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The real AI story in most organizations isnt about algorithms; its about habits. New tools arrive with impressive demonstrations and confident promises, yet the day-to-day routines that decide what gets attention, who can take a risk, and what counts as a good job tend to remain the same. Leaders set up special units, roll out training, or look for quick savings, only to find that the old culture quietly resets the terms. When that happens, early gains fade, adoption stalls, and cynicism grows. This article draws on our forthcoming book to look at three recurring myths that help prop up existing cultures and prevent the deep transformations that are needed to support successful AI implementations. Transforming a business to make the most of AI means moving past these comfortable stories and changing the conditions under which the whole organization works. Myth 1: ‘Innovation Units Will Save Us’ After five years of operation, the U.K.s Government Digital Service (GDS) seemed untouchable. Created in 2011, the GDS revolutionized Britains digital services. With the goal of reenvisioning government as a platform, it consolidated hundreds of websites into a single, easy-to-use portal, cut waste by forcing departments to unify their platforms, and showed that, with the right attitude, even government agencies could move with the speed of a startup. In 2016, the U.K.s digital services were ranked the best in the world. Yet by 2020, the GDS had disappeared as a force within the U.K. government. This pattern repeats regularly across corporate innovation labs: create an elite unit, give it special rules, celebrate early wins, watch it die. An innovation unit can deliver extraordinary results so long as it has senior leadership protection, free-flowing resources, and an internal culture that attracts exceptional talent. But the model also contains the seeds of its own demise. The outsider status that enables breakthrough innovation makes large-scale sustainability nearly impossible. When executive sponsors move on, the shield drops, and organizational antibodies start reasserting cultural norms. This predictable lifecycle applies to AI-focused teams as much as those driving any other type of technological change. Leadership transitions are inevitable. New executives question special rules. The innovation unit that draws its power from being outside the system gets pulled back in again, and the flow of novel ideas slows to a trickle. The lesson to take from this isnt that we should abandon innovation unitsits that we should use them strategically and follow up on the gains they make. Innovation units should be seen as catalysts, not permanent solutions. While these teams are forging ahead with quick wins and proving new approaches, organizations also need to transform their broader culture in parallel. The goal shouldnt be protecting the innovation unit indefinitely but aligning organizational culture with the innovative approaches it pioneers. If innovation units are sparks, culture is the oxygen. You need bothat the same timeor the flame dies. Myth 2: “Our People Just Need Training” Companies spend millions teaching employees to use AI tools, then wonder why transformation never happens. The reason is that the underlying problem isnt just about skillsits about the imagination needed to use them effectively. You can train your workforce to operate the new technology, but you cant train them to be excited about it or to care where it will take the business. That requires change at the cultural level. When it comes to AI, the real gap is conceptual, not technical. Employees need to shift from seeing AI as a better calculator to understanding the role it can play as a thought partner. This requires more than tutorials. It means showcasing how AI can transform workflows and then rewarding its creative use. Show a sales team how AI can predict client needs before calls, not just transcribe them afterward. Demonstrate how legal teams can shift from document review to strategic counseling. When organizations tell employees to use the tools but dont change the social norms around using them, people can be punished for doing exactly what leadership asked. A recent experiment with 1,026 software engineers found that when reviewers believed code was produced with AI assistance, they rated the authors competence lower by about 9% even though the work was identical. Even more concerning was that the penalty was larger for women and older engineers, groups who tended to be treated negatively in assessments already. In a companion survey of 919 engineers, many reported hesitating to use AI for fear that adoption would be read as a lack of skillillustrating why access and training dont translate into uptake when the culture signals that visible AI use will harm credibility. Myth 3: “AI Makes It Easy to Slim Down the Workforce” Theres a seductive promise being sold to companies right now. The way to realize AIs value is simply to replace as many workers as you can. Fire half your staff, pocket the savings, let machines handle the rest. Simple arithmetic for simple minds. The messy truth is that AI can and will replace many human jobs, but it wont do it cleanly and it wont do it easily. In most cases, the idea that you can simply swap out the human component and replace it with a machine just doesnt work. Humans work together as parts of multilayered social structures that have evolved as ecosystems. Its often the case that if you change one part, there will be major consequences for another. If we rush into automation too quickly, we risk pulling away the pillars that hold the whole structure up. Think about the tedious hours that junior analysts spend cleaning data, checking figures, and building models from scratch. Or the work a newly appointed manager will do overseeing