AI Day for Employees in 2026: Program and Workshop

Corporate AI Day: how to collect case studies, divide participants by level, conduct safe workshops, and continue ideas after the event.

AI Day for employees is an internal day of practice with artificial intelligence. Employees work on tasks from their departments using approved tools. By the end of the day, the company has task cards, responsible persons, and verification rules; general lectures alone are not enough for this.

At Aventura, we start preparation with the work context. HR and the training team are responsible for the audience, digital transformation for cases and tools, information security and lawyers for the boundaries of data handling. Then we combine content, stage, workshops, demo zones, and technical plan into one scenario.

Why does a company need a separate AI Day?

AI Day is needed when employees are already trying artificial intelligence tools but act in a fragmented way. Some don't know where to start. Others upload unnecessary data to services or trust the first answer. A separate day aligns basic terms and rules, shows the boundaries of acceptable use, and turns interest into several verifiable work tasks.

The day has three goals: explain the technology's capabilities and limitations, practice techniques on approved materials, and select initiatives for further validation.

AI Day does not replace corporate policy, information security training, or a long-term skills development program. It provides a common start and helps see the difference between an impressive demonstration and a useful scenario. For a complex company, we build it according to the logic of a business event: goal, audience, content owners, participant journey, and a documented outcome.

The closest format on the website is company innovation day. The difference is that AI Day requires a separate framework for data, answer verification, authorship, and human oversight.

Result of the day: cases, skills, and solutions

After the event, each selected scenario should have a next step. The participant can choose an approved tool, formulate a query, verify the answer, and understand when the task requires an expert. The department receives a task card with an owner and a benefit criterion. The company receives a list of issues to be addressed by policy, training, or a pilot project.

We do not aim to 'inspire everyone with artificial intelligence'. Emotion helps hold attention, but it is difficult to use the next working day. Observable results look more concrete:

• an employee distinguishes between open, internal, and confidential data;
• knows which services the company allows for work tasks;
• verifies facts, calculations, references, and limitations of the answer;
• can describe the task before choosing a tool;
• records the expected result and verification method;
• passes a promising case to the process owner.

The NIST Artificial Intelligence Risk Management Framework (NIST AI RMF) proposes four functions: Govern, Map, Measure, and Manage. For the day's program, we translate them into four questions: who sets the rules, where the system is used, how the result is verified, and who makes the decision about further use.

Instead of a public ranking of participants, we evaluate the quality of the card and the clarity of verification. An experienced user may not know the process limitations, while a beginner may well understand the work task itself.

How to assemble working cases before the event?

Before developing the program, we collect short task cards from departments. The form describes the costly action, required data, acceptable outcome, and the person who will verify the answer. After initial screening, specialists choose a route: open demonstration, protected environment, or rejection with a written explanation of the reason.

A working case card contains:

1. Context and process owner.
2. Specific current action of the employee.
3. Input data and its access level.
4. Desired result without promising automation.
5. Quality check criteria.
6. An error that would cause noticeable harm.
7. Approved environment for the practicum.
8. Next step after the event.

For the stage, we choose cases understandable to multiple functions. For the laboratory, narrow tasks from one department are suitable. For example, a general breakdown might show the document structure using synthetic data, while a closed pilot with real materials will take place later in an approved environment.

We also check whether the task duplicates an existing project. Corporate demo day helps compare mature initiatives, while AI Day first aligns basic skills and rules. These formats can be linked: strong cards from AI Day move to demo day after verification.

AI Day One-Day Program

The one-day program moves from general rules to independent practice. In the morning, participants get a unified context and see a breakdown of a real task. Then they split into routes, work in small groups, and return with solution cards. The finale is for selecting next steps, not for competing with polished presentations.

The sample program is adapted to functions, shifts, cities, and access to tools:

Lectures take up a limited part of the day. Even a strong expert cannot give the same useful depth to one audience. Practice starts with a simple task, and then the participant chooses complexity: context accuracy, source verification, working with multiple options, or designing human oversight.

