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For most of modern business history, accounting has been something leaders looked at periodically. Numbers were reviewed and reports arrived on a schedule (often monthly, quarterly, or at tax time). Accounting happened when there was time, not necessarily when insight was needed. Across industries, a new model is taking shape: always-on accounting. These are systems that capture financial activity continuously, organize it automatically, and surface insights in real time. While this shift is relevant everywhere, its especially visible in the rental housing market where millions of small, independently run businesses (often managed by individuals or families who balance day jobs) are adopting operating standards once associated only with large enterprises. Ive experienced this evolution from multiple sides. Ive been a renter, an investor, and the cofounder and CEO of a property management software company, and over the past decade Ive worked closely with thousands of small rental business owners. Across all of those perspectives, Ive been at the forefront of a major shift reshaping how these businesses operate: Accounting is no longer something thats checked on periodically; its something that runs continuously. FROM PERIODIC REVIEW TO CONTINUOUS AWARENESS Traditional accounting systems relied heavily on manual processes. Financial data lived across spreadsheets, folders, and disconnected tools. Capturing expenses, categorizing transactions, and reconciling accounts took hours of focused work, which meant most businesses reviewed their numbers periodically due to time constraints. As accounting technology has matured, many of the tasks that once demanded human attention now happen automatically. Transactions flow in without manual entry. Expenses are captured at the moment they occur. Data is categorized consistently in the background. The systems themselves maintain continuity. That technological shift enables a behavioral one. Instead of treating accounting as something to revisit at set intervals, business owners can operate with continuous awareness. Financial insight doesnt need to be reconstructed; its already there. Patterns surface naturally over time. Trends become clearer, not because leaders are checking more often, but because the information is always current. Weve seen similar transitions before. Cloud infrastructure replaced periodic system checks with real-time monitoring. Analytics platforms turned marketing into an ongoing feedback loop. Finance is now following the same path, moving from static snapshots to living systems. WHY RENTAL HOUSING IS LEADING THE WAY Small rental businesses are a revealing example of this shift. They operate at the intersection of entrepreneurship and long-term asset ownership, often run by people balancing full-time careers, families, and other responsibilities. Efficiency matters. Clarity matters even more. For years, accounting was the last major workflow to modernize. Rent collection became digital. Communication went mobile. But financial tracking often remained manual, fragmented, or delayed. Modern property management software systems now pull in transactions automatically, extract data from receipts, categorize expenses by property, and generate up-to-date profit-and-loss views with minimal effort. What once required hours of administrative work now happens effortlessly in real time. The result is more than cleaner records; its confidence. When financial insight is always current, owners engage with their businesses differently. They spend less time wondering if something was missed and more time understanding what the numbers are telling them. ACCOUNTING AS INFRASTRUCTURE, NOT PAPERWORK One of the most meaningful mindset shifts Ive observed is how accounting is now viewed: not as paperwork, but as infrastructure. Always-on accounting supports operations the way reliable connectivity or modern logistics do. It reduces friction, minimizes human error, and creates a single source of truth that decisions can build on. In rental housing, this has implications beyond individual businesses. Small operators collectively provide homes for millions of people. When they run with better financial visibility, theyre better positioned to maintain properties proactively, manage cash flow sustainably, and navigate economic shifts with steadiness. What looks like operational efficiency at the business level often translates into stability at the community level. A BROADER LESSON FOR SMALL BUSINESSES EVERYWHERE Whats happening in rental housing reflects a broader trend across the economy. Small businesses are increasingly adopting systems that assume continuity rather than reminders, thanks to automation technology that works without constant prompting. The best tools today dont ask owners to remember tasks or reconcile gaps later. They capture activity automatically and organize it intelligently. This shift reduces cognitive load, which is one of the most underappreciated constraints on entrepreneurship. When leaders arent mentally tracking loose ends, they have more capacity for strategy, creativity, and long-term thinking. Always-on accounting frees attention. And attention is one of the most valuable resources any business has. BETTER SYSTEMS CHANGE BEHAVIOR, NOT JUST WORKFLOWS The most interesting impact of always-on accounting isnt speed. Its behavior. When people trust their numbers, conversations change. Instead of asking whether the data is complete, they focus on what it reveals. Better-informed decisions can be made earlier with greater confidence. In rental businesses, this often means treating properties less like side projects and more like durable enterprises. In other industries, the takeaway is similar: Clarity reshapes leadership. Accounting becomes something owners engage with at a higher levelreviewing trends, comparing periods, and planning next movesrather than something they have to reconstruct under pressure. THE FUTURE OF SMALL BUSINESS ACCOUNTING IS ALWAYS-ON The future of accounting wont be defined by louder dashboards or more complex reports. It will be defined by systems that are always on, working continuously in the background. In rental housing, that means books that are always current and decisions that are always informed. In the broader business landscape, it signals a shift toward tools that support momentum instead of interrupting it. Always-on accounting represents a larger evolution in how small businesses operate: fewer fire drills, more foresight; less reconstruction, more understanding. Confidence doesnt come from having more data; it comes from knowing the data is already there when you need it. And as more businesses adopt systems built for continuity, that confidence is becoming the new standard. Ryan Barone is cofounder and CEO of RentRedi.
