This is what we set out to do with Offbeat Fest, our flagship event, from day 1. Attending conferences all around Europe for the past 5 years, I’ve learned that the best part was connecting with people. New people. People I’ve known from the internet. People from all corners of the world. People in different companies. I always left conversations inspired, curious, and a bit more courageous.

At the same time, most conferences aren’t designed with connection in mind. So in 2024, we started wondering whether there’s a different way to run a conference. And Offbeat Fest was born.

For the past 2 years, we’ve been running Offbeat Fest in London. If you’re curious how it went, watch this video or read this document to see what went into putting it together.

Although at first we were a bit shy, having seen it in action and having heard the amazing feedback people shared, we’re more sure than ever that this is the future of conferences. Smaller groups. More space for participants to talk to each other. Fewer sponsors. Reasonable prices.

On September 15, our tickets for Offbeat Fest London will get a bit more expensive. Non-fellows will purchase them at €699, instead of €499. Fellows will purchase them at €499, instead of €399.

We have only 2 tickets left at super-early bird pricing, and if those are gone before September 15, prices will go up even earlier.

Get your ticket

We’re looking forward to welcoming you in London on June 7-8, 2027 at the same Big Penny Social, which by now feels like home.

Offbeat Fest is coming to Bucharest (as well) in 2027

L&D professionals in South-Eastern Europe don’t have many dedicated conferences they can attend. So we’re really excited to announce that Offbeat Fest will also be held in Bucharest in 2027.

We’ll stick to the same format, and the official language of the event will be English. If needed, we’ll make sure to accommodate Romanian speakers if they don’t feel fully comfortable expressing themselves in English.

A few peers will walk on stage to present case studies. Aki Friedrick, Lisa Wetter, Anamaria Dorgo, and Maya Camarasu have already agreed to join us.

And most of the time will be spent in conversations facilitated by experts. We’ll talk about what it takes to become a strategic L&D team, AI adoption, measurement, and leadership development, among others.

Surprises are also to be expected.

Offbeat Fest Bucharest will be hosted on October 4-5, 2027, at the beautiful Deschis Gastrobar. We’ll enjoy an amazing learning experience and hopefully some pretty sunsets as well.

Join the waitlist

Registration will open on September 15, and early-bird tickets will be available until November 15 for €199. For now, join the waitlist to be the first to learn when we launch.

We’re living in times when qualitative live experiences are very much needed. So the dream is big. We already did extensive research this summer on what it would mean to bring Offbeat Fest to the US. While this won’t happen in the next 2 years, we know it will happen at some point. APAC is also on the list. And who knows where else we’ll go? You’ll have to follow our journey to find out!

Meanwhile, we hope to see you either in London, Bucharest, or, if you’re ambitious, in both! 💜

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When we made the move to Substack, we also decided to make it a space where more peers can share their knowledge. So this week, Al Dea, Founder of The Edge of Work, jumped in to share his ideas about applying the scientific method to career development. I loved his thoughts, and I hope you will too. Enjoy! Lavinia

When I entered the workplace 2 decades ago, I expected that career development was going to work the way that school had: linear, sequential, and predictable. After freshman year is sophomore year; you want to always get the highest grades, and someone will always tell you what to do next. I quickly learned that the corporate world doesn’t work like that. I went looking for best practices and tried everything under the sun. Career plans, SMART goals, the 10-year plan, the career roadmap you build and work backward from. Each has merit, but they didn’t work for me.

I started my career at a management consulting firm, where the structure of the work was project-based, meaning I had different projects and after one project, I would go to the next one. This meant that every new client engagement was essentially an experiment. This was true in my early years, when I didn’t have much work experience to draw from yet. And that framing did something important, in that it gave me permission to accept that no project had to be perfect, so as long as I was willing to give it a try, immerse myself in it, and have it teach me something that I could carry to the next one.

That reminded me of the scientific method that I learned from high school science class. To jog your memory, the scientific method is a way that researchers construct a hypothesis, test it through an experiment, and draw a conclusion about an idea related to their field. I took this approach and started applying it, first to my client work, and then to how I was managing my career.

Instead of trying to build a roadmap, or trying to self-reflect my way into a 5-year plan, I could just run a career experiment: a small test of a specific hypothesis about an interest, skill, path, or opportunity that I wanted to explore. Instead of trying to map the future, I was testing out thoughts like: “I think I might enjoy this kind of role,” “This skill seems important for me to build; let me find out,” or “I really enjoy this kind of work, let’s try to get more of it and see what happens.”

Over time, I formalized that thinking into a framework that I teach to leaders called Career Experimentation. Career Experimentation is a dynamic, curiosity-driven approach to how to manage your career in a complex, changing, and iterative workplace. Given everything that is going on in the workplace right now, I think that career experimentation was meant for this moment. I know that I’m not alone in this: Offbeat friend, and NY-Times Best Selling Author- Anne-Laure Le Cunff and her work around Tiny Experiments makes a beautiful case for this approach for many things in life, not just careers.

Here’s what this looks like in practice:

Step 1: Formulate a Hypothesis. It starts with a hypothesis. Not a plan, just a guess about an interest, curiosity, or thing you want to test. You don’t need proof, just the smallest thing you can test.

Step 2: Test. Then comes a test. A small, cheap, low-stakes action and experiment that provides insight. It could be a low-stakes curiosity conversation, a side-project this quarter with another team, or shadowing a colleague once a month. The point is to gather real insight rather than relying on guesswork through experimentation.

Step 3: Learning. Take the time to understand and document what you learned, process your experiment, identify what you learned from it, and how it informs what you want to do next

Step 4: Action. Based on what you learned, what’s the next step? Sometimes, it means going further with another similar experiment. Sometimes, it means stopping because you learned all you need to know. Sometimes it means running an adjacent experiment. The key is the follow-through.

In my speaking and advising work, I spend a lot of time thinking about the idea of the liminal space - that in-between period where the old way of doing things hasn’t fully ended, but the new way hasn’t solidified as well. Right now, it feels like every part of the workplace is in a liminal space, including how we think about managing our careers. On one side is the past, which is legible and clear, but also becoming less relevant by the day. And on the other side is the future, which we can sort of see, but feels very unclear. This gap makes it hard to know how to act, what action to take, or what to anchor decisions to. This is exactly the moment where career experiments become relevant.

One of the pieces of feedback I get around Career Experiments is about what isn’t needed. You don’t need a stable ladder or career leveling guide. You don’t need a leader or manager to tap you on the shoulder or build you a plan (although I encourage all leaders to support their employees to run career experiments). You just need the willingness to tap into your curiosities, run small tests, and use those learnings to take action.

