training program

    • ✕Teaches AI concepts
    • ✕Ends with a certificate
    • ✕Uses generic examples
    • ✕Measures satisfaction
    • ✕Depends on external trainer
    • ✕Treats AI as a tool

    The Unlearning School program

    • ✓Changes real tasks
    • ✓Ends with documented workflows
    • ✓Uses team work
    • ✓Measures adoption signals
    • ✓Builds internal champions
    • ✓Treats AI as a work adoption problem
    AI adoption gap score

    2 minutes. 10 questions. Know where your organization stands.

    A weighted maturity model that gives you an instant score, gap analysis across four dimensions, and a ranked next-step roadmap. Use it before choosing a sprint, lab, audit, or training format.

    Take the AI adoption gap score →Free · 2 min · Instant result
    The final word

    Ready to move from AI experiments to adopted workflows?

    Get in touch if you already know what team you want to work with.

    Take the AI Adoption Gap Score if you first want to understand where adoption is stuck.

    Contact us →AI Adoption Gap Score →

    The Unlearning School - AI adoption training for companies

    The Unlearning School helps companies turn scattered AI usage into shared work practices. Programs focus on Copilot, Gemini and ChatGPT adoption, AI literacy evidence, practical team routines and measurable business use in Romania and across the European Union.