For a large audience, the mechanics of a conference, forum, or seminar are suitable: the main stage sets the frame, while parallel rooms provide depth. Transitions, capacity, and equipment access time need to be considered as carefully as the content.

Three routes for different levels of preparation

A single workshop almost always splits the room into those who are bored and those who struggle. We offer three routes: basic for those who are solving a work task with AI for the first time, applied for regular users, and project-based for process owners. All routes share common safety and verification rules, but the tasks, pace, and expected outcomes differ.

The route is chosen by the participant or the group leader before the event. It should remain possible to switch between streams. If the basic group moves faster, the facilitator provides an additional example. If the project stream hits a legal or safety issue, the expert records it and does not improvise an answer on stage.

GOV.UK, in its guidance on developing AI skills for employers, links training to real tasks of the role and advises showing when the tool should not be used. It also describes a modular approach and different ways to participate. For AI Day, this logic is more useful than a general workshop for the whole company.

How to run a workshop without data leakage?

A secure workshop is built on a pre-approved environment and prepared materials. Participants should not have to decide on the spot whether they can upload a contract, customer database, HR document, or internal report. Before the event, organizers agree on services, accounts, data sets, screen recording rules, and how to contact the specialist.

We divide materials into four levels:

1. Public data already lawfully published.
2. Synthetic examples created specifically for the exercise.
3. Anonymized training sets after review by the data owner.
4. Real work data only in an approved secure environment and not in a mass workshop.

Federal Law No. 152-FZ links the processing of personal data to specific and predetermined purposes. The data composition must correspond to these purposes. The AI Day event itself does not create a separate basis for collecting additional information about employees or clients. For registration forms, diagnostics, and task cards, access and retention periods must be defined separately according to the processing purpose.

The OWASP GenAI Security Project describes risk classes for applications using large language models, including prompt injection and sensitive information disclosure. At the event, we do not turn this material into a technical lecture for everyone. It is enough to show real-world situations: a suspicious instruction inside a file, a request for restricted data, a convincing answer without a source, and an attempt to use a result without verification.

NIST advises documenting the context of use, limitations, feedback, and risk management actions. Therefore, each laboratory receives a simple protocol: what was entered, what was obtained, how it was checked, what remains unknown, and who is responsible for the next step.

The related article about the employee cybersecurity day helps separate general event practice from mandatory information security rules. The process owner approves the policy. The organizational team makes it understandable in the participant journey.

Team roles and speaker preparation

Before the final scenario, we assign those responsible for the day's goal, training, security, law, and production. The leader sets the meaning of AI Day. Experts are responsible for rules in their area, and the moderator keeps the conversation within verifiable tasks. The organizational team connects the program with the venue, registration, equipment, access, and backup.

We assign roles before the final scenario:

• the client approves the goal, audience, and outcome of the event;
• the learning and development team (L&D) or HR collects participant profiles and learning paths;
• digital transformation manages the pipeline of use cases;
• information security aligns tools and data;
• the lawyer checks applicable restrictions and wording;
• process owners prepare working tasks;
• speakers and facilitators undergo editorial preparation;
• the moderator manages questions, time, and the boundaries of promises;
• the technical team prepares accounts, network, screens, and backup;
• coordinators assist with flows and record questions.

The speaker is given one question, several key points, and a specific conclusion. The demonstration is rehearsed with the same account permissions and data that will be on site. If the service is unavailable, the facilitator has a recording of the approved scenario, screenshots of key steps, and a task that the group can work through without a connection.

Demo zone and departmental labs

In the demo zone, departments show working scenarios and immediately explain how they verify the result. Each booth has a task, permitted data, the author of the approach, the verification method, and known limitations. Within a few minutes, a visitor should understand who benefits from the scenario and what is needed to verify it in their department.

Suitable stations:

• working with a long open document and checking references;
• preparing several options for message structure;
• classifying synthetic requests according to given criteria;
• searching for contradictions in a training set;
• creating a draft instruction with mandatory expert review;
• analyzing an erroneous answer and reconstructing the verification chain.

