Hands-on AI training for companies
AI training for businesses
So AI becomes your team’s way of working, not a one-off presentation
Milios delivers artificial intelligence training for companies and their teams. We teach people to use ChatGPT, Claude, Gemini and other AI tools for real work tasks, not generic examples. Training takes place in Vilnius, Kaunas, at your office anywhere in Lithuania, or online.
Trusted by
Where AI genuinely helps them
How to frame a task properly
How to supply context
How to check the result
What information must never be shared
When AI is enough
When it is time to automate the process instead
Good AI training doesn’t end with “that was interesting” — it ends with a new way of working the team can apply the next day.
People know the tool but can’t see it in their own work
Someone may well understand that AI can write text or analyse documents and still not know: “where exactly do I use this in my job?” So we work with real job functions in the training, not just generic examples.
Different departments have different tasks
A sales team is solving entirely different problems from finance.
For managers — preparing decisions and analysis
For administration — processing information
For customer service — enquiries and knowledge search
For operations — documents, processes and repetitive work
So a single training format rarely suits an entire organisation equally well.
AI skill levels vary widely within a team
One person uses AI every day
Another has typed a handful of questions into ChatGPT
A third avoids it entirely, unsure what can safely be done with it
For larger teams we design the training to level the fundamentals first, then move into targeted topics by function and readiness.
Tools get used without any shared rules
AI use inside a company usually starts before any internal guidelines exist. People pick their own tools and don’t always know:
What information may be shared
What must never be uploaded
When the output has to be verified
When an AI answer can’t be relied on by itself
Who is accountable for the final result
So practical training has to cover the limits, not just the possibilities.
After the training, people slip back into the old way of working
A one-off lecture can spark interest. But if someone doesn’t come away with a concrete method and see how AI applies to their own task, the new tool is quickly set aside. That is why we design training around real work.
Outcome
What you are actually buying
Not a presentation about the latest AI tools. Not a few hours of “prompt engineering”. Not a technology overview that will be out of date in a few months.
You get a practical artificial intelligence training and adoption programme for your team. Its result should be:
A shared foundation
Clear working methods
Scenarios tailored to each department
Practical templates
Clear limits for safe use
Real use cases
Next steps
We adapt the content to your team’s size, functions, current level of AI use and business needs.
Preparation
First we understand how your team actually works
We don’t want to guess what matters to you, so we gather context beforehand.
We may ask for:
A list of the main teams or departments
The tasks done most often
The systems and AI tools in use
Typical documents with sensitive information removed
The problems that recur most
Participants’ level of AI experience
What leadership expects
Specific questions the team wants answered
From that we choose:
The structure of the training
The practical exercises
The demonstrations
The department scenarios
The right level of difficulty
Your team shouldn’t have to fit a generic programme. We fit the programme to your team.
Programme
A recommended path for a larger team
When an organisation has more people and more varied functions, a single shared lecture usually isn’t enough. We recommend a staged programme.
Assessing needs and current AI use
Step 01
Before the training we establish:
How people use AI today
Which tools are already in use
Which functions hold the most potential
Which topics are most relevant
What the main security or usage risks are
The point is not to start the training from generic assumptions.
Shared fundamentals for the whole team
Step 02
We level the essential knowledge. The team understands:
What generative AI can and cannot do
How a model receives context
How to frame a task
How to verify the result
How to judge the risk of hallucination
How to work safely with business information
How to pick the right tool
The aim is a shared vocabulary across the organisation.
Assessing level and needs
Step 03
After the fundamentals we can see which groups need more practice and which are ready for more advanced scenarios. That avoids spending time on identical content for people with completely different needs.
Targeted workshops by function
Step 04
From there we work with specific teams, for example:
Sales
Administration
Management
Marketing
Customer service
Finance and analytics
Operations
In the workshops participants don’t just watch a demo. They do the tasks themselves.
Working templates and methods
Step 05
After the training nobody should have to start from a blank ChatGPT window. So we prepare, or build together:
Prompt structures
Working templates
Principles for supplying context
Checklists for verifying results
Worked examples of specific scenarios
The point is that a good result can be repeated.
Capturing automation opportunities
Step 06
Training often reveals tasks that shouldn’t be done by hand every time, even with AI. Where we spot a repeating process, we flag it as a possible use case for:
Automation
An AI agent
Data work
A custom solution
That way the training also becomes a practical way to uncover further AI opportunities.
