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For decades, digital transformation raised hopes of simpler work. And while many companies found complexity instead of clarity, the story isnt over. AI brings a new wave of hope and energy, and with that, a new kind of tension. Whenever I connect with business leaders, I can feel their deep optimism and sincere sense of responsibility to deliver on AI transformation. Leaders want to boost productivity and stand by their people. Theyre guiding teams through uncertainty while inspiring them to embrace change. Thats why AI transformation is a people challenge as much as a tech challenge. Org charts are shifting. Roles are evolving. And the new priority for leaders is equipping people with the skills and wisdom to adopt AI and power this transformation with confidence. Leaders can do this with a three-step playbook: 1. BUILD THE HR ORGANIZATION FOR THE FUTURE. If HR operating models dont evolve, leaders are asking their people to build the future on quicksand. AI demands new ways of working, which is why HR leaders are stepping into hybrid positions that mirror the real world of work, where talent and technology are intrinsically linked. That starts by making HR and IT true partners so they can co-create AI experiences that solve real business problems. Fifty-five percent of organizations have launched 100+ AI use cases, but only 19% are tracking how those use cases impact business goals. Siloed efforts cant scale. Thats why weve built what we call an AI Factory, a model to collect, evaluate, and prioritize use cases quickly and ethically, at scale, across the business. Employees have submitted thousands of AI use case ideas. About 100 of them have gotten past our prioritization frameworkmeaning we believe they can deliver ROI, safely and at scaleand weve prioritized about a quarter that demonstrate the most value. As technology evolves, so must the roles around it. Leaders need to imagine new roles that move HR from an administrative function to a strategic hub. Think AI orchestration designers, or AI ethics officers. These roles are tailored to the companys business needs and critical to a people-centric AI transformation. 2. ENABLE AI ACROSS THE ORGANIZATION AND RESKILL WITH URGENCY. AI is already increasing what employees can do and changing their daily tasks. To lead through change, we need to understand not just what people need to learn, but how they learn best. It starts with the concept of an AI heatmap to identify which tasks can be automated or augmented and quantify potential gains. That insight helps leaders rethink how they grow and support their teams. By using AI and data, we can map current skills, surface gaps, and design targeted, real-time development paths. Then, we need to do the hard work: Train people to know, work with, build, and lead with AI. Weve built an AI-native learning model through ServiceNow University to do just that. Our goal is to train 3 million learners by 2027. And this isnt just a nice-to-have. The skills gap is real. According to the World Economic Forums 2025 Future of Jobs report, 63% of employers see it as a major barrier to transformation. If we dont close this gap now, well never realize AIs full potential. 3. TRANSFORM THE WORKFORCE LIKE ITS YOUR FULL-TIME JOB (IT IS!). Leaders are steering through massive change. Some employees will fear the unknown. Organizations that invest in an agile, resilient workforce, one person at a time, will win the AI race. Thats why leaders need to take an X-ray of their organizationnot just charts and systems, but a deep look at the workforce structure, skills, and capacity to grow. Then, they can start closing gaps and ensuring AI is adopted in a way thats human at the core while fueling business growth. Old org charts need a rebrand. Work is more dynamic and cross-functional. And now, we have AI working alongside people. Because of this, we need to move beyond traditional, linear models of change management toward continuous, adaptive, and decentralized change readiness. This agentic AI workforce will require thoughtful planning, human wisdom, a focus on well-being, and a strong culture at the core. Thats why collaboration and orchestration are critical. If leaders get this right, they can unlock new business models and real growth. THE RESULT? VALUE Leaders who follow these steps can supercharge business results while avoiding the pitfalls that slow AI adoption. At ServiceNow, we track adoption and ROI through our AI Control Tower, a real-time measurement that creates a flywheel of value: unlock time, reinvest it, and grow faster. The opportunity is clear: Embrace AI, lead with confidence, and bring people along the journey. The organizations that thrive will help people and AI technology co-create, not just coexist. Jacqui Canney is chief people and AI enablement officer of ServiceNow.