performance and filling in paperwork. We call it grunt work, but its actually how humans develop the skills they will need in more senior roles. Take away the entry-level jobs and you lose the career path that delivers the highly skilled senior leaders you need. Allow AI-powered deskilling to take place and you lose the human judgment and oversight that institutions rely on. Klarnas trajectory shows both sides of this equation. In early 2024, its AI assistant handled two-thirds of customer chats, delivering resolution times under two minutes and a 25% drop in repeat inquiries. By 2025, Klarnas leadership was publicly acknowledging the limits of an AI-only approach and began reopening human roles and emphasizing the customer experience alongside automation. The real question isnt how many people you can eliminate. For effective AI implementation, you need to understand that humans make essential contributions that dont appear in their job descriptions. The Culture Transformation Playbook: Fixng the Myths Culture change depends on habits, incentives, and expectations, not just adding new tools. The playbook that follows presents concrete steps that leaders can take now to avoid the pitfalls many companies are running into. Run Parallel Transformations (Fixes Myth 1). The innovation unit delivers quick wins while a separate initiative transforms broader culture. These must happen simultaneously, not sequentially. Use the innovation units protected status and early victories to create organizational belief in change but invest equally in preparing the mainline culture for whats coming. Without parallel tracks, the innovation unit becomes an isolated island of excellence that will eventually be washed away. Transform the Middle Layer (Fixes Myth 2). Middle managers are the real gatekeepers of culture change. Stop wasting energy trying to convert skeptics. Instead, identify the curious and give them authority to experiment, budget to fail, and cover from meeting traditional metrics. Try giving selected managers a micro-charter to implement change in their team, along with a weekly show-the-work session (what AI was used, what was accepted or overruled, and why) to share what theyve learned with peers. Build Alternative Learning Paths (Fixes Myth 3). If AI eliminates the experiences that build judgment, you must consciously re-create them. High-fidelity simulations, rotation programs, and human days working without AI become existential necessities. Explicitly preserve activities that develop pattern recognition and business instinct. The investment might seem wasteful until you realize the alternative is a workforce that can operate tools but cant respond when something breaks. The Choice Culture transformation is harder than technology implementation. Its messier, slower, and impossible to fully control. Most companies will choose the easy path: buy the AI, train on the tools, create an innovation lab, and hope for the best. The few who choose the hard pathparallel transformation, cultural evolution, preserved learning experienceswill gain powerful competitive advantages. Theyll have workforces that dont just use AI but think with it, cultures that dont just tolerate change but expect it, and organizations that dont just survive disruption but drive it.
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E-Commerce
There has been a lot of chatter about A24s takeover of the Cherry Lane Theatre. What might seem a quirky side project for the independent studio known for Lady Bird, Uncut Gems, and Hereditary is in reality a sharp, shrewd move in an industry facing disruption and streaming fatigue. Live performance is one of the few cultural experiences that cant be automated, replicated, or played on demand. By stepping into theater, A24 is hedging against an AI-saturated future while also deepening its cultural footprint.When the deal was first announced in late 2023, the scuttlebutt was rooted in practicalities. Its all about creative synergies, was one refrain. Theyre diversifying their revenue streams to help offset the volatility of the film business. Theatre is more predictable than film. They can test out new stories in a low-risk environment. These were all valid comments, sure, but then a close friend and accomplished film industry executive said something that really piqued my interest. I wonder if they are further differentiating themselves in the market by building a futureproof brand. Aha! Now we were getting somewhere. [Photo: Dia Dipasupil/Getty Images] A powerful brand Coming from the branding world, I may be biased. Or perhaps just acutely aware when I sense a company doing something out of the norm. A24 certainly fits the bill. To start, the story its looking to tell, its role in the industry, and how it wishes to be perceived are markedly different from the other studios. As a result, it has a growing community of acolytes who identify with it and love it for that. These are trademarks of a powerful brand. Michelle Yeoh in Everything Everywhere All at Once, 2022. [Photo: David Bornfriend/courtesy A24] Most studios understand that their franchises are brands that they can build around, but when it comes to themselves, there is little to no attention given to an overarching narrative about what they stand for. To a branding person, this is perplexing. There is only one other industry that comes to mind that acts like this: Big Pharma. The drug companies seem to care that their patients know their drugs by name (think Lipitor, Prilosec, Viagra, Prozac) but less about how they themselves are perceived. Adam Sander in Uncut Gems, 2019. [Photo: Julieta Cervantes/courtesy A24] A24 is different. It is one of the very few in Hollywood that seems to be building a truly beloved brand. Do this simple test at home. When A24 flashes up on the screen at the start of a movie, does it mean something