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Every company wants to have an AI strategy: A bold vision to do more with less. But theres a growing problemone that few executives want to say out loud. AI initiatives arent delivering the returns they were hoping for. In fact, many leaders now say they havent seen meaningful returns at all. IBM recently found that only 1 in 4 AI projects hit the expected ROI. And BCGs research goes further still: 75% of businesses have seen no tangible value from their AI investments. Stop buying tools your team doesnt know how to use The fix? Increase your investment in AI training to support your business transformation. The data tells a simple story. An Akkodis survey suggested only 55% of CTOs believe their executive teams have the AI fluency needed to grasp the risks and opportunities of the tech. Yet, it is these same executives who are trying to reengineer entire workflows, teams, and business models around tools that their people barely understand. And when performance disappoints, the knee-jerk reaction is to buy even more tech. More platforms. More licenses. More dashboards. But that only makes the problem worse. The teams that were struggling to learn one tool are now juggling five. Everyones overwhelmed. No ones effective. And adoption flatlines. Even if you have the most advanced tech in the world, if your team doesnt know how to use it effectively, its worthless. Expand your training budget But, equally, throwing money indiscriminately at AI education alone isnt going to fix the problem. The training investment must be smart. And that means implementing training programs that are truly pan-company and aligned with the business objectives. Too many businesses funnel their AI training into a tiny corner of their workforceusually just their IT, engineering, or data teams. And while these teams do need support, theyre not the ones who are going to deliver the productivity gains that you are trying to realize. That job falls to the rest of your company: the 90% working in frontline roles and business functions where the AI transformation will be felt most. Whether thats operations, strategy, product development, sales, finance, marketing, HR, legal, or customer service. These are the people who run your business. And if they dont know how to apply AI to their day-to-day work, your transformation will stall. If the goal is to modernize the business end to end, your training needs to reach end to end. Teach Data and AI literacy before you teach tools At the same time, surface-level AI training that focuses only on toolssuch as how to write a prompt, where to click, and how to navigate an interfacewill also fall short. Effective AI training needs to build capability and not breed dependency. The best results come when your people understand whats happening under the hood. Dont get me wrong, your team members dont all need a PhD in computer science. But they do need solid data literacy. They need to know how to interrogate, interpret, and act on data. The real value of data comes from understanding what it can actually doseeing its potential and seizing it with both hands. Without even the most basic data skills, AI will create beautiful spreadsheets that cant be acted on. And thats not the revolution anyone had in mind. Train your managers just as muchif not more Equally, when it comes to AI training, theres a myth I sometimes hear: Managers dont need AI training because they’re not doing the work. Their job is to manage the team or set the vision, not run the tools. But that logic falls apart quickly. Firstly, I can think of countless ways that AI can make managers more effective: being able to synthesise and extract lessons from performance data, providing their team with hands-on guidance on how to use AI, and spotting opportunities to reengineer workflows. But, more importantly, it is the bad message that not training your managers sends to your wider team. It runs the risk of your wider company writing off your transformation as “hot air” and “warm words” rather than concrete, in-the-trenches implementation. Wide-scale transformation needs managers who can lead by example. If you train the team but skip the managers, dont be surprised when nothing changes. Build a culture that lets people use what they learn Finally, even the best training program will fall flat if your workplace punishes people for using it. In many businesses, employees are quietly, and perhaps unconsciously, discouraged from using AI. Theres a genuine fear that if theyre seen to be using AI, they will be criticised for cutting corners or cheating. The result? Team members keep their heads down and go back to old habits. In other companies, colleagues are afraid to give AI a go in the first place. Theyre hamstrung by a fear that theyll make a mistake or get something wrong. In both cases, your training budget goes to waste. So, if you want this to work, you need to create a culture of experimentation and entrepreneurship, where trying something new is actively encouragedand not seen as a riskand where teams share learnings, trade prompts, and build real know-how together. Too many companies are pinning their hopes on the next big AI tool. But no tool, no matter how powerful, will move the needle if your people dont know how to use it. The smart move right now isnt just buying more software. Its training your people to work smarter with the tech you already have. Thats how you make AI worth the investment. Thats how you turn strategy into results. And thats what will, ultimately, stop your AI vision from dying on paper.