Workshops and hands-on stations require the same instructions for facilitators. We specify the entry, action, expected outcome, limitation, and how to help the participant. The station should not collect work data into the facilitator's personal account or save history without an agreed-upon basis.

We build exchange between departments around the task. The participant marks proposals they want to discuss and receives the contact of the owner. On the shared board, each proposal has a status: idea, needs verification, ready for prototype, or stopped with a reason.

How to check the venue and equipment?

Before the employee AI Day, we rehearse the full participant journey from invitation to submitting the task card. First we check accounts, access, and the network, then equipment, navigation, and connectivity between rooms. Finally, the team runs through the backup scenario in case of failure so that facilitators know the procedure without improvising.

Run-through checklist:

1. Open the invitation and program on a phone.
2. Log in with a test participant account.
3. Verify access only to authorized services.
4. Run each exercise on training data.
5. Simulate network failure and unavailability of one tool.
6. Display the prompt and response on screen without personal information.
7. Send a question from the in-room and remote audience.
8. Check subtitles, contrast, navigation, and the ability to pause.
9. Walk through room transitions with a real group.
10. Verify card collection and access rights after the event.

Remote employees need a dedicated digital room, not a camera at the back of the hall. The W3C draft guidance on accessible remote meetings treats the platform, materials, audio, video, and participation as a single system. If the room works in small groups, the remote audience receives an equivalent task and their own facilitator.

For a distributed audience, you can draw on experience from hybrid and online events. A technical stream host monitors access and questions while the main moderator works with the stage.

Metrics and follow-up after AI Day

Reach and program evaluation only show participants' impressions. To verify application, we need data on completed workshops, task card quality, resolved questions, and decisions on pilot projects. These metrics help understand whether general interest turned into safe actions and whether the process owner received material for the next decision.

We divide metrics by levels:

Immediately after the event, participants receive a memo with allowed tools, a question channel, and materials from their route. The team publishes answers to common questions after review by owners. After an agreed period, case owners return to the cards and update the status.

UNESCO links institutional capacity to responsibly implement AI with skills, safeguards, leadership, and overcoming data and infrastructure barriers. ILO complements this picture with critical thinking, creativity, ethics, and human-AI collaboration. Therefore, a good follow-up after the event evaluates the quality of the decision, responsibility, and impact on people's work, not just the new service feature.

If the best ideas require deep collaborative development, they can be moved to a corporate hackathon. AI Day provides common rules for describing tasks and initial selection. A hackathon is needed later, when the task, team, data, and pilot project criteria are already defined.

Brief and Budget

Before calculating the budget, the client and the organizer agree on the initial conditions. In the brief we record the goal and the audience profile. Then - the level of preparation, permitted tools, data access rules, experts and the expected outcome. Without this framework, the budget compares the number of screens and facilitators, but not the quality of practice.

At the start, the following answers are needed:

• who the client is and who makes decisions on the cases;
• which roles and cities are involved;
• what employees already use;
• which services and accounts are permitted;
• what data is acceptable in the exercises;
• who approves security, legal and content;
• whether parallel tracks and hybrid are needed;
• how many labs and facilitators are required;
• how case cards are collected;
• what will happen to them after the event.

In the budget, we separately show the concept, script, speaker editing and task development. In the production block, we calculate the venue and stage equipment. Another block includes network, staff, design, registration and reserve. Variable lines depend on the number of streams, stations, cities and participants.

If your company needs a practical day with measurable work outcomes, we at Aventura can combine content, routes, and production into a single project. Request an estimate via the form, to discuss department tasks, data policies, and participation format.

Sources

• NIST: AI Risk Management Framework
• UNESCO: AI for the Public Sector
• GOV.UK: What works for AI upskilling - employer guide
• International Labour Organization: Core skills in the age of artificial intelligence
• 152-FZ, Article 5: principles of personal data processing
• 152-FZ, Article 6: conditions of personal data processing

• OWASP GenAI Security Project: LLM Top 10
• W3C: Accessibility of Remote Meetings