to you? Does it affect your perception of what is to come? Like me, do you even get warm and giddy inside? Florence Pugh in Midsommar, 2019. [Photo: Gabor Kotschy/courtesy A24] Perhaps deep down, we all understand that A24 is all about exceptional, original, creative content. The questions one might ask are: How is it going about building a world-class brand? And why does that matter? A$AP Rocky and Rose Byrne in If I Had Legs I’d Kick You, 2025. [Photo: Logan White/courtesy A24] Content is still king One simple idea prevails in our fast-evolving world: Creativity wins. And increasingly so. The adae content is king has been floated around the entertainment industry over the years. And yet, for whatever reason, it seems to get forgotten with every new business cycle. In its place, Im hearing things like, “We’re leveraging AI-driven audience analytics and predictive modeling algorithms to revolutionize content creation through real-time sentiment optimization and it’s a complete paradigm shift that democratizes storytelling via data-driven narrative matrices.” Paul Rudd and Jenna Ortega in Death of a Unicorn, 2025. [Photo: courtesy A24] Its becoming readily apparent that the general public doesnt want an AI-generated model in their Guess ads, or a machine-generated song that Nirvana could have written. Audiences want to be surprised and delighted with new, fresh, exciting content. A24s brand is synonymous with creativity, and younger movie lovers inherently understand this.The brands that matter most today are those that are daring. Anyone who has seen Ons Zone Dreamer campaign with Zendaya floating around in outer space knows what I mean. The Big Five studios are publicly traded, and so by nature forced to drive revenue for shareholders and to mitigate risk (the yin to darings yang). Ironically though, the greatest historical payoffs in Hollywood, either on a % basis or through expansion into other verticals, are those that have taken calculated risks. A24 exemplifies that spirit today, with the Cherry Lane acquisition its latest proof point. Dwayne Johnson in The Smashing Machine, 2025. [Photo: courtesy A24] Daring creativity Another key tenet to building a strong studio brand has to do with the same advice as I give to all the companies and institutions we work with: Do what the robots cant. At its core, this is a euphemism for fostering human interaction. That matters especially now. The U.S. surgeon general recently declared a loneliness epidemic. In this context, A24 isnt just buying a building, its investing in the kind of in-person experiences people are craving. Whether actively or intuitively, A24 is building an increasingly powerful brand that stands for daring creativity. Believed and beloved, it has established a cultlike following that subscribes to this world its creating, a world that gives it license to expand into any line of business that stands for the same. The Cherry Lane is a fascinating early move that insulates the company from a future saturated with AI. The question is whether others will follow suit. Will studios also see an opportunity to congregate people, perhaps by incorporating next-generation movie theater experiences into their businesses? Could they blend hospitality and entertainment through partnerships that deliver immersive venues? The door is open . . . and A24 is first through it.
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E-Commerce
Climate tech isnt a thing. It has shifted in recent years from a category to define clean energy companies to an umbrella phrase that loses meaning the more we use it. Granted, the term is everywhere: inserted into VC pitch decks, plastered on billboards along highways from San Francisco to Austin to Boston, wedged into government policy papers, and featured prominently on conference agendas. Media properties from CNBC to GreenBiz rely on it as a traffic-driving category. And theres a reason why. A changing climate is the most complex and vast challenge and opportunity confronting our society today. That also means we cant afford ambiguity. We need accountability. We need progress. We need to reengineer infrastructure with advanced tech that future-proofs as it solves urgent and complex problems. Now. Which means we need precision. And we need to acknowledge that infrastructure and markets that have served us for so long are failingand in need of rebuilding to anticipate and meet future challenges. Our world is in desperate need of solutions tied to specific applications and impact across energy tech, waste tech, food tech, and carbon tech. We need solutions that advance specific areas of deeply specialized work with distinct metrics and challenges like energy storage, batteries, food security, and sustainable fuel development. And, we need talent trained and sharpened to tackle these specific problems. Ambiguity is the enemy of progress Progress requires clarity. Energy technology is a distinct thing. Waste technology is a distinct thing. Transportation technology, energy storage, agriculture and food sustainability, carbon removalthese are specific categories with definable challenges and measurable outcomes. Each is firmly tied to infrastructure and requires dedicated engineering, specialized expertise, and different pools of capital. For example, grid storage is not a climate tech problemits a specific energy challenge with concrete metrics: cost per kilowatt-hour, storage capacity, duration, and efficiency. Grid storage is about optimizing supply and demand, the outcome of which is a financial, political, and engineering goal, not a moral imperative. We must connect the promise and hype of AI-powered software solutions to their physical applications in the real world. Why? Because solving these big, specific problems requires more than computation behind a screen. Realizing the promise of AI to transform and