How Are We Using AI in 2025? Productivity-Out, Well-Being-IN
A new HBR study reveals: in 2025 we're using AI less for productivity and more for well-being, meaning, and personal growth. Surprising trends and inspiring use cases in the full post.
← Paths
Guided learning path
AI in Learning & Organizations
A path from changes in work to diagnosing, designing, building and responsibly adopting AI in an organization.
- Core
- 10
- Deep dive
- 12
- Practical exercise
- 1
Who this path is for
For learning, HR, organizational development and business leaders who want to connect AI to a real need instead of another isolated experiment.
What you will know
By the end, you can identify opportunities, analyze gaps, design and build an intervention, and assess value, adoption and risk.
Understand the change
Separate real usage, hype and shifts that require new capabilities.
Why go deeper here Start with what people actually do with AI, not what vendors or organizations assume they do.
Why go deeper here After real usage, place it on the adoption curve to distinguish experimentation, hype and sustainable value.
Gartner's Technology Adoption Hype Cycle 📉
So where does artificial intelligence sit on the curve? And how can we use it to lead technology adoption processes that generate real business value in the organization? If you'd like to read more about it, here's a link to the model on Gartner's website.
Why go deeper here Hype creates personal and organizational pressure. Before choosing an initiative, build a learning rhythm that avoids chasing every tool.
How to Deal with AI FOMO and Not Get Left Behind
How to deal with AI FOMO without chasing every new tool, and how to build learning and usage habits that stand the test of time.
Why now After reducing the noise, examine which roles and skills are actually changing and what the organization must prepare.
The World Economic Forum's Future of Jobs Report 2025 📑
An analysis of the Future of Jobs Report 2025–2030: which skills are changing, what role AI plays, and how to prepare for the next labor market.
Identify opportunities and gaps
Translate broad change into workflows, capabilities and data you can examine.
This chapter starts with Move from the skills map to the role of learning teams: where AI changes diagnosis, development, production and measurement.
How Artificial Intelligence Is Changing Organizational Learning and Development
A practical look at how generative AI is changing the workflows of Learning and Development professionals, from learning analytics and personalization to content creation, automation, and safer AI adoption.
Why go deeper here Identifying opportunities requires explaining a need, understanding a system and thinking about the user—not merely knowing tools.
The New AI Skillset Everyone Working with AI Must Develop
You don't need to know how to code to work with AI — but you do need to develop three core skills: explaining clearly what you want, understanding how systems work, and thinking like a product manager.
Now put it into practice Now apply the map to a real workflow and get priorities by impact, effort and solution type.
LearnOps Radar
A focused diagnostic that maps opportunities for AI, agents, skills and automation in learning workflows.
Exercise outcome
A prioritized opportunity map ranked by impact and effort.
Why now After choosing an opportunity, move from impressions to data and frame questions AI can help analyze.
Artificial Intelligence in Data Analysis and Learning Analytics 📉
How to use artificial intelligence to analyze learning data, uncover insights, and improve decision-making in learning development processes.
Why now The principles become a case study: broad macro data breaks down into individual knowledge gaps that can drive action.
Knowledge Gap Analysis: The Shift to Personalized Learning — A Case Study (Part 1)
From generic macro analysis to personalized learning: a bank knowledge-gap project with 1,250 employees and 31,771 answers, showing how AI turns mountains of data into action.
Design learning that works
Use learning principles, feedback and short-form content to design an intervention.
This chapter starts with An identified gap is not yet a solution. Return to feedback, context and repetition to understand how people actually learn.
What Can We Learn About Human Learning from Machine Learning? 🗣️
How principles from machine learning can help us understand human learning, feedback, context, and knowledge transfer.
Why go deeper here Move from principles to a real classroom where structure, measurement and AI combine in a complete learning experience.
What Happens When a Learning Solutions Designer Enters the Classroom? Insights from a Digital Learning Development Course
What does a digital learning development course at HIT look like when a learning solutions designer becomes a lecturer, combining a three-part structure, sensing quizzes, and AI to turn a classroom into a living learning lab?
Why now A complete learning experience need not be a large course. Short-form content can build rhythm, exposure and adoption over time.
What's the Connection Between Learning Management, a Marketing Funnel, and Short-Form Content?
How to use Short-Form Content to embed a culture of learning in your organization. Discover why authenticity and speed in content creation beat expensive productions and drive real adoption on the ground.
Why go deeper here When the need is specific and personal, a small tool tailored to the learning moment can replace a course or large system.
Disposable Apps — How to Build a Personal Solution in 10 Minutes ♻️
In an era where anyone can build their own digital solution in minutes, disposable apps are becoming a tool for learning, problem-solving, and smart self-customization with the help of AI.
Build employee experiences
Move from a designed intervention to a prototype people can try.
Why go deeper here After choosing a small intervention, learn how to turn it into a prototype through conversation with AI and without a full development team.
Vibe Coding – Building an App by Chatting with AI
Discover how to build applications with AI-assisted tools like Replit, Claude, and Lovable. A practical guide with tool comparisons, implementation ideas, a hands-on project, and tips for getting started with Vibe Coding.
Why now The ability to build gets a clear learning goal: a simulation in which employees practice and receive a response.
Creating an AI-Powered Service Simulator with Amazon PartyRock! 🤘
How to build an AI-powered customer service simulator with Amazon PartyRock, and what organizations can learn from it about employee training.
Why now Move from a single simulation to a complete employee experience around a critical moment: the period before the first day.
How Can You Improve Pre-Boarding with Vibe Coding?
How to use Vibe Coding to improve the pre-boarding experience and build solutions that connect employees even before their first day.
Why go deeper here After onboarding, examine another employee-lifecycle moment: an interactive assistant adapted to an interview process.
AI Job Interview Assistant – How to Build a Smart Interview Tool Without Code
A guide to building a no-code AI job interview assistant, including process structure, tools, and how tailored interview questions are generated.
Adopt, govern and measure value
Choose systems, examine impact, define risk and prevent implementation failure.
Why go deeper here A successful prototype is not necessarily an organizational system. Before purchasing, examine needs, integrations and the ability to evolve.
How to Choose an LMS and Come Out Alive 🖥️
How to choose an LMS intelligently: what questions to ask, what mistakes to avoid, and how AI is reshaping the decision-making framework.
Why go deeper here A system choice is not measured only by time saved. Examine how usage affects anxiety, agency and employee experience.
Does Artificial Intelligence Affect Our Happiness at Work?
HBS research suggests that AI can improve not only productivity but also feelings of competence, happiness, and reduced anxiety at work.
Why go deeper here After defining the desired outcome, choose the level of autonomy: a predictable workflow or an agent that makes decisions.
What Is the Difference Between Automation Workflow and Agentic Workflow
A clear explanation of the difference between standard automation and Agentic Workflow, and when to use each approach in your organization.
Why go deeper here More autonomy and data access require clear boundaries around permissions, data leakage and malicious content.
2 Security Vulnerabilities in LLMs — And What Do the Avengers Have to Do With It? 🦾
Two common security vulnerabilities in large language models, why they are dangerous, and how to think about safer LLM use in your organization.
Why now Finish by connecting every part: why initiatives fail when organizations buy before understanding need, workflow, learning and adoption.
According to MIT Research, Only 5% of Companies Succeed in Achieving Transformation Through AI — Why, and What Can Be Done Differently?
MIT research shows that only 5% of artificial intelligence initiatives succeed in delivering measurable business value. Where do organizations fall short, and what practical steps can be taken?
You completed the core path. Where next?
Continue with the remaining deep dives or choose another path.
You also completed every deep dive—the entire playlist is done.