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Executive Snapshot
Role: Lead Instructional Designer, AI Hub Strategist, Facilitator
Audience: All employees
Scope: AI upskilling, adoption support, habit-building interventions
I helped design and evolve the AI Hub initiative to increase employees’ practical use of generative AI, with a strong focus on positioning Dinootoo as the safer internal alternative to external AI tools.
The strategy combined workshops, micro-webinars, micro-videos, prompting challenges, job aids, and AI assistants with habit-building solutions such as the Dinootoo Bingo initiative, screensaver-based Prompt of the Month nudges, and deployment of the Dinootoo app to company computers in cooperation with IT.
This project shows how I design not only learning activities, but also adoption systems that reduce friction and support lasting behavior change.
Overview
I have designed and deployed multiple formats of learning activities (workshops, micro-webinars, micro-videos, prompting challenge on gamification app) and job-aids (AI assistants, infographics with prompting techniques) to assist our colleagues in overcoming the initial resistance. In later iterations of the strategy, I also introduced habit-reinforcement solutions aimed specifically at keeping Dinootoo visible and easy to access in daily work: a Dinootoo Bingo initiative, screensavers with a “Prompt of the Month,” and, in cooperation with IT, installation of the Dinootoo app on all company computers to reduce friction and bring the tool closer to employees.
After a 6-month pilot consisting of eLearning micro-videos and F2F workshops on prompt engineering, we conducted interviews with 30 employees in order to adjust our training strategy, which turned out to be priceless. Not only did we figure out that most employees have no clue about the AI Hub learning initiative since we did not have a proper space of promoting them, but also that they can prioritise synchronous learning activities.
Hence I created a Sharepoint site with the possibility to enroll into various types of activities and started organising regular micro-webinars presenting intriguing Gen AI use cases. Over time, the strategy expanded beyond training delivery into environmental habit design: making Dinootoo easier to notice, easier to open, and easier to try in the flow of work.
Business Context & Performance Problem
The organization had already introduced a Generative AI tool internally, but tool availability did not translate into usage.
The core business risk was not lack of awareness, but:
Low confidence in practical application
Fear of misuse or “doing it wrong”
Inability to translate AI capability into day-to-day work value
Doing nothing meant:
Continued underutilization of an expensive strategic tool
Growing skepticism toward future digital initiatives
Fragmented, ad-hoc AI usage driven by individuals rather than capability building
Success was defined not by course completion, but by observable behavior change:
Employees using AI independently
Returning to the tool weekly
Generating role-relevant use cases without L&D mediation
Action Mapping (Diagnostic Backbone)
I used Action Mapping as the primary diagnostic framework, supported by interviews with 30 employees after an initial pilot.
Business Goal
Our primary objective was to ensure comprehensive adoption of Generative AI across our organization of 1000+ employees. This meant not only introducing them to the technology but transforming it into an integral part of their daily workflow, ultimately leading to improved efficiency and productivity in their respective roles.
To measure the success of our initiative, we established three key performance indicators:
Initial Adoption Rate: 85% of employees have successfully accessed and utilized the tool at least once by the end of 2025
Sustained Engagement: 50% of weekly active users by the end of 2025
Use Case Development: Measuring the diversity and quantity of unique use cases identified through workshop participants = 50 by the end of 2025
Critical Behaviors
Employees:
Open the AI tool proactively
Formulate effective prompts for real tasks
Iterate based on AI output quality
Share and adapt use cases within teams
Key Barriers Identified
A) Knowledge & Skill Gap Challenge:
Employees demonstrated limited understanding of the tool's practical applications and lacked fundamental prompt engineering skills.
Solution Implementation:
Developed a series of use case-oriented micro-videos integrated into a gamified educational platform
Designed targeted, regular micro-webinars focusing on specific prompting skill development
Created engaging micro-challenges to encourage prompt exploration
Produced blog content featuring practical applications through video demonstrations and hands-on exercises
Created AI assistant that helps find use cases specific for job line
Developed the Dinootoo Bingo initiative to encourage repeated experimentation with practical, low-barrier prompt tasks and to support habit formation over time
B) Environmental Time Constraints Challenge:
Employees struggled to prioritize eLearning within their busy schedules.
Solution Implementation:
Orchestrated focused two-hour workshops customized for 70+ distinct teams
Developed an engaging gamified eLearning on Beedeez application
Implemented incentive-based learning through prompting challenges
Created special events like the AI Advent prompting calendar with tangible rewards
Introduced screensavers with a “Prompt of the Month” to keep one practical use case continuously visible and reduce the effort required to think of ways to use Dinootoo
C) Information Accessibility Challenge:
Limited visibility and accessibility of the AI tool as well as AI hub training materials within organizational infrastructure.
Solution Implementation:
Developed a dedicated SharePoint site housing all educational resources on Gen AI
Facilitated Slovak language localization of the tool
Integrated the AI tool into the organization's standard application suite
Created streamlined workshop and micro-webinar registration processes
In cooperation with IT, helped deploy the Dinootoo app to all company computers so the tool became easier to access and more present in employees’ daily digital environment
This combination of instructional and environmental solutions was designed not only to improve skill, but also to make Dinootoo the easiest and most visible first choice compared to external AI tools
What Was Intentionally Excluded
Deep AI theory
Model architecture explanations
Generic “AI trends” content
These were excluded because they did not directly support the required behaviors.