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In a world obsessed with productivity hacks and optimization strategies, I propose we try something radical: What if the secret to peak performance isn’t doing more, but doing differently? What if our industrial-era approach to productivity is not just outdatedbut it’s actively sabotaging our best work? We tend to think about productivity as timesomething that can be constructed and divided up into neat segments. But this view of productivity has serious limitations, especially in a knowledge economy dependent on imagination and creativity. As part of my research for my book Move. Think. Rest. I interviewed nearly 60 people and examined my own journey from academic burnout to entrepreneurial vitality. I’ve identified three game-changing insights that could transform how you and your team approach work: 1. Your Brain Needs Your Body to Think Better The most counterintuitive finding in my research? Movement isn’t a break from thinkingit’s essential to it. As human beings, we’re designed to move. Our spinal cord is an extension of the brain. If we’re hunched over a laptop all day, we’re literally constricting blood flow to the brain, and therefore oxygen to the brain. We’re simply not doing our best thinking. This isn’t just about taking a walk to clear your head. I’ve come to understand movement as a form of inquirya way of collecting different types of data through your body. When I take a ballroom dance class, or go open water swimming, I experience a different type of thinking happening through my body, a different type of data collection that I’m absorbing through the movement. I get out of my head and into my body, which paradoxically helps me think better. The practical application? Start incorporating movement into your work routine. Take phone calls while walking. Use a standing desk. Design brainstorming sessions that get people out of conference rooms and into different physical spaces. Your body isn’t separate from your mindit’s part of your thinking apparatus. 2. Replace ‘Productivity’ with ‘Cultivation’ I challenge the fundamental premise of our work culture by proposing that we shift away from asking “How can I be more productive?” to asking “What might I cultivate?” The difference is profound. This insight came from looking backward. Before the first industrial revolution, most societies were agrarian-based economies. I’m not romanticizing farming (that’s the ultimate volatile, uncertain, complex, and ambiguous environment), but the agricultural model offers a powerful framework for modern work. When we cultivate, we value both the solo practitioner and the collective. We value quick spurts of growth, but we also value slow growth. We value measuring what we can see, but we also factor in that there’s a lot going on during dormant timespercolating and marinating. If we trust the process through experience, we know that something incredible will emerge. This “both/and” model recognizes that some of our best work happens during what appears to be downtime. Like a farmer who understands that soil needs time to regenerate between seasons, effective leaders must create space for ideas to develop organically rather than forcing constant output. 3. Rest Is a Strategic Advantage, Not a Luxury Perhaps the most radical element of my framework is positioning rest as a competitive advantage. When we rest, we restore. Restoration is so important for being able to spark new questions. When we get exhausted and drained, the new ways of thinking, the new ways of asking “I wonder if I tried this?” simply won’t emerge. When you’re tired, you’re just trying to survive. This isn’t about nap pods in every office (though I’m not opposed to those). It’s about recognizing that rest operates on multiple scalesfrom micro-breaks during the workday to sabbaticals every five to seven years. The key is intentional design of rest periods that actually restore cognitive capacity. My own practice illustrates this principle. As an entrepreneur, I deliberately take dance lessons three days a week, go on micro-retreats that last a day, and ensure daily walkseven if just for five minutes. I’ve become very mindful about self-preservation and self-compassion. I noticed that when I was “procrastinating” (when I stepped away from my laptop) I would come back and all of a sudden things clicked, or I got an idea that seemed even better than before. The Bottom Line The World Economic Forum predicts that by 2027, critical thinking will be the No. 1 job skill, with creativity ranking second. My Move-Think-Rest framework isn’t just about personal wellnessit’s about building the cognitive capacity that future work demands. The companies that will thrive aren’t necessarily those with the most sophisticated AI or the fastest execution. They’ll be the ones that understand how to cultivate human creativity through the simple, profound practice of moving, thinking, and resting with intention. Creativity is the engine for innovation. If you want to consistently innovate over time without burnout, you need this ebb and flow. Movement, thought, and rest help us be more creative, which helps us to sustainably innovate. The future of work isn’t about working harderit’s about working more humanly.
