The work in plain language
Teach the thinking, not the interface.
Paloren delivers AI literacy training that gives every employee the same working standard, from approved tasks to review habits. When some employees use AI for everything and others avoid it entirely, you need a common starting point. We help your people understand where AI is useful, what information they can share and how to spot an answer that sounds right but is not. Everyday workplace scenarios make the decisions concrete, with no coding knowledge required. Learners practise giving instructions, checking results and knowing when to ask for help. You get a more confident team and a clearer view of the gaps still to address. Training supports responsible use; attendance alone is not proof of regulatory compliance.
What this can change for your team
- Less hesitation over appropriate everyday AI use
- Fewer avoidable mistakes with company information
- A shared standard for checking work before it goes further
01 / 04AI literacy
Give people the judgement to decide, not just definitions.
Learners distinguish generating a response from finding a verified fact, and a suggested action from an authorised action. They examine why a fluent answer can still omit an important exception. The vocabulary stays connected to work: prompts are instructions, context is available material and evaluation is checking against an agreed standard.
How we make this work
Literacy training gives employees practical understanding of the systems they encounter, without making them engineers. Learners distinguish generating a response from finding a verified fact, and a suggested action from an authorised action. They examine why a fluent answer can still omit an important exception. The vocabulary stays connected to work: prompts are instructions, context is available material and evaluation is checking against an agreed standard. The objective is to recognise when AI fits the task, what evidence is needed and when a person or another method is better.
- Generation versus verified source retrieval
- Suggested action versus authorised action
- Task selection based on consequences and evidence
02 / 04AI literacy
Simple agenda where mistakes are safe.
Learners sort example tasks into suitable, needs-review and do-not-use categories under a supplied policy. They compare a generated summary with its source and find the omitted exception. Guided practice follows with a worked example internal announcement. Cohorts are up to 25 people; pricing from USD 3,500 per session.
How we make this work
Paloren starts with learners sorting example tasks into suitable, needs-review and do-not-use categories under a supplied policy. They compare a generated summary with its source and find the omitted exception. Guided prompting practice follows with a worked example internal announcement, an audience and a clear output format. Learners then review a deliberately flawed response containing an unsupported claim. The final exercise asks them to choose a method and explain their checks without a facilitator leading each step. We can run this as a half-day workshop or shorter linked sessions, matching pace and breaks to the group's confidence. The curriculum covers what AI does (generation, not retrieval, most of the time), how to brief it with the necessary context, why fluent-sounding output still needs verification, how to identify what information is safe to share, and when to escalate to a human reviewer. Cohorts are up to 25 people; pricing from USD 3,500 per session.
- Task sorting using a supplied acceptable-use policy
- Summary checking against an original source
- Independent explanation of checks and escalation
03 / 04AI literacy
Make safe habits normal before real data appears.
Employees learn to identify personal details, confidential business information and credentials. We confirm approved accounts and restrictions before hands-on practice. Worked examples let beginners make mistakes without experimenting on customer records. We also show how to report a concern, stop an unsuitable task and ask for help without turning uncertainty into blame.
How we make this work
The practical question is what information may enter a tool. Employees learn to identify personal details, confidential business information and credentials, then apply your organisation's rules instead of guessing from a product name. We confirm approved accounts and restrictions before hands-on practice. Worked examples let beginners make mistakes without experimenting on customer records. Learners without access can still analyse supplied outputs, but that is distinguished from interactive practice. We also show how to report a concern, stop an unsuitable task and ask for help without turning uncertainty into blame.
- Data-category recognition exercise
- Approved account and policy preparation
- A named route for questions and concerns
04 / 04AI literacy
Assess judgement and clear the next blocker.
A learner should identify an unsuitable input, explain a limitation, check a claim and find the responsible reviewer. We compare a baseline scenario with a later independent exercise to see which concepts need another explanation. Follow-up asks whether employees can apply your rules in ordinary work and whether those rules are clear enough to use.
How we make this work
Assessment looks at decisions around the task, not how impressive the output appears. A learner should identify an unsuitable input, explain a limitation, check a claim and find the responsible reviewer. We compare a baseline scenario with a later independent exercise to see which concepts need another explanation. Follow-up asks whether employees can apply your rules in ordinary work and whether those rules are clear enough to use. A learning record can document participation and feedback; it is not professional certification or proof of compliance. Further training is recommended only where a demonstrated need remains.
- Baseline and final scenario assessment
- Individual feedback on judgement and checking
- Follow-up questions for policy and learning owners
What you take forward
What you leave with
Beginner learning agenda
Safe-use scenario pack
Plain-language terminology sheet
Practical assessment feedback
Task-checking checklist
Follow-up learning actions
- 01
Establish the baseline
Ask learners to classify a task and explain their current checks.
- 02
Teach through examples
Compare sources, instructions and deliberately flawed outputs.
- 03
Practise independently
Apply the policy and checking method to a new scenario.
- 04
Close the gaps
Review unresolved questions with the learning and policy owners.
| Stage | What it changes |
|---|---|
| Establish the baseline | Ask learners to classify a task and explain their current checks. |
| Teach through examples | Compare sources, instructions and deliberately flawed outputs. |
| Practise independently | Apply the policy and checking method to a new scenario. |
| Close the gaps | Review unresolved questions with the learning and policy owners. |
The next move
Could every team explain which AI tasks are safe, reviewed or off limits?
Tell Paloren who needs a common starting point. We will send a proposal within two business days and reply within one business day.
- First reply
- From the team, within one business day.
- What to bring
- The problem, the systems involved and what should work better.
- What happens next
- Paloren turns it into a working brief. The team reviews it.
Before we begin
Questions we get asked, answered with numbers
Is this a technical AI course?
No. It is a 3.5-hour session for cohorts of up to 25 people with no coding and no jargon beyond what a manager needs to make a decision. Engineers who want repository practice should take the Claude Code course instead.
Can literacy training satisfy a legal requirement?
It produces a record: attendance, the topics covered and a short assessment, within 5 business days of the session. Whether that meets an obligation (the EU AI Act's literacy duty, for example) is a call for your legal or compliance lead; we scope the objectives to their reading of it.
What if employees are worried about AI?
Then the session is doing its job. About a third of the 3.5 hours is questions, and we separate what has been decided from what is speculation. Managers should tell people beforehand what the programme is for and what has not been decided about roles.
What happens after the introductory session?
Within 2 weeks every learner gets one defined practice task, and the organiser gets a summary of the gaps: skills, unclear policy or missing access. From there, some teams book tool-specific workshops; others need a policy before they need more training.
How large can an AI literacy cohort be?
A literacy session is a 3.5-hour session for cohorts of up to 25 people, with pricing from USD 3,500 per session. There is no coding and no jargon beyond what a manager needs to make a decision. Engineers who want repository practice should take the Claude Code workshop instead.
Could every team explain which AI tasks are safe, reviewed or off limits?