Category:
E-Commerce
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
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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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
Your colleagues decide in less than a minute whether your email is worth replying to. Microsofts 2025 Work Trend Index Report shows that the average employee receives 117 emails a day, and most are skimmed in under 60 seconds. In other words, if your email takes someone more than a minute to understand, theres a strong chance you wont be getting a timely response. Well-written emails dont just make you sound smarter; studies show that they also reduce misunderstandings and speed up responses. Here are five simple ways to get faster email responses, while also helping your recipient preserve mental energy and time. BREAK UP WITH THE EMAIL BRICK Long blocks of text are the enemy of attention. Research shows that visually uncluttered text (with white space and intentional spacing) is easier for busy readers to scan and digest quickly. Simply formatting your email with bullet points, bold text for important questions or updates, and short paragraphs will significantly increase your chances of getting a prompt response. Structure is just as important as length. If the email is longer than this article, consider your reader overwhelmed. DONT LEAD WITH SMALL TALK One of the biggest mistakes professionals make is burying the lead. Instead of opening with a short anecdote or unrelated small talk (Hope your week is going well), start with the purpose of your email, and ideally, the action you need. In military and executive communication, this is known as BLUF (Bottom Line Up Front). BLUF requires you to put key information, like the request or decision needed, in the first sentence or two. After you have led with the key information, you can share further details that the recipient can read if they need background context. And yes, you can still ask your coworker if they have plans for the weekend or how their dog is doing. But for the sake of everyones sanity, leave this to the end. DONT PLAY EMAIL TENNIS The back-and-forth dance of unanswered questions (When works for you?, Morning works, What time?) costs time and demands cognitive switching. One survey of modern workplace behavior found that knowledge workers spend roughly 28% of their workweek managing email, with a large portion of that time simply waiting on or chasing down replies. To reduce this, try to include all relevant details on the first send. One way to address this is, if youre proposing a meeting, include your availability windows, the purpose of the call, and how long you expect it to take in a single message. If you want a call back, include your direct phone number rather than waiting for the other person to ask. Write a clear subject line In a crowded inbox, the subject line acts as a decision filter: Is this relevant? Is this urgent? Can this wait? Studies show that email subject lines critically influence whether a recipient opens, defers, or ignores an email (before theyve read a single sentence of the message).Do your best to craft a subject lines that are specific, concise, and action-oriented. For example, Budget Review Needed by 3 PM is more effective than a generic phrase like Quick Question. BUILD EMAIL TRUST If you teach people over time that your emails are concise and to the point, you are building email trust. This means that recipients are more likely to respond positively and quickly when they see your name. Researchers in written communication emphasize that consistency in formatting and clarity doesnt just improve readability, it builds an implicit reputation for professionalism. Getting faster email replies isnt about sounding smarter. Its about making decisions easier for the person on the other side of the screen. When your emails are clear, scannable, and consistent, you reduce mental load, build trust, and teach people to respond to you faster.
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E-Commerce
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