Action-map connecting goals to desired behavior, barriers, training and non-training solutions
Experience the eLearning Solutions
Constraints & Friction
This project operated under several non-negotiable constraints:
AI upskilling was not a top-down executive priority
Primary communication channel was an underperforming intranet
Learners had limited discretionary learning time
Adoption depended heavily on manager reinforcement, which varied by department
These constraints informed both format selection and promotion strategy.
Options Considered & Rejected
Several plausible approaches were intentionally rejected:
Single mandatory AI course
Rejected due to low retention and poor habit formation.
Self-paced-only eLearning
Pilot data showed low completion and low perceived urgency.
Instead, I chose a blended, behavior-first approach combining synchronous commitment with lightweight, repeatable reinforcement.
Design Strategy & Key Decisions
Key strategic decisions included:
Prioritizing calendar-booked micro-webinars and workshops over optional self-paced modules
→ Increased attendance and perceived importance.
Designing content around job-specific use cases, not AI features
→ Reduced cognitive load and increased relevance.
Using gamification selectively (challenges, rewards)
→ To overcome activation energy, not to “entertain.”
Treating AI assistants as performance support, not training content
→ Enabled just-in-time application.
Use of AI as an Accelerator
AI was used intentionally to accelerate thinking and execution, not as a showcase.
AI supported:
Action map drafting and iteration
Rapid prototyping of micro-learning content
Prompt examples and scenario variations
Faster iteration cycles based on feedback
AI was not used to:
Replace instructional judgment
Automate learner feedback interpretation
Generate generic content at scale
Implementation & Delivery
I led:
Strategy definition and KPI design
The AI Hub team
AI Ambassadors onboarding & leading
Learning architecture and format selection
eLearning development (micro-videos, challenges, AI assistants)
Workshop and Webinar design and (partially) facilitation
Evaluation framework setup
Delivery included:
AI Ambassadors community
Beedeez (gamified challenges)
Synthesia (micro-videos)
AI Assistants for use-case exploration
Rise 360 (supporting content)
Cornerstone LMS (tracking and reporting)
Workshops specific for job-lines
Micro-webinars (use-case oriented)
SharePoint hub for visibility and enrollment
MS Teams community
Top AI Assistant competition
Evaluation methods
Quantitative Metrics:
Weekly tracking of total user adoption
Analysis of consistent user engagement patterns
Measurement of unique use case implementation
Qualitative Assessment:
Implementation of Kirkpatrick Level 1 surveys for immediate feedback
Follow-up Kirkpatrick Level 3 evaluations after two months
eLearning NPS evaluation
Assessment of Barriers in Adoption
In-depth interviews with 30 participants regarding adoption barriers and preferred formats of instructional activities
Outcomes, Signals & Learnings
After the 6-month pilot:
+735 % increase in first-time AI tool users
~580 % increase in weekly active users
45 complex and unique use-cases / AI assistants identified
Clear increase in employee confidence and experimentation
NPS of Workshops 90
However:
Adoption plateaued once initial promotion slowed
The use of external AI tools dominated
After training 85% of the employees the role of AI Ambassadors needed to change
This led to a strategy pivot toward habit-building initiatives, prompting challenges, safe use of external AI, stronger promotion and manager engagement.
Learning Science
Constructivist Learning Theory
Encouraged active learning through real-world, job-specific use cases and hands-on exercises.
Integrated social learning opportunities via peer-to-peer knowledge sharing and collaborative workshops.
Engagement Strategies:
Behaviorism & gamification elements for sustained motivation (e.g., challenges, rewards, badges) to motivate learners and reinforce desired behaviors)
Management reinforcement mechanisms
ARCS motivational model
Attention: Captured learner attention through engaging, interactive content, AI avatars and gamified elements.
Relevance: Highlighted relevance by tailoring content to specific roles and workflows.
Confidence: Built learner confidence through scaffolded skill development and immediate application opportunities.
Satisfaction: Maintained satisfaction with gamification, tangible rewards and success stories.
Instructional Design:
Cognitive load management: Designed materials to manage cognitive load by breaking content into micro-learning modules and using multimedia principles.
Scaffolding: starting with easier topics & tasks and increasing the difficulty level over time (mostly in eLearning activities).
Spaced repetition system: throughout the project we re-used or rather recycled certain prompting techniques and included them in the afore-mentioned different formats of activities, so as to secure a long-term skill retention. We did not rely on one-off learning activity to develop learners' skills.
Learning Approach
Phase 1: Awareness and Buy-in
Executed targeted marketing campaigns highlighting practical benefits
Developed compelling success stories from early adopters
Conducted management briefing sessions
Created awareness through multiple internal communication channels
Phase 2: Core Implementation
Facilitated interactive workshops and micro-webinars
Developed micro-videos on customized prompting techniques and AI assistants
Launched an aI prompting challenge using a gamification app Beedeez
Phase 3: Reinforcement and Support
Deployed micro-learning videos through multiple platforms
Conducted regular prompting technique-focused micro-webinars
Implemented gamified learning challenges
Added habit-building interventions such as the Dinootoo Bingo initiative, screensaver-based “Prompt of the Month” nudges, and broader desktop availability of the Dinootoo app to reinforce repeated use in day-to-day work
What This Project Demonstrates About Me
I diagnose performance problems before designing solutions
I make explicit tradeoffs under organizational constraints
I design for behavior change, not content delivery
I use AI to reduce cycle time and risk, not to decorate solutions
I am comfortable operating without perfect information and iterating based on evidence
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