improve is only possible if it enters the physical realm and changes the mechanics of existing ways of doing things. Calling the solutions to these problems climate tech is a disservice to the work because it no longer adequately captures the scale and range of what’s required. Breaking climate tech down to drive breakthroughs We need to build and invest in technologies that are better, faster, cheaper than what came before and solve real problemsrather than loaded words that offer environmental promise and not much else. The trajectory of biotech offers a solid framework. Rather than lumping everything under a term like health tech, industry pioneers stood up clearly defined categories, including: immunotherapy, CRISPR, mRNA vaccine development, oncology, longevity, and so on. Each domain pursued a specific set of problems and attracted talent and capital to solve them. The result? Breakthroughs. Whether we realize it or not, software also focused in recent years, which has helped to accelerate progress. Information technology gave way to specific technical disciplines like cybersecurity, cloud computing, and enterprise tools. Category focus allowed companies to gain market share and differentiate with customer experience and accountability front-and-center. Its time that climate tech undergoes the same level of rigorous redefinition. And its not just because were approaching critical climate tipping points (which we are). Its because the economic opportunity cost of not acting is too great. The future of American communities and industries from agriculture to manufacturing rests on our ability to effectively seize the opportunities in front of us and reengineer them. Everything needs to be built for the future with engineering precision and a specific problem in mind to solve. We need infrastructure and hardware solutions to solve focused problems like recycling plastic for manufacturing, rendering cement carbon-neutral, electrifying freight transport, rethinking protein production, and removing carbon at scale. We cannot grow the economy in the future without approaching all tech as climate tech. For example, the investment firm I cofounded, Incite, invested in Monarch, a startup with a fleet of AI-powered electric vehicles and tech solutions that work for agricultural clients ranging from dairy farmers to municipalities to winemakers. Monarch recently shipped MonarchOne, an end-to-end physical AI platform for OEMs to more efficiently manage work and use data to influence operations across environments. Monarch isnt a climate tech company. Its an AI and robotics company with clear environmental benefits. Working toward a post-climate tech world Climate tech served its purpose as an initial rallying cry. It placed an urgent crisis squarely on the map of capital markets, boardrooms, and policy agendas. It made innovation to help us take care of our planet inevitable. Totally unsurprisingly, however, grouping a product or tech into the vague category enables more greenwashing and ambiguity when what we need is progress, focus, and accountability. In order to scale up the grid, add resilience to infrastructure, and prevent the housing market from insurance collapse, we need to retire not just the language but the entire categorization of climate tech completely. We must dismantle the umbrella term into specific, infrastructure-centered areas in need of urgent work. Lets refine our language. Words matter. Tech is crucial to curbing negative environmental impacts. But the utility of climate tech is running on fumes. Lets stop pretending its still a thingand seize the opportunity to build and invest in the physical infrastructure, software, apps, and technologies that will power economic opportunities and enrich life around the world.
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E-Commerce
Every year, 12.5 million travelers pass through South Station, Boston’s 126-year-old transportation hub, to hop on Greyhound buses, Amtrak trains, and the commuter rail. But the station hadn’t been renovated in 30 years, and looked worn, industrial, and dated. For decades, the city of Boston has been working on an ambitious urban infrastructure redevelopment project to reimagine the city’s downtown. It recently unveiled a stunning transformation of South Station that includes a redesigned transportation hub as well as a 51-story tower that will house luxury condos, offices, a rooftop garden, and a high-end restaurant. [Photo: Jason O’Rear/courtesy Pelli Clarke & Partners] For the hundreds of thousands commuters who pass through South Station every day, the most obvious change is the new vaulted concourse, called the Great Space, that will usher them to their trains. It features 10 concrete arches that reach 60 feet into the air. The archways open to the street, bus stops, and train lines. The structure supports three enormous domes that have a ring of spotlights at the center of them to brighten the interior. While the previous concourse felt industrial and functional, with concrete ceilings and metal railings, it now feels opulent and open. [Photo: Jason O’Rear/courtesy Pelli Clarke & Partners] The design of the space was conceptualized by Pelli Clarke & Partners, an architectural practice based in New Haven, Connecticut founded in 1977 by Yale Professor Cesar Pelli. The firm is known for taking ambitious projects in cities around the world, including the Petronas Towers in Kuala Lumpur, Malaysia; Tokyo’s Mori JP Tower, which is now the tallest skyscraper in Japan; and the Natural History Museum in Chengdu, China. The project was a private-public partnership, backed by the developer Hines. Amtrak, the Massachusetts Department of Transportation, and Boston Planning and Development Agency were also involved in the process. [Photo: Jason O’Rear/courtesy Pelli Clarke & Partners] While part of the goal of the