Einstein is credited with saying, “You cannot use an old map to explore a new world.” I think about that line a lot when it comes to how we manage our careers. We keep pulling out the same map when we talk about career development, wondering why it doesn’t seem to get us anywhere. It’s not that we’re reading the map wrong. It’s that the world it was drawn for doesn’t really exist anymore.

I think this is the opportunity that is hiding in the uncertainty. We don’t have to keep indexing to that map that’s no longer helping us. We can treat this liminal space for what it is - An opportunity to check our priors about careers, and to build a model for that that is more honest, more human, and more dynamic to what suits us today.

In a constantly changing and evolving world of work, maybe the honest thing is to admit that we don’t have the map anymore. Instead, we just have to create the next experiment worth running.

3 L&D Resources Worth Exploring

How to figure out your next career move

Cliff Maxwell (with Bobby Moesta, of Jobs to Be Done) turned 1,000+ interviews with people changing jobs into a six-question process for figuring out your next move. The part worth stealing: the “energy profile”. Audit a month of your calendar, mark what energised you and what drained you, then do the same for your last two roles. It’s an interesting career conversation starter you can run for yourself or your development programs.

The Coaching Loop: How Great Managers Leverage One-to-Ones

Most manager training teaches coaching as a skill set. This piece teaches it as a meeting. The arc is Explore, Focus, Commit, Follow up, and the whole thing hinges on the last step: opening every one-to-one with “Last time you said you’d do X, how did that go?” There’s a five-item starter list for new managers at the end that’s worth considering if you’re onboarding new managers any time soon.

16 ways to evaluate the impact of learning

In a pile of AI Slop on LinkedIn, Matt is for me a beacon of light at the end of a very shitty tunnel. In this particular post, he lays out 4 categories of metrics: changes in knowledge, self-rated behavior change, other-rated behavior change, and organizational results. If you’re deep into “How can we improve the way we measure our impact?” you might find this very interesting.

The AI Find: What leads to AI Slop?

The complaint about AI Slop has intensified quite a lot over the past couple of years. When it’s not about the fact that everybody’s using vibe coding to create all sorts of apps, it’s about who writes and doesn’t write with AI, or visual assets and decks created with AI but never reviewed by the human who created them.

This is a problem in many organizations, as well as L&D teams, so I think it’s important to discuss it publicly. I’m not here to tell you what’s AI Slop and what’s not. What I wanted to explore is what leads to AI Slop being created in the first place.

I spent this entire week reflecting on this, which also led me to create a rather complicated diagram. I tried capturing how AI companies are influencing this, how companies using AI tools can create environments where AI Slop is accepted (if not encouraged), and how each of us, individually, can let it slip and actually create more of it. To see the details of the diagram, don’t hesitate to open it in a new tab.

I wanted to spend a bit of time on a few of the chains in the diagram I find important and also under a company’s control.

1. Many companies exercise a lot of pressure over AI adoption, without considering the way that pressure can backfire. If the company is in a hurry, it rarely considers reviewing the output of an AI tool. So people end up exercising less critical thinking themselves, reducing the quality of their individual interactions with available AI tools and their prompts. This chain leads to worse AI output, hence more slop.

Action: One action would be for companies to implement review processes for AI output. I’m not an expert, but I’m pretty sure this can be done in a human-AI partnership. The important thing is for more than one “eye” to look over the produced output, and ideally one of the eyes should be human.

2. Another way in which pressure to adopt AI can backfire is when a company doesn’t have the time or the desire to create standards for what even counts as AI Slop. Without any shared standards, it’s harder for people to even recognize when AI Slop slips into everyday work. People can’t offer feedback on what they can’t agree on, and even if they do, when people hold different opinions, feedback is simply subjective and holds no shared meaning. And if nobody tells me, “Hey, it’s not acceptable to create a deck with AI and not review it closely before you send it to the team”, there’s a higher chance that I will do it again, creating more AI Slop.

Action: Create shared standards for what type of AI output is good enough and put it in a Claude Skill, for example. Standards alone won’t lead to more feedback, but it’s a starting point.

3. Companies usually drown in documentation. This doesn’t mean its quality is high. Some documents are outdated, or some have failed to capture tacit human knowledge. We all know by now how important context is in producing a high-quality AI output. But when the documentation that should provide part of that context is subpar, the quality of the output is lower; hence, more AI Slop.

Action: Creating filters for what company documentation you allow into your models is obviously extremely important, but also something I feel is a temporary problem. AI jumping into meetings, reading emails and Slack/ Teams messages, keeping itself up to date by default, and capturing tacit knowledge will improve its memory. How ethical that is, that’s another problem.

4. As AI use cases expanded and the technology evolved, it opened up doors for everyone. You can now create apps with no engineering background. You can write about all sorts of topics. You can create decent visual assets with no design expertise. Lowering the cost of producing an asset also led to companies increasing their expectations around what and how much humans can produce. Focusing on speed and volume reduces time for critical thinking and increases AI Slop.

Action: The cost of producing an asset will only go lower from here. That’s not where we should intervene. Focusing less on how much we create versus why we create might be the most important question we need to answer this century.

5. In direct correlation with the democratization of different capabilities like engineering and design, is human expertise. While I can now create a product in Lovable, it doesn’t mean I will create a good product, a product that’s actually useful, and people might want to use. The level of human expertise combined with prompting skills can increase or decrease the quality of AI output, and how much Slop we let out into the world.

Action: For now, continuing to invest in human expertise is important. I hope that will always be the case, although our definition of expertise might change in the future. Every company having a realistic view on what AI can do and what humans should oversee is extremely important.

This is my first draft. It’s a conversation I wanted to open up, to see how other people define AI Slop, what else leads to it, and even if it is as big of a problem as I’m perceiving it.

Looking forward to hearing your thoughts!

Surprise of the week

Each week, Offbeat brings you a new random surprise. Sometimes it might be big － a challenge you can join to win something nice. And sometimes it might be small, like a video or a nice Instagram account. Curious? Hit open!

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We’ve officially started our second round of Offbeat Sparks this year with Seattle, and we’re continuing with Iasi, where registrations are officially open here. Big thanks to D’Jeanne, Armina, and Victoria, Slalom, speakers, and participants for the second Offbeat Sparks in Seattle! Good luck to Andreea and all the Iasi speakers for September 17th!

A few weeks ago, on a hot Wednesday morning, I joined a group of over 70 L&D professionals from Romania to discuss the connection between learning functions and business impact. Many interesting ideas were shared, and today I wanted to report one of them.

Iulia Buciuman came in after 4 other speakers, myself included, to ask the ultimate question: how strategic is your L&D function?

The question was already interesting enough, but Iulia went on and presented a diagnostic she designed and used with some of her clients. The presentation was in Romanian, but I’ll do my best to share its main ideas here.

Iulia defined a strategic L&D team as one that “starts with business needs and priorities, shapes which capabilities the organization builds, designs for transfer into real work, and proves learning’s contribution to performance.”