Sales
Customer research
Meeting preparation
Drafting communications
Meeting summaries
Structuring CRM information
Administration
Document analysis
Structuring information
Drafting emails
Processing information from meetings
Speeding up repetitive tasks
Management
Summarising information
Scenario analysis
Preparing decisions
Structuring risks
Verifying information
Marketing
Research
Generating ideas
Content structure
Creating variants
Analysis and optimisation
Customer service
Drafting responses
Knowledge search
Classifying enquiries
Preparing context for complex cases
Finance and analytics
Document analysis
Explaining data
Preparing reports
Verifying information
Structured preparation for analysis
Operations
Documenting processes
Processing documents and requests
Workflow analysis
Identifying repetitive steps
01
A practical working method
We don’t teach one good prompt. Participants learn a repeatable sequence:
Define the task
Supply the context
State the result you want
Get structured output
Verify the result
Refine the process
This method stays useful even as the specific AI tools change.
02
Scenarios matched to their own work
Instead of a generic “write some marketing copy” example, participants see scenarios close to their actual job.
03
Practical templates
Depending on the programme, these might be:
Structures for drafting emails
Instructions for analysing documents
Templates for processing meetings
Research structures
Report templates
Checklists for verifying results
04
Clear limits for safe use
Participants understand:
What can be given to an AI tool
What shouldn’t be
How to handle confidential information
When sources must be checked
When the decision has to stay with a person
05
A clearer sense of what is worth automating
People start to tell apart a task worth doing better with AI from a task worth automating outright.
A shared foundation for using AI
The team shares a more consistent understanding of AI’s capabilities, limits and method.
Use cases tailored by department
Not abstract “AI possibilities”, but specific tasks where AI can be used.
A set of working templates
People can adapt the examples used to their own day-to-day work.
Safer AI use
The team more clearly understands:
How sensitive information is
The risks of sharing data
Verifying results
Human accountability
A list of automation opportunities
Repetitive tasks that surface during the practical sessions can be recorded for later assessment.
Clear next steps
After the training you can distinguish:
What the team can start applying on its own
Where more practice is needed
Where usage guidelines are needed
Where automation is worthwhile
Where a technical solution is required
AI fundamentals for the team
AI training for companies where employees’ experience with AI varies widely.
Possible topics:
How generative AI works
Using ChatGPT, Claude, Gemini and other tools
Framing a task
Supplying context
Structuring results
Verifying information
The risk of hallucination
Analysing documents and text
Safe use
Practical working templates
Practical workshops tailored to a department
AI training for employees working in one specific function.
The whole programme is built around that department’s real tasks.
Practical automation workshops
For an advanced team already using AI that wants to move from one-off prompts to:
Workflows
Automation
AI agents
Integrations
The aim is to understand not only how to do a task with AI, but how to turn it into a repeatable process.
A bespoke programme for the organisation
When an organisation has several departments and varying readiness, we build a staged programme:
Needs assessment
A shared foundation
Level assessment
Target groups
Practical workshops
Next steps
In practice
Less theory in training, more real work
In the practical sessions participants have to do the task themselves. For example:
Analyse a document
Produce a structured report
Condense a large volume of information
Extract the key facts
Write a meeting summary
Create a working instruction
Check an AI answer against its source
Build a reusable working template
Judge whether a task is worth automating
A demo shows what is possible. Practice shows whether someone can actually use it in their job.
Safety
Safe AI use is part of the programme
AI training shouldn’t only teach what is possible. The team needs to understand the limits too.
Based on your use cases we cover:
Handling confidential information
Personal data
The difference between consumer and business accounts
Verifying information
Using sources
Copyright questions, where relevant to the scenario
Human accountability for the final result
Whether internal AI usage guidelines are needed
If the organisation needs it, we can extend the programme to include drafting internal AI usage principles.
Value
Value that appears during the training itself
You shouldn’t have to wait months to see a result.
During a practical session participants can:
Build their first working template
Improve a real task
Find a more efficient way of working
Work out which tasks AI isn’t suited to
Identify a process for automation
So we aim to create value not only “after the training”. It should appear during the session itself.
Involvement
How we keep the prep work off your team
You don’t have to put the programme together yourselves.