project was urban renewal, the architects were also tasked with modernizing the transportation hub to increase capacity and improve efficiency. There is now 50% more capacity in the bus terminal, and 70% increased rail capacity. “As Boston’s population grows, so is the demand for transportation,” says Graham Banks, a partner at Pelli Clarke, who worked on this project. “But rebuilding South Station without disrupting any of the transportation service was an enormous challenge. Work took place slowly.” Banks says work began on this project in early 2020. The COVID-19 pandemic delayed construction, and then afterwards, workers were only able to work in the brief stretches when trains and buses weren’t running. “Workers would be sitting around waiting for Amtrak to give them the signal that they could get going,” he recalls. “Orchestrating the logistics of construction took a lot of work.” [Photo: Jason O’Rear/courtesy Pelli Clarke & Partners] The original South Station structure was unveiled in 1899 during the late Gilded Age, when railway tracks expanded rapidly across the country. Five different railroads served Boston, and initially, each had their own terminal. South Station, which was designed by architects Shepley, Rutan, and Coolidge, was meant to consolidate these different lines. By 1913, it had become the busiest station in New England, helping to boost Boston’s status as a city. The station in the late 19th century. [Photo: GHI/Universal Images Group/Getty Images] Pelli Clarke wanted to preserve the original South Station building, while also modernizing it. They have kept the South Station’s facade, but they also built a glass tower on top of it, adding another skyscraper to Boston’s skyline. On lower levels, there is office space. Banks says that there is already interest from local firms to move in. “These offices are designed to have all the amenities and ambiance of a hotel,” says Banks. “Companies realize that they have to entice workers to come into the office.” [Photo: Jason O’Rear/courtesy Pelli Clarke & Partners] Starting at the 36th floor and going to the top, there are luxury Ritz-Carlton apartments, that cost between $1.3 million for a 683-square-foot one-bedroom and $14.5 million for a duplex penthouse. Residents will have access to an outdoor pool that overlooks Back Bay, as well as a 1-acre rooftop park that features gardens, a dog run, an outdoor movie theater, and a dining terrace. Residents have their own private entrances, both from the street and from a private parking garage. The idea of introducing luxury apartments to South Station is fairly radical. For years, the station and the area around it were crime ridden. The neighboring financial district emptied out at night, as workers went home. But building high-end condos is likely to make the area livelier and spur restaurants, grocers, and shops to come back to the area. It’s a similar transformation to what has happened in New York’s financial district, which is now bursting with luxury apartments, office buildings, and glittering shopping centers. “This part of the city will now be alive 24/7,” Banks says. [Photo: Jason O’Rear/courtesy Pelli Clarke & Partners] South Station’s redevelopment is part of a broader revitalization of downtown Boston. Boston’s Planning and Development Agency, in partnership with WS Development, transformed the Seaport District from an industrial wasteland, covered in parking lots and vacant wharves, into one of its hottest neighborhoods. In 2014, it unveiled the new mixed-use development, which features high-end condos, buzzy restaurants, and hip retailers like Warby Parker and Mejuri. It quickly became the fastest-growing part of Boston, and is now an economic engine for the city. There’s some concern that these luxury apartments and offices will alienate Boston’s lower and moderate income residents. And it could further exacerbate the city’s affordability crisis, much like the one New York City has experienced in recent years. But at the same time, the revitalization of this transportation hub also benefits everyday Bostonian who pass through it on their daily commutes and who rely on buses and trains to get in and out of the city. We’ll have to wait and see how the new South Station Tower transforms the neighborhood. But in the meanwhile, hopping off a bus or train upon your arrival to Boston is already a more pleasant experience.
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E-Commerce
With more than 30 years in digital transformation, Ive seen technology cycles come and go. And the latest wave Im seeing is AI-powered automation. It promises sweeping gains in productivity, but without ethical guardrails, it risks undermining the trust leaders depend on to grow. Thats why leaders can no longer treat ethics as an afterthought. Automation isnt just a technical upgrade. It is a human, cultural, and reputational challenge. The choices that leaders make today will determine whether automation drives sustainable progress or fuels mistrust and inequity. The promise and the peril Automation has a lot of benefits. It can free workers from repetitive tasks, improve customer service, and open new possibilities for innovation. In manufacturing, robots can boost safety by removing people from hazardous environments. When it comes to finance, AI can spot fraud faster than any analyst. And in healthcare, hospitals are using automation to speed up patient admissions (though there are privacy and consent issues). But every gain carries a shadow. Bias in algorithms can lock in discrimination. Displaced workers may find no clear pathways to re-skill. Opaque decision-making can leave customers and regulators in the dark. And what looks like a cost saving in year one can become a reputational crisis by year three. Lessons from the field Global surveys show that leaders remain uncertain about the