She then went on to present a set of 20 tactics any L&D team can score from 1 (this doesn’t happen) to 4 (it’s a systemic practice that can be proven) to start diagnosing how strategic they are. I won’t go in depth with the tool, but I wanted to share its 6 categories, and 20 tactics:

1/ Connection with the business strategy

– L&D knows and understands the business strategy

– L&D engages directly with the leaders who own business outcomes

– Every significant initiative begins with a real business need

– L&D identifies the future capabilities the organization needs

2/ Diagnosis and prioritization

– There is a real diagnostic process before a solution is chosen.

– L&D checks whether the problem can be solved through learning.

– Investments are prioritized based on impact.

– L&D explicitly decides what it will not invest in.

3/ Influence and partnership with the business

– L&D doesn’t automatically accept the solution the business proposes.

– L&D takes part in conversations about change, performance, and the future of work.

– L&D presents a strategy, not just a course calendar.

– Learning is integrated with the other organizational systems.

4/ Learning design and transfer into real work

– Every program defines observable behaviors.

– Programs use real work problems and situations.

– The direct manager is involved in learning transfer.

– Transfer is designed before the program is delivered.

5/ Vendors, technology, and use of resources

– Vendors are selected on strategic criteria.

– Technology and AI are used to free up strategic time.

6/ Measurement and accountability

– Metrics are defined before the program launches.

– L&D can demonstrate what changed.

Depending on your own answers, your L&D function might be anywhere from Course Admin (just administers training), Training Provider (designs or works with suppliers to deliver learning programs), Business Partner (is involved in diagnosing the gap and figuring out the best solution to close it), or Strategic Function (is involved in deciding the capabilities the business needs, plans for solutions, and measures performance).

There are a few interesting things about this diagnostic. Firstly, it forces an L&D team to agree on a definition of what strategic work requires. Secondly, when starting to think of shifting an L&D team from order-taker to strategic partner, it clarifies the most pressing issues that need to be addressed first. And finally, it can be taken in time to see if the needle has truly shifted.

It also reminded me of the more complex L&D Maturity Model developed by David James and 360 Learning. Wherever you decide to start, as an L&D Manager, you shouldn’t start alone. Bring your entire team together to think things through, learn, and make decisions as a unit.

Good luck!

3 L&D Resources Worth Exploring

Building a Culture of Curiosity. If you’re making a case for building curiosity in your organization, this toolkit might come in handy. Scott Shigeoka argues that curiosity shouldn’t be just a soft-skill training, but should be treated as an organizational initiative. He put together five areas where curiosity shows up (creativity, bridging difference, wellbeing, belonging, and emerging technology), each one with a real case study, a short list of practices, and reflection questions you can use however you want.

Mind the Belief Gap. Leaders know what good leadership looks like, yet they still don’t align their behaviors to their knowledge. Why so? A study conducted by BCG found that the gap might come from some limiting beliefs. For example, 41% rank empathy in their top three qualities; only 16% say their leaders are good at it. The belief between knowledge and action is that empathy erodes accountability. Our leadership development programs can surface those beliefs by asking leaders to finish sentences like “We’re doing this because we believe…” and showing them the consequences of their actions.

The case for outgrowing your user manual. I remember being really excited by the idea of having a user manual. This piece challenges a bit how we’re using these manuals, and proposes keeping it, but as one block inside Tim Clark’s Work Model Canvas, which maps your whole working life and counts stress and damaged health in the cost column alongside your hours. Worth reading if you’re rethinking onboarding or career conversations.

The AI Find: The CRISP Framework

I started the 100School 15 Days of AI course this week, and have been enjoying it quite a bit. Every day, I receive a lesson with very little theory and a lot of practice that I can get done in less than an hour. Having just come back from my holiday, I had to stack 3 lessons in one day, which wasn’t ideal, but in a normal week, I could’ve gone through all of them daily, as I should have.

Anyway, the course starts with the basics of prompting, feeding your LLM of choice with context about yourself and building personas to get diverse perspectives on the same piece of work. Overall, it was a fantastic reminder of how my prompting determines the results I get from Claude, ChatGPT, Gemini, or any other LLM. You might have heard of the principle as Garbage-in-Garbage-out.

One of the tools the course shares to counter Garbage-in-Garbage-out is called CRISP: Context, Role, Instructions, Style, and Parameters.

While it sounds very 2024, since I felt this to be a good reminder for myself, I thought it might be a good reminder for you as well. I don’t know about you, but I still have a tendency of using AI like it’s Google because I feel like I’m always in a hurry, and then get frustrated by the results, and spend a lot of time afterward giving Claude (my LLM of choice) the context I should’ve given it from the beginning.

As much as we’re trying to make AI this wizard that can do a shit-ton for us, in reality, its results are still dependent on our prompting skills. That’s maybe one of the constants of the past few years in a very chaotic AI world. While models have improved, good prompting still hasn’t decayed as a skill. So here’s a detailed overview of CRISP, together with an example.

– Context: What is the situation? Who is involved? What does AI need to understand before helping?

Example: “I’m an L&D business partner at a 400-person fintech. A sales director has asked for “negotiation training for the whole team” after a bad quarter. I’ve run six 30-minute conversations with four sales reps, one manager, and one enablement lead, and have raw notes. I need to send the director a short diagnostic before we commit the budget.”

– Role: What kind of expert should AI be? What perspective do you need?

Example: “You are an experienced L&D partner who is sceptical of training as a default solution. You separate skill gaps from system problems, and you say so plainly without being difficult about it.”

– Instructions: What should AI actually do? Draft, rewrite, summarise, critique, plan, compare, or decide?

Example: “Read my notes and produce: (1) what people actually said, grouped into themes, (2) which themes point to a skill gap and which point to something else, like process, incentives, tooling, management, (3) a recommendation with one training and one non-training component, (4) two things I still don’t know and should check before we spend anything.”

– Style: What format, tone, length, examples, sections, or constraints should AI follow?

Example: “Direct, collegial. Written for a sales director, not an L&D audience, so avoid L&D terminology. Quote the reps’ own words where they make the point better than a summary would. Short paragraphs.”

– Parameters: What should AI avoid? What must it include? What limits should it stay within?

Example: “Under 400 words. Don’t recommend a solution the notes don’t support. If a theme only came up once, label it as such rather than presenting it as a pattern. Flag where my sample is skewed.”

There’s another reason why I thought this to be a good reminder. AI has been here for a while, and everyone and their mom talks about it. We might have been using it for years at this point. This doesn’t always mean we’ve had the time to polish our skills properly. In fact, talking with more and more people makes me realize there’s still a lot of room for improvement. So regardless of whether you’re a heavy AI user or just starting out, a reminder is always helpful! I know it’s been for me.