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While tech and AI giants guard their knowledge graphs behind proprietary walls, a more open model is quietly powering innovative projects from So Paulo to Nairobi. Wikidata, the collaborative backbone behind Wikipedia’s structured data, has become the world’s largest free knowledge database. Lydia Pintscher, who leads the Wikidata project at Wikimedia Deutschland, oversees this enormous experiment in open collaboration. More than 25,000 contributors across 190 countries have built a database containing 116.6 million data points, edited nearly 500,000 times daily. Unlike with proprietary alternatives, anyone can access, query, and contribute to this growing repository of human knowledge. Developers can build upon this community-driven knowledge base without worrying about corporate gatekeepers or sudden API changes. Pintscher spoke with Fast Company about how open data challenges Big Tech dominance and enables innovation in underserved markets, and why transparency in knowledge graphs matters more than ever. The conversation has been edited for length and clarity. What are a few projects built on Wikidata that reflect technology’s potential for social good? There are many, but the ones Id like to highlight are: Govdirectory, making it easier for people to get in touch with their government and make their voices heard on topics that matter to them OpenSanctions, tracking politically exposed persons, their connections, and the sanctions imposed on them, ensuring that international sanctions are enforced Aletheiafact, a fact-checking project from Brazil combating misinformation Open Parliament TV, making it easier to track what politicians are saying in parliament about crucial issues Gestapo.Terror.Orte, a project helping to understand the atrocities of the secret police in Nazi Germany All of them are grassroots efforts, made possible or easier with the support of Wikidatas data and community. When developers could use proprietary APIs from major tech companies, why choose the more complex path of building on open data? Ill answer that question with another question: Do you want to be beholden to the whims of a major tech company that could decide tomorrow to no longer make the data available to you, or only make it available to you at a price, and under conditions you cannot agree to? Or would you rather work with and support a movement that cares deeply about access to knowledge for everyone? On top of that, Wikidata empowers you to be an active participant, not just a consumer. You found an issue in the data? Something you really care about is missing? You can go and make the changes in Wikidata yourself, directly. How does Wikidata’s approach differ from how companies like Google or Microsoft manage their knowledge graphs? The starkest contrast is the openness. In Wikidata you can literally go to the website, look up an entry, and sift through every single change that has ever been made to that entry to see how it got to where it is today. And beyond just being able to see what that entry looks like now or looked like in the past, you can also make an edit to it and contribute to the sum of all human knowledge. Right there. With one edit. The second difference is the complexity and nuance with which we try to model the world. Since the beginning of Wikidata I have found so many beautiful, weird, and thought-provoking entities that really dont lend themselves to a simple model of the world. Did you know about that one year Sweden decided to have a February 30th, for example? Or all the countries that have more than one capital city? There are plenty of funny examples but also ones that really matter, such as disputed territories where other websites might decide to show you just one side of the dispute depending on where you access their site from. We cant have civil conversations when we dont even get shown that another view on a topic exists. Thats why I believe it is so important to surface at least some of that complexity. The world we live in is complex, weird, and beautiful, and the technology we use in that world needs to be able to reflect that. What’s the most notable technical challenge you’ve solved that other organizations building global platforms should know about? Making a knowledge graph the size of Wikidata publicly accessible and queryable to everyone is definitely a technical challenge, especially given the rate of changes and access to the data. Wikidata gets edited almost 500,000 times a day. Our SPARQL endpoint serves about 10,000 requests per minute, and it is growing every day. Building and maintaining infrastructure to support that with the resources of a nonprofit is definitely a challenge. What’s your sense of how open data projects will evolve over the next few years? Large tech companies have been extracting value from the commons for many years, be that in open data or free software. As a society, we need to understand that this is undermining the commons we all rely on, and we need to expect and demand better. I believe, especially in the age of LLMs and related technologies, that we need to understand what this technology is built on, and this is often happening without giving back. So I would like to see people contribute more to open projects like Wikidata and then build on that data, all the while giving back to the project they rely on. The alternative is a world where we as a