From your side
We usually need you to:
Tell us how many will attend and in which roles
Explain the main goals for the training
Share a few typical work scenarios
Where possible, provide anonymised sample documents or tasks
Tell us which AI tools the team already uses
From the Milios side
We:
Put the programme together
Pitch the content at the right level
Prepare the practical scenarios
Run the training
Facilitate the practical exercises
Produce the agreed training material
Record the valuable use cases
Recommend the next steps
You provide the context. We turn it into a practical programme.
Office hours
A Q&A session after a period of real use
Advanced workshops
Mentoring for specific teams
Assessing new use cases
Refining internal AI working templates
Analysis of automation opportunities
The goal isn’t to create a permanent dependence on a consultant.
The goal is for the team to become steadily more self-sufficient, with our help reserved for where deeper expertise is genuinely needed.
Measurement
How we judge the result of the training
A participant survey alone isn’t enough to measure everything.
Depending on the programme we can assess:
The change in participants’ level of AI use
How the practical exercises were completed
How many working templates were created
The use cases identified
The automation opportunities found
People’s confidence in using AI
Whether AI use continues after the training
Where specific evaluation criteria are needed, we agree them before the programme starts.
Implementation
Training as part of an AI rollout
A technical solution doesn’t work if people don’t take it up. So training can be more than a standalone service — it can be part of a larger AI rollout. For example:
Analysis and preparation
Team training
Process automation or an AI agent
Embedding the new way of working
In that case the training helps the team understand not just a new tool but a changed way of working.
Fit
When this service is a good fit
Hands-on AI training for companies and employees is especially useful if:
People use AI in very different ways
Part of the team is only just starting
ChatGPT is used, but not systematically
Leadership wants AI used more safely
The team doesn’t know where to apply AI in real work
Generic lectures haven’t produced practical results before
You want to prepare the team for an AI rollout
You want to find automation opportunities together with your people
Different departments need different scenarios
When training alone may not be enough
If you already know exactly which process needs automating, training isn’t necessarily the first step. In that case it may make more sense to go straight to assessing:
Process automation
An AI agent
A data solution
A custom system
If the problem is the process rather than people’s skills, we will recommend fixing the process.
We teach against real work
We build the programme around the tasks your team actually does, not around a list of tool features.
We understand the technical delivery side too
During the training we can distinguish:
What a person can do with AI themselves
What is worth standardising
What is worth automating
Where an agent is needed
Where a custom solution is needed
We teach a method, not a single product
AI tools change. The working principles — framing the task, context, structure, verification and risk assessment — don’t.
We can carry on from training through to implementation
If a valuable process emerges during the practical sessions, we can help to:
Analyse it
Automate it
Turn it into an AI agent use case
Integrate it into a system
Is the training suitable for complete beginners?
Yes. We start with shared fundamentals so the whole team has the same understanding of what AI can do, its limits and how to use it safely. No technical knowledge is required.
Can the training be for a single department?
Yes. Workshops can be organised for a single function, such as sales, marketing, administration or finance. All exercises then come from that department’s daily work.
Can you train a larger team?
Yes. For larger organisations we offer a staged programme: a needs assessment, shared fundamentals for everyone, and targeted workshops by function and level.
Do you use our real examples?
Yes. Before the training we collect your typical tasks and anonymised document examples. The hands-on exercises are based on them.
Do you only teach ChatGPT?
No. We teach ChatGPT, Claude, Gemini, Microsoft Copilot and other tools, but the most important thing is a working method that stays useful however the tools change.
Do we get the material afterwards?
Yes. Participants receive the agreed training materials, work templates and result-checking checklists.
Can you help after the training?
Yes. We can continue with consulting hours, a Q&A session, advanced workshops, or help automate the processes discovered during the training.
Does the training help meet the requirements of the EU AI Act?
Since 2 February 2025, Article 4 of the EU AI Act requires organisations that use AI systems to take measures to ensure a sufficient level of AI literacy among their staff. Our training includes a safe and responsible AI use component, so it helps you meet this requirement, and participation can be documented.
Want your team to actually use AI, not just try it once?
Tell us how many people will take part, which departments they represent, how they currently use AI and what change you want to see afterwards. From that we will propose the right programme structure — from shared fundamentals through to targeted practical workshops.