P.S. This is not a paid ad for 100school and their 15 Days of AI course. They’ve been kind enough to enroll me for free to test it out, and I want to share with you what I’m learning in the process. If you decide this would be an interesting course for you and your company, please do your due diligence to figure out whether this is a good choice for your context.

Surprise of the week

Each week, Offbeat brings you a new random surprise. Sometimes it might be big － a challenge you can join to win something nice. And sometimes it might be small, like a video or a nice Instagram account. Curious? Hit open!

Share

Before jumping into the newsletter, quick announcement! Thanks to D’Jeanne, Armina, and Victoria, our community is gathering in Seattle for an Offbeat Sparks on August 20th! If you’re in Seattle or know someone who is, check the registration page right here. If you’re in another city and want to host an Offbeat Sparks, let us know here. Now enjoy Offbeat!

Tomorrow, I’m turning 32. So it’s that time of year when I’m pondering my life, reflecting a lot, being amazed by how fast time flies, yada, yada, yada. One of the things I always do around this date is thinking about all the things I’m learning about learning. I’m not talking only about the lessons I take from books, talks, or scientific papers. I’m talking about what I’m learning about learning from learning as much as I can, every year.

I allow myself the grace to change my mind, and I invite you to challenge me. It turns out it’s one of the ways I learn best. So feel free to comment with your own thoughts and lessons learned! Now, let’s dig in.

In no particular order, here are 32 lessons learned about learning in my 32 years:

1. Sometimes learning comes first, curiosity comes second.

2. Information access doesn’t equal knowledge acquisition.

3. No amount of reading helped me learn as much as practice.

4. No amount of practice helped me learn as much as practice + reflection.

5. Immersion into a topic (through communities, books) is a useful way of building understanding.

6. Learning can happen in unexpected places.

7. Learning alone is fun, learning with others can be fun(er).

8. Our beliefs about the world can shape how much we understand the nuances of a subject.

9. Tapping into two apparently different topics can bring up unexpected insights.

10. Extreme curiosity can become overwhelming.

11. Learning should sometimes be fun.

12. But learning should also be challenging sometimes.

13. Learning can be rational, but learning is also emotional.

14. To learn some topics you might require discipline, to learn others you might require playfulness.

15. You can’t force learning onto people, especially self-learning.

16. Different cultures can bring you beautiful perspectives about learning.

17. Every person, book, experience can teach you something. It’s all up to you.

18. We’re all learning something. Nobody has everything figured out.

19. “Thinking you know everything is the end of progress,” Brian Cox.

20. Trying something new is hard. Extend grace to everyone who’s brave enough to do so.

21. Rationally knowing failure is okay is not the same as dealing with it emotionally.

22. Learning requires breaks.

23. Changing environments can be beneficial for learning.

24. Learning new habits is hard. Getting rid of old ones is equally hard.

25. Changing your mind is a sign of learning. It doesn’t mean it’s easy or pleasing.

26. Technology can support learning, but can’t force one to learn.

27. Mental health is important to learning.

28. Sport is important to learning.

29. Sleep is important to learning.

30. Nutrition is important to learning.

31. Most organizations aren’t designed to learn. They’re designed to make money. Those where learning is truly prioritized are the true unicorns to me.

32. Organizations don’t require just individual learning, but also systemic change.

I look forward to hearing your lessons learned about learning.

3 L&D Resources Worth Exploring

Your team doesn’t need an all-knowing leader. They need a transparent learner. Short read from Brian Glaser, Google’s Chief Learning Officer, on three shifts leaders need to make right now. From expert to sense-maker, from building trust through certainty to building it through navigating uncertainty openly, and from process manager to organizational architect. His warning about layering AI tools onto legacy workflows is one we should probably repeat more often in L&D. Each shift comes with something to try this week, which makes it easy to lift into a leadership session.

What if you’re not supposed to have a long-term plan? Molly Graham makes the case for emergence as an alternative to the career ladder. The idea comes from nature, where starlings follow three simple local rules and a murmuration appears without anyone choreographing it. Applied to careers, it means getting clear on your own principles, following your energy, and trusting the pattern to reveal itself rather than optimizing toward a destination. She collects the guiding principles of people like Claire Hughes Johnson, Adam Grant, and Patty Stonesifer, and shares two exercises for finding your own, including scoring the energy of every 30-minute block of your week. Relevant for anyone doing career conversations or coaching.

Building expertise in the age of AI: Who trains the next generation? The routine tasks we automated away were also the tasks juniors learned from, and a third of employers already report that AI has reduced the foundational work early-career people build judgment on. This piece lays out a four-part response covering knowledge management, role design, learning in the flow of work, and manager coaching.

The AI Find: 10 ways to get senior leaders to use AI

One of the lessons we learned in our L&D x AI Week a few months ago was that activating senior leadership is important to drive AI adoption. While most leaders rely only on telling people to use AI, those who choose to use AI themselves seem to be coming across as more trustworthy and inspiring. So the hard question is — how do we get leaders to use AI more?

Using AI requires a lot of experimentation, playing around with different tools, encountering errors, fixing them, failing, and learning in the process. But given the nature of their roles, senior leaders tend to be removed from the day-to-day messiness of thinking about detailed workflows, fixing bugs, and hands-on work. Additionally, senior leaders are used to coming across as know-it-alls, a posture that doesn’t pair well with experimentation. So getting this target audience to actually use AI is not an easy task.

In the L&D x AI Week event, we already got some advice from L&Ds who are already pushing through the barriers:

• Private 1:1 coaching. Several teams invested in coaching individual executives away from the crowd. The turning point was almost always value. The moment a leader built their own “thought partner” or got an AI-generated daily brief of their inbox and open action items, that was when “it opened their eyes.”

• Tiered programs by seniority. One company designed distinct tracks: an exercise for the executive committee on rebuilding the company from scratch if AI disrupted the model, a track for VPs on how to set and lead a strategy, and one for managers on how to enable their teams to adopt it. Different levels of complexity, same direction.

• Space to share their own learning, not their results. One CEO openly shared what they’d tried over a weekend. Another company explicitly asked its executive team to model being on a learning journey, with the message “we’re also just learning here,” rather than performing mastery. At this stage the goal is establishing a learning culture, not showing off outcomes.

Our friends at 100school, led by Max Haining, jumped in with more lessons they’ve learned from the work they’re doing with clients. Here’s what they’ve got to say:

• Put leaders in the same programs as everyone else. On one of our enterprise programs (BCG’s Legal & Risk function), directors and senior managers did the exact same 15-day program as their teams and shared their progress in the same Slack channels. No exec track, no watered-down version. Leadership participation was one of the things that created momentum for everyone else — a director posting their day-4 experiment is the loudest permission signal in the company.