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E-Commerce
There are a few odors from adolescence that are seared into the brains of most Americans who grew up after the 1980s: the aroma of freshly baked brick pizza in the school cafeteria, the acrid stink of a locker room, and the unmistakable scent of teen boys wearing an unforgivable amount of Axe body spray. The phenomenon of teens dousing themselves in Axe has become so ubiquitous since the brand’s founding in 1983 that over the past few years it’s inspired its own subgenre of memes (see this one and this one, for example). Now Axe has its sights set on a new generation of consumers with a redesigned spray mechanism for its signature product. To mark the occasion, on February 20 the brand announced its self-referential History of Overdoing It campaign. Axe has always been part of the cultural conversation around guys doing too much, and for years that included how our body spray was used, Dolores Assalini, head of Axe U.S., said in a press release. [Photo: Axe] At last, Axe is offering a solution. According to the Unilever-owned brand, overspraying was always a design problemand to fix it, the team has invented new spray technology to keep offensive odors at bay. The Axe bottle gets a facelift Brajin Vazquez, senior manager of DEO formats technology at Unileverand one of the minds behind the Axe redesignsays the chronic overspraying of Axes old product was influenced by a few factors of the bottles design. The formulation of the spray, combined with the design of the bottles valve and nozzle, resulted in a thick, diffused cloud of fragrance, creating that classic overpowering smell. For years, weve heard that while people liked the fragrances, Axes spray could feel too heavy or create too much of a cloud, Vazquez says. That feedback made us look closely at the delivery system itself. We realized that improving the user experience wasnt just about messaging, it required updating the spray technology. [Photo: Axe] Vazquezs team started by rethinking the products ingredients. They reduced the amount of propellant gas in the spray and added nitrogen to the mix, which, Vazquez explains, made room for a higher proportion of liquid formula and created space in the formulation to increase odor-control actives and deliver more fragrance per spray. Essentially, this means that users can spray less of the product and still get the same body-odor-masking effect. This new formulation is combined with a reengineered spraying system. The old design, Vazquez says, operated at a high pressure, which resulted in a stronger, higher-velocity spray. The new valve component mitigates the problem by keeping the sprays flow light. The bottle also features a spray insert with a nozzle opening thats 25% smaller than the old version, allowing users to apply the fragrance to more targeted areas without that dreaded cloud effect. Realistically, Axe’s retooled design probably won’t solve chronic overspraying altogetherbut at least now there are some guardrails in place for a problem that’s plagued middle school hallways for decades. [Photo: Axe]
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E-Commerce
Below, Tom Griffiths shares five key insights from his new book, The Laws of Thought: The Quest for a Mathematical Theory of the Mind. Griffiths is a professor of psychology and computer science at Princeton University and director of the Princeton Laboratory for Artificial Intelligence. Whats the big idea? How can we study something we cant see or touch? Mathematics allows us to develop rigorous theories about how minds work. It also lets us use those theories to build artificial intelligence systems. Just as physicists seek to identify Laws of Nature, cognitive scientists hope to discover the Laws of Thought. Listen to the audio version of this Book Biteread by Griffiths himselfin the Next Big Idea app. 1. The story of AI goes back hundreds of years. For many people, AI seems to have come out of nowhere. In late 2022, it suddenly became possible for anyone to have a conversation with chatbots that could draw on more knowledge than any human. Dig a little deeper and you might discover that the approach behind those chatbotsbuilding bigger and bigger artificial neural networkshad its first dramatic demonstration in 2012, when it was used to significantly improve how well computers identify images. But the story goes back much further than that. When Enlightenment thinkers, like René Descartes or Gottfried Wilhelm Leibniz, first began using mathematics to effectively describe the physical world around us, they also suggested that the same kind of approach might be used to describe the mental world inside us. Those early efforts led to the development of mathematical logic and digital computers, which in turn led to the creation of cognitive science by psychologists who used mathematical ideas to come up with new theories about the mind. Modern AI springs from that tradition: Key advances in the development of artificial neural networks came from psychologists seeking to understand how the human mind works. 2. No single piece of mathematics describes the mind. Cognitive scientists started using mathematical logic to describe thought, but after a couple of decades realized that wasnt going to work. Concepts have fuzzy edges that logic just cant capture. Artificial neural networks were developed in parallel and became much more powerful after a group of psychologists showed how they could be used to learn more complex relationships than anyone had thought possible. Continuing to scale up those neural networks takes us to modern AI. But understanding how neural networks learnand how to create systems that learn more like peoplerequires a different approach, one that uses ideas from probability theory. These three mathematical traditions intertwine to give us a more complete picture of how the mind works. 3. Crucial discoveries come from pursuing unpopular ideas. The first neural networks that could learn were built by a computer scientist who abandoned the project after deciding that, in order for them to learn anything interesting, they would have to be much larger than he considered practical. But a psychologist worked out how to make them learn better, which caused a lot of excitement about the potential of that approach. However, that same computer scientist then showed that even those neural networks had fundamental limitations, and they decreased in popularity. A decade later, some psychologists became interested in neural networks as tools for understanding human cognition, cracked the problem of how to get them to learn more complex relationships, and neural networks became popular again. Then, machine learning researchers became interested in the statistical foundations of learning, and neural networks decreased in popularity. Soon, more powerful computers and larger datasets made it possible to use neural networks to solve even more challenging problems, bringing us to the present day. This back-and-forth between disciplineswhere an unpopular idea in one discipline is picked up and improved upon by researchers in another disciplineis a nice illustration of how an interdisciplinary field like cognitive science can have a huge impact. 4. We are closer than ever to understanding the human mind. I used to tell my students that cognitive scientists have made a lot of progress in figuring out how to ask questions about the mind, but were still a long way from having answers. But now, the progress in AI over the last decade is beginning to suggest answers to some of our deepest questions about human intelligence. Mathematical frameworks like logic and probability theory are fundamental to describing the nature of thought and learning, but the abstract rules and inferences they identify need to be implemented in real human brains. Artificial neural networks give us important hints about how that might work. Putting these pieces together gets us remarkably close to fulfilling the vision that Descartes and Leibniz had centuries ago of having a mathematical framework for describing thought. 5. There are still big differences between human minds and AI. Despite all that progress, modern AI still has some important gaps. One of the biggest regards learning. If you read aloud all of the text that is used to train todays chatbots, it would take tens of thousands of years. By contrast, a human child learns to be a fluent speaker of their native language in less than 10 years. That means that theres something in human brains that is different from what is inside our AI algorithms. Figuring out what that might be is a problem that we study in my lab, and a preoccupation of many cognitive scientists. There are also interesting questions about what exactly it is that artificial neural networks are learning, and whether they represent the world in the same way as us. In some cases, they may be, but in others, we can show that they are quite different. Figuring out what AI systems know and when they are likely to succeed or fail at a task is a great opportunity to use the methods that cognitive scientists have honed by studying humans. For a long time, we have only had one species that demonstrated this kind of intelligent behavior, so having another one to study opens the door to not just understanding more about AI but understanding more about ourselves. Enjoy our full library of Book Bitesread by the authors!in the Next Big Idea app. This article originally appeared in Next Big Idea Club magazine and is reprinted with permission.
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