society do not have influence over the technology we use every day and that democracy depends on. Instead, wed be beholden to the black-box technology we are given. Thats not a future I wish to live in. What do you mean about LLMs not giving back? These large AI companies are basically strip-mining the internet. They will undermine the source of a lot of the material that they’re training their models on. If they’re not sending people back to projects like Wikipedia or Wikidata, or many others, they’re basically cutting them off from the people who actually make the answers possible. Are you saying the sites providing the content might disappear? So someone put out a blog post about Stack Overflow analyzing how large language models influenced the traffic on their site. And the analysis suggested that if people are just asking their programming questions to an LLM, why would they need to go to Stack Overflow anymore, right? But why is the LLM able to answer programming questions? Because it has been trained on something like Stack Overflow. So what should AI companies do to ensure the vitality of the communities they’re taking material from? Two things. One is recognition in the sense of “Hey, this answer you’re getting here is coming from these places,” and they’re starting to do that, so that people can find their way back to the source of that content. Andthe second is that they’re making a lot of money, and they should give some of that money back to the projects that are making them that money. How do you handle conflicts when contributors from different countries or just different perspectives disagree about how to structure or present information? There are community processes to handle editorial disputes, starting with discussing the pros and cons of different ways of describing a situation (in what is called a WikiProject) together with people interested in the same topic. Often, more senior editors can help resolve disagreements that way, for example, by pointing to best practices for modeling or by asking for references for a specific data point someone wants to add. Worst case an entry might get locked down by an admin if different parties cant stop editing back and forth on a particular point. Many potentially divisive topics thankfully never even escalate to that level, in part because of how Wikibase, the underlying software of Wikidata, is built. Based on many years of experience in Wikidatas sister project Wikipedia, from the start we centered it around the concept of verifiability. That means an editor cannot just show up and claim something. They need to have a reliable and trustworthy source for what they claim, such as an article in a reputable newspaper. Additionally, we allow differing views and even conflicting claims to stand side by side, something especially important for disputed territories, for example, and then add context to these claims that helps [explain] the nuance of the situation. This can include things such as which international body supports or does not support a specific territorial claim. Your 25,000 contributors span 190-plus countries. How do you ensure voices from marginalized communities aren’t drowned out by more resourced contributors? We are dedicating a lot of effort to ensuring that everyone can contribute data that is relevant to them and their communities. For example, we are running editing workshops across Africa to help more people make their first steps in contributing to Wikidata. We are also working on improvements to editing from mobile devices to make sure people who primarily or even exclusively access Wikidata from a mobile phone have a good experience contributing to the worlds knowledge. What has surprised you most about how developers worldwide have used Wikidata’s open data? What astonishes me the most is the fact that many of the applications people are building with the help of Wikidata are ones that I would never have imagined when we first started. Take KDE Itinerary, for example, the digital travel assistant that keeps track of all your travel documents andthanks to Wikidatareminds you to bring an adapter for your laptop when traveling to a country with different power outlets. Or eRutter, the historical sea-routing website that lets you imagine how you might have traveled from continent to continent in ancient times. A Bangladeshi developer with Wikidata can access the same data infrastructure as Google. How does open data level the playing field for innovation in the Global South? A lot of applications today are powered by data. As a developer, that means you dont just have to actually build your application, you also have to collect and maintain the data your application relies on. For a large company, that is not as big of a problem, but if you are an individual developer or small team, this really limits what you are able to build. This is where Wikidata is there to support you, with basic data about the things that matter in the world, from people to events to locations to culture, you name it. Thanks to a dedicated community of over 25,000 editors on Wikidata, you have access to up-to-date and reliable basic data to build upon. And not just that: Wikidata also provides you with links to 10,000 other websites, archives, social media sites, and more to make it easier to access additional data about the topics you need for your application.
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