• Let leaders be beginners in public. The biggest blocker we see isn’t capability, it’s fear — of looking silly, of putting in the wrong data, of getting it wrong. A leader sharing “I tried this, it was rubbish, here’s what worked on the second attempt” does more to unlock experimentation than any polished demo. Vulnerability travels further than proficiency.

• Hook them through their own Tuesday, not the strategy deck. Leaders don’t adopt AI because transformation matters; they adopt when it takes an hour off something they personally resent — board prep, meeting synthesis, first drafts. Find each leader’s one real task, get AI into it, and usage sustains itself. Evangelism doesn’t survive a busy week; self-interest does.

• Walking the talk includes protecting the time. At Ocado, visible senior involvement was a participation nudge — but the bigger move was making managers responsible for protecting time for people to actually do the learning. A mandate without protected time quietly signals it doesn’t really matter. Less “telling everyone AI is important”, more making space for it.

• The questions leaders ask get copied. When a leader asks “Did we try AI on this?” in an ordinary meeting — not an AI meeting — it does more than any town-hall announcement. What leadership is curious about becomes what the organization practices.

• Anecdotes beat dashboards. Senior leaders repeating one specific, personal win (”this saved me two hours on Thursday, here’s how”) move an organization more than an adoption dashboard ever will. Stories are the unit of culture change; give leaders one of their own to tell.

• And the anti-pattern: don’t appoint one “AI person” and outsource the transformation to them. We see this constantly — AI adoption dumped on a single People/L&D/AI hire with an impossible remit while leadership watches from a distance. Walking the talk means the opposite: distributed ownership, leaders included. If the whole transformation can be delegated to one job title, it isn’t a transformation.

We hope these 10 ideas are useful. If anything else worked for you, we’d love to hear your story! Don’t hesitate to comment below.

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On September 1st, together with Aki, Milica, and Frank, I’ll be discussing AI Adoption. We’re bringing pretty diverse perspectives to the table, but I bet we’d learn even more if you’d join us and also share yours. You can register here — 404 people already did. If you’re interested in more sessions about the intersection of L&D and AI, check out the event this session is part of: The AI-Native L&D Leader.

Learning comes up as the skill of the future everywhere you look. McKinsey defined it as self-development. The World Economic Forum created an entire category for a series of skills related to learning called “learning and growth”. And BCG spoke extensively about learning how to learn.

Now, in all honesty, even without reading all these reports, so many of us who’ve ever dealt with continuous change have seen how important learning is. Companies have experienced it firsthand in their push for AI adoption. Many gave employees access to the tools and then found that few people rushed to use them or to learn how to use them well.

So we know learning is important. That doesn’t mean learning happens at the pace life demands. That intention-to-action gap deserves attention from L&D professionals.

We know from COM-B that behavior, in this case learning, is a combination of capability, opportunity, and motivation. And the question I have for you today is “are your colleagues motivated to learn?” Of course, the question can be asked in general, to see whether your colleagues are generally motivated to learn in your organization, but also in specific situations:

– Adoption of a new technology

– Becoming a people manager for the first time

– Someone changing roles or teams

– Onboarding into a new project

– Recruiting a new employee

All these moments will require people to learn, and whether they are motivated to do so or not is not something to be dismissed with a simple yes or no answer.

The COM-B model treats motivation as a changeable characteristic and splits it into two components: reflective motivation and automatic motivation.

– Reflective motivation includes someone’s stated intentions, their beliefs about whether a behavior is worthwhile, and their sense of identity around it. If someone decides “I should learn more often” and believes it matters, that’s reflective motivation at work.

– Automatic motivation covers things like the discomfort that makes someone avoid the behavior, or the habitual pull toward a familiar routine even when a person has consciously decided to change it.

Before any of the triggers above, you can ask people a series of questions to diagnose their motivation levels:

1/ What would change for you if you spent 30 minutes a week to learn/ try [X]? -

2/ How would you prioritize spending 30 minutes learning/ trying [X] this week against other tasks?

3/ Does anything happen if you learn/ try [X], and does anything happen if you don’t?

4/ Is learning [X] part of what you think your job is now, or is it something on top of the job?

5/ How are you handling things right now without [X], and how long have you worked that way?

6/ Do you intend to spend 30 minutes learning/ trying [X] this week?

All the answers to these questions will give you an overview of whether what blocks people from learning is motivation. If it’s not, you can move on to investigating capability or opportunity.

But if it seems to be a big problem, it might be a good idea to think of ways to support people and design their environment in a way that motivates them — and running a training on motivation doesn’t count. For more diverse solutions, you can check:

– The Make It Toolkit

– The Ten Conditions for Change

– The Behaviour Change Wheel

And maybe, an important question for you is: are you motivated to learn [X]?

3 L&D Resources Worth Exploring

What It Means to Develop Leadership Instead of Leaders. Organizations spend an estimated $366 billion a year on leadership development, and most of it goes into instructor-led training that happens far away from the actual work. The Ready argues the problem starts earlier, with our definition. If leadership is a title given to a few people with authority, then development means fixing individuals. If leadership is creating the conditions for an organization to fulfill its potential, then development means changing how teams set priorities, clarify roles, and run their operating rhythm. The three worked examples near the end are the useful part, showing what a typical L&D response would be to burnout or role confusion, and what a systemic one looks like instead.

Culture Rot: Who Owns It? And Why It Can't Be Delegated. Ask most companies who owns culture, and you get a shrug, or a pointed finger toward HR. This guide from Tim Clark's team argues that handing culture to the Chief People Officer is abdication rather than delegation, and that ownership is nested, with every leader accountable for the culture inside their own span of control. It offers three signals for diagnosing a culture quickly, including what happens in the two seconds after someone disagrees, and describes the two directions cultures rot in: pathological compliance and pathological conflict.

Designing Spaces for Thinking. Stefy Bolaños starts with her nephew scrolling through The Deep Sea on her phone, completely absorbed, and asks why our learning environments so rarely feel like that. She borrows interface design principles from Peps McCrea, progressive disclosure, one action at a time, immediate feedback, consistency, and then adds three of her own from redesigning lessons at 100 School. The one that stuck with me is the question she started asking before adding any interaction: what cognitive job is this actually doing?

The AI Find: The anatomy of a good AI Tutor

One of the most common learner-facing tools I’ve been hearing about over the past couple of years is the AI Tutor. Quite a few vendors in your industry have rushed to create their own tutors, so you might be in a position to be sold one. So I took a bit of time to better understand:

• What an AI Tutor is and how it’s different from any other AI learner-facing tool

• What the anatomy of a good AI Tutor is

Let’s start with the first question. We often talk about AI Tutors, AI chatbots, AI Coaches, AI Simulators, or AI Recommendation Systems. So many freaking terms. I wouldn’t blame you for being confused. I damn sure was. Here’s how I understood the differences:

Now, a few weeks ago Carl Hendrick from The Learning Dispatch wrote a piece about what research tells us about AI and Learning. Scott H. Young also picked it up and wrote his own article, called We need guidelines for learning and AI. Having read those, I became curious about what a good AI Tutor should look like based on what research tells us, not what vendors are promising.

The anatomy of a good AI tutor

In the table above, I described a good AI tutor as an LLM “deliberately constrained by instructional design principles.” Scott Young already sketched a version of what that means. He frames good learning as a loop: see (get instruction, a worked example, something to guide you), do (practice on your own), feedback (find out how it went), and argues AI can genuinely help with the first and last step, never the middle one:

Translate that into what a tutor should actually be built to do, and you get something close to this:

• It pushes you to solve the problem first. When you hand an AI Tutor a problem you’re facing, it shouldn’t hand you back an answer, but it should nudge you to try to solve the problem yourself first.

• It doesn’t reply with answers, but questions and hints. In a study done in 2025, a group of students was split into three: a control group, a group that got an AI Tutor that answered questions directly, and one that got an AI Tutor that only gave hints. The two groups that used the AI Tutor both did well during practice. But when the time for the real exam came, the group with the AI Tutor that gave answers did worse than the control group, while the group with the AI Tutor that only gave hints showed no such drop. Just being handed answers doesn’t help.

• It should be able to calibrate itself to what you already know. Beginners and experts shouldn’t be treated the same by an AI Tutor. A beginner needs more guidance. An expert needs less of it, maybe narrower, more targeted, with room to reflect rather than being walked through fundamentals they already have.

• It’s generous with alternatives, stingy with certainty. Instead of giving you one “right” answer, a good AI Tutor should be able to surface ideas, resources, or approaches you might have missed, broadening your thinking, not narrowing it down.

• It treats itself as fallible, and makes checking a habit. A tutor that acts like it knows everything gets taken at face value. One that expects you to double-check important claims teaches you to think critically in the process.

• It flags mistakes, but doesn’t fix them. While AI can help you flag mistakes, it shouldn’t correct them, but rather push you to practice again and correct yourself.

• It sends you back to retry, without support, and gives you feedback. The conversation with an AI Tutor shouldn’t stop after one round of practice. A good Tutor will hand you another problem, let you solve it without support, and provide feedback on how that went.

• It resurfaces what you’re likely to have forgotten. New sessions with an AI Tutor shouldn’t start with a blank slate, but with old materials and problems you already got through.

Given all of the above, my conclusion is that a good AI Tutor will be built by someone who understands both the subject and how people actually learn it. Or maybe in partnership by a subject expert and a learning expert.

A couple of final thoughts:

1/ Let’s not forget that at the end of the day an AI Tutor is still a knowledge-builder. Especially in the corporate world, where practice happens mostly in the real world, we should treat it as a “first-step” support tool that can help with building understanding and knowledge retention, not as the answer to all our problems.

2/ Most of the research in the field has been done on K12 and university students, not the corporate world. So if vendors claim their products are science-backed, I would be cautious and ask for the research papers they referenced.

3/ We all know this technology is moving fast! What’s true today might be false tomorrow. This is my first attempt at clarifying what the deal with AI Tutors is, so I welcome feedback and any thoughts and experiences you may have. Drop them in the comments below.

I’m looking forward to hearing from you, as always.

Surprise of the week

Each week, Offbeat brings you a new random surprise. Sometimes it might be big － a challenge you can join to win something nice. And sometimes it might be small, like a video or a nice Instagram account. Curious? Hit open!

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A few weeks back, we brought in Ajay Jacob as a guest in the Offbeat Fellowship to explore the intersection of storytelling and L&D. I honestly walked in with a very basic understanding of how the two can be connected. Little did I know I would leave so inspired!

I haven’t given storytelling much thought before. So my starting point was very basic. I knew of it as a technique to pack things in a way that would leave people with something they’d actually remember. We could use it to have a narrative around our data, or support stakeholders in role modeling behaviors by sharing their stories in marketing or behavioral science campaigns.

Both of these uses are real. But our session brought me two more layers to storytelling that are way deeper.

Layer 1: learning is what rewrites the stories people tell themselves.

Every person in your organization runs on narrative. “I’m bad with numbers.” “I’m the one who keeps this team together.” “Managers here don’t listen.” “I’m too old to learn this tool.” These aren’t facts. They’re stories, told so many times they’ve hardened into identity.

Real learning supports people in changing the stories they tell themselves. Funnily enough, we can use storytelling to support people in changing their narratives. I did a bit of research after our session to see how this can be done, and I found a few simple techniques:

– Make the current story visible. People can’t change what they’re not aware of, so we need to design experiences that leave enough space for people to grow their self-awareness.

– Offer counter-stories from believable people. This is one of the uses of storytelling I mentioned earlier. We can find people who’ve walked similar paths to share their own stories.

– Ritualize the retelling after the change. Offering people space at the end of a journey to reflect and repeat what’s changed for them. “I used to think… but now I think…”

Another framework we explored in our session was one people usually go through when rewriting their narratives: the hero’s journey.

Layer 2: the hero’s journey is a tool for designing that rewriting.

Ajay mentioned the hero’s journey is actually a learning journey, and once you look at its stages through an L&D lens, it becomes a design map. There are three key moments in the journey: the known, the adventure or the unknown, and the return.

Act 1: the known

– The call to adventure sits before the beginning of an experience. It’s when we should make sure we tell people something that truly resonates with them (their problems, their experiences, why the world as they know it will change), while also being honest about the difficulty of the journey ahead.

– Something we didn’t explore in the session, but that I think is really important for L&D, is the refusal of the call. There are so many blockers that prevent people from accepting our call to adventure. We need to make sure we talk about them in our call to adventure or be prepared to address them when they come up.

– I’d like to think of L&D as the mentor, the supernatural assistance that comes up in the story to help people move between the known and the unknown. The Gandalf, the Hagrid of the corporate world. We’re there to support people in the known and the unknown.

– Then, we should intentionally design the crossing of the threshold, that moment when things stop being easy, because the hero encounters challenges they haven’t faced thus far. In the last issue, which you can find here, I spoke about disorienting dilemmas and why we shouldn’t shy away from pushing people out of their comfort zone.

Act 2: the unknown

In the unknown, the hero encounters trials, surprises, and even crises. I wonder, once again, how much we design for the uncomfortable and for surprises intentionally. At the end of the day, it’s what makes people remember a learning journey the most.

Also, Harry Potter had Hermione and Ron. He was rarely alone facing He Who Must Not Be Named and his army. Community is powerful, and this is a good moment to activate it.

Act 3: the return

This is the part we have the least control over. Our heroes usually come back having changed their minds about different things and equipped with new tools to do work differently or even different work. More often than we like to admit, what they find is no support to implement their ideas. I think our responsibility is to equip them with the courage to face other people’s refusals of their call to adventure, while also influencing managers to create the conditions where our heroes thrive.

So, I ask you, what’s the story you’re telling yourself about L&D? Consider Offbeat a call to adventure to change your narrative. Our journey has lots of ups and downs, but we’re in this together!

I’m looking forward to exploring these ideas in more depth, and I hope that now, so are you.

Quick Sponsor Break

This issue of Offbeat is sponsored by my friends at Sana. Most learning stacks are patchwork, not design. Sana Learn replaces the mess with one intelligent platform that connects your HRIS, comms tools, and content sources, then lets AI handle enrollments, recommendations, and analytics. Learners get a seamless experience. Leaders get real-time insight.

3 L&D Resources Worth Exploring

Has Psychological Safety lived up to the hype? Can you train people to adopt psychological safety? We’ve been trying to do that in L&D, and more often than not, I hear people being disappointed by the results. This piece argues that while psychological safety is real and worth valuing, it’s not a lever or install-able practice, but rather something that emerges as a consequence of a variety of conditions.

The Art of Leadership. A 12-part series where Dr. Ian Hallett covers twelve principles of leadership that his research found underpin superior company performance. Available so far are the art of storytelling, listening, simplicity, foresight, and disagreement.

The Facilitation Inspiration Library. If facilitation is your passion, you’ll love this. Rebecca Courtney brought together books, podcasts, tools recommendations from 200+ of the best facilitators around the world. These resources are enough for you to create a mini-master in facilitation, without any other help. Love it!

The AI Find: Anthropic launched Claude for Teachers

On July 14th, Anthropic launched Claude for Teachers. Well, Claude for some Teachers, because it’s only aimed at K-12 teachers in the US. I think this is interesting to understand, because it might also point to a potential future we can see in L&D.

Okay, so here’s how it works.

1/ Any K-12 teacher in the US can register and, once verified as an educator, gets access to Claude for Teachers free for one year (the offer runs for sign-ups until June 30, 2027).

2/ The teacher then gets access to a set of Claude skills (pre-written instructions that tell Claude how to do a task well) useful for different tasks (e.g. lesson planning). The skills were built by Anthropic in partnership with Learning Commons, with feedback from teachers, and are, according to Anthropic, grounded in learning science.

3/ The teacher also gets access to different connectors (live links that let Claude pull information from outside sources). The Learning Commons connector gives Claude access to the academic standards across all 50 states and trusted curricula (OpenSciEd for science, IM v.360 from Illustrative Mathematics for math). The ecosystem connectors plug Claude into tools teachers already use for generating assessments, analyzing recordings from classrooms, among others.

Claude for Teachers also includes Claude Code and Cowork, so teachers can hand off recurring work and focus on forming closer relationships with students and designing learning experiences that fit each of them.

Now, let me tell you why I said this is interesting for the future of L&D. Because the same set-up could be rebuilt with our own tech stack and materials. If you look at the architecture of this set-up, it has an LLM in the center that’s plugged into a bunch of things. This is what it would look like in our case:

– A content layer (competency framework, skill taxonomies, and corporate documents as curricula)

– An expertise layer (skills built the same way the teaching skills are, but focused on adult learning science)

– An agentic layer (Claude Code + Cowork: delegated and scheduled work)

The one element where the translation gets harder is performance signals. Teachers can give tests, get data about their students’ performance, and adjust accordingly. A true signal in L&D means actual performance improvement, which would require connecting the LLM to business dashboards. And we all know how hard that is to do.

Some of the questions I’m left with looking at this picture:

– While teachers have a vetted curriculum, we don’t. We rarely even measure the performance of our programs. So we don’t have much to feed into the system, other than our current materials, maybe vetted by SMEs, right?

– If the learning design craft can be codified into skills, what’s left as our distinctive contribution? The K-12 answer was “time with students.” What’s ours?

– If we truly get all these connectors and the feedback loop runs daily (assess, analyze, adapt), what happens to the annual needs analysis and the course as the unit of delivery?

If you already have such a set-up or even parts of it, I’d love to learn more about it!

Surprise of the week

Each week, Offbeat brings you a new random surprise. Sometimes it might be big － a challenge you can join to win something nice. And sometimes it might be small, like a video or a nice Instagram account. Curious? Hit open!

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Before jumping into the newsletter, quick announcement! Thanks to D’Jeanne, Armina, and Victoria, our community is gathering in Seattle for an Offbeat Sparks on August 20th! If you’re in Seattle or know someone who is, check the registration page right here. If you’re in another city and want to host an Offbeat Sparks, let us know here. Now enjoy Offbeat!

Almost none of my growth has come from staying in my lane.

I remember going to Estonia when I was 19 for a one-week Erasmus exchange. It was my first time leaving Romania and my first time flying. I was 2000 km away from home, surrounded by strangers, speaking mostly English, a language I had never used conversationally. We were also discussing leadership, a topic I was virtually unfamiliar with. Nothing about what happened there was in my comfort zone. Yet, to this day, I think of my week in Estonia as the first moment in my life when I thought life can be more than what I knew, and what led me down the path I’m on today.

This experience, and some others I’ve had that were similar in length, made me believe that for big change to happen, learning experiences need to be long and very immersive.

In a recent conversation we had, Andy Sontag challenged my notion that we need a long time to challenge people’s worldviews. Of course, more time gives us more opportunities to do so, but even short sessions can provoke mental models if designed properly. Now, looking back at my own experiences, I concluded he’s right.

Not all experiences that made me grow were as long as a week. Spending 3 days in Copenhagen for the Kaospilot’s Experience Design Program made me rethink how and why we design learning. And many 1-hour therapy sessions have shaken me to my core.

What made different experiences powerful for me wasn’t the time I spent in them, but the degree to which they made me uncomfortable and disrupted my thinking.

In his article, Designing experiences that enable behavior change, Andy talks about The Transformational Learning Theory and references Mezirow to define transformational learning as “the process of fundamental change within our current worldviews, mental models, beliefs and values”.

The model has 10 phases:

(1) Encountering a disorienting dilemma

(2) Examining your emotional reaction

(3) Assessing presently held assumptions

(4) Recognizing others also encounter disorienting dilemmas

(5) Finding a sense of agency and exploring how one can think differently

(6) Creating an action plan

(7) Gaining knowledge for the plan

(8) Trying things out

(9) Developing confidence

(10) Integrating the new perspective in one’s life

Today, I only want to unpack the first one. A disorienting dilemma is “a situation or an experience that does not fit individual preconceptions and can be perceived as confusing or jarring.” It might take people by surprise and usually elicits an emotional response that needs to be unpacked.

Others have studied similar phenomena.

– Vygotsky’s zone of proximal development suggests learning happens just beyond what you can currently do.

– Robert Bjork’s work on “desirable difficulties” showed that things that make learning feel harder in the moment (spacing, interleaving, retrieval practice) often produce better long-term retention.

Now, I wonder how much time we spend purposefully creating moments where people encounter disorienting dilemmas in the learning spaces we design in L&D? My experience says “not much”. We’re often too focused on the next framework we need to present, or the next fun exercise we have to facilitate. I know I often am.

But what my own experience says, and what theories make clear is that to change minds, we need to change how we design learning. Carefully, but surely, we need to take people out of their comfort zone.

To do so, we can take inspiration from familiar AND unusual places.

(1) Ronald Heifetz at Harvard Kennedy School teaches leadership by refusing to lead. He opens class with silence, declines to answer questions directly, and lets the group flounder. Then, he uses the group’s own frustration, in real time, as the case study about authority and leadership.

(2) Dialogue in the Dark (a museum experience run entirely in pitch black, guided by blind guides) and Dialogue in Silence deliberately strip participants of a dominant sense. Visitors report genuine disorientation and even mild fear. The discomfort produces empathy and perspective-shift that a lecture about disability never could.

(3) IDEO-style workshops often force participants to build a physical prototype in 10 minutes with pipe cleaners and cardboard, then show it to strangers for feedback to break perfectionism and attachment to ideas.

(4) We decided to switch the traditional conference space for a brewery to intentionally give Offbeat Fest a feeling of informality and humanity. Tom Goulooze recently told me a story of how he brought a design team to a museum to reconnect with their craft.

Depending on your goal, a disorienting dilemma can be created through senses, by changing spaces, by challenging hierarchy and authority notions, by adding constraints, by designing for failure, or by compressing or stretching time. The point is for it to be meaningful to the change you’re trying to create and bold enough for people to perceive it as disorientation.

So here’s my challenge to you. The next time you design a session, ask yourself:

(1) What’s a belief this group wouldn’t think to question?

(2) How might we design an experience that draws out this belief and its consequences?

Have fun with it!

3 L&D Resources Worth Exploring

The Skills Construct Problem. If you’ve ever sat through a skills platform demo, inherited a competency framework, or been asked to map learning content to a “skills cloud,” this article names the problem you’ve probably felt but struggled to articulate: leadership, resilience, SQL, and customer focus are not the same kind of thing, yet our systems treat them as if they were. Teresa Rose unpacks why tidy skills lists hide messy constructs, why more data scales the confusion rather than fixing it, and what it takes to build taxonomies that can actually support decisions about learning, mobility, and development. Essential reading before your organization bets its workforce strategy on a list of labels.

Five principles for designing brain-powered organizations. McKinsey found that 79 percent of organizations have adopted gen AI, but only 39 percent see any profit impact. What they argue is that we’re offloading so much thinking to AI that our reasoning muscles are atrophying, and automation has stripped out the easy tasks that used to give our brains a breather. An even more important conclusion for us, in L&D, is that our upskilling efforts can fail just because people are tired.

An Applied Behavioral Science Field Guide. Matt Furness shared this guide earlier this week in a LinkedIn post, and I found it very interesting. It comes from UNICEF, and it lays out what behavioral science is as well as their DEPTHS framework, a systematic process to help practitioners apply behavioral science in practice.

The AI Find: SumUp’s No-Code AI Hackathon

One of the lessons our L&D community quickly learned when supporting AI adoption is that traditional formats fall short. To start working with ChatGPT, Claude, Gemini (insert any other tool here), people need to get their hands dirty!

At SumUp, the learning and development team was quick to act on this insight. They brought in the AI Hackathon, a format that was generally used for engineers, to people outside of product and tech. The event is a two-day, no-code hackathon where colleagues from Legal, Risk, Finance, Ops, and People get together to redesign workflows and present their ideas.

The event was run once in São Paulo and later in Berlin.

Entering the experience

The experience starts online, with a session where the L&D team shared privacy guidelines and InfoSec requirements. The same session was used to form the teams, assign roles, and get people familiar with their chosen tool, while also helping them do the pre-work needed before the building actually started. For this, the L&D team shared a Miro board that was structured as a team workspace to help each group organize their project idea in a clear and consistent way during the hackathon.

Each team had its own dedicated section, including fields to define the team name and team members, followed by key project-building blocks:

• Roles & Responsibilities: to clarify who would be responsible for each part of the project.

• Problem Statement: to define the problem the team was trying to solve.

• Main Goal: to align on what success would look like.

• Stakeholders: to identify who had an interest in the project or would be impacted by the solution.

• Tools: to list which tools, platforms, or AI resources the team planned to use.

• Constraints & Risks: to capture possible limitations, dependencies, or risks.

• Workflow: to map how the solution would work from start to finish, including the main steps and tools involved.

• Milestones & Actions: to define the key next steps and activities needed to move the project forward.

Overall, the board was designed to guide teams from problem definition to solution planning, helping them structure their thinking, align responsibilities, and prepare a clear project presentation.

The two days

During the two days, the teams spent their time building. Since the event was so intense, the space included sofas, comfortable chairs, and massage stations. But people were so excited they ended up using those relaxation areas to keep on discussing their projects.

In Berlin, participants also had access to mentors. AI Experts sat together at a table among the participants. Everyone could walk to them to ask for advice whenever they felt stuck.

Exiting the experience

In São Paulo, at the end of Day 2, the entire company was invited to watch the presentations, and each of the seven teams pitched for seven minutes. In Berlin, because the number of teams was much higher, mentors acted as judges. Three winners were selected in each city for the following prize categories: Best Overall Solution, Best Use of AI, and the Audience Award.

As a follow-up, the projects were shared in the company’s Slack channel, where even senior leaders congratulated and encouraged the teams. Some workflows built during those two days are now allowed in production.

Maria, the People Development Partner who led the AI Hackathon, also created a Slack channel for each project, open to anyone in the company who wanted to follow along, so teams could keep collaborating and pushing their solutions after the event.

Formats such as training and e-learning don’t tend to drive practice and application as quickly as a technology such as AI needs. Things also move so quickly that social learning seems to be the best way to keep up, compared to formats that are more static and become outdated quickly.

What Maria and her team did at SumUp seems to be the new standard for how AI learning sessions should look. Hope you’ve enjoyed the short story, and if you want to learn more details, the full case study is available here for Offbeat Fellows.

Surprise of the week

Each week, Offbeat brings you a new random surprise. Sometimes it might be big － a challenge you can join to win something nice. And sometimes it might be small, like a video or a nice Instagram account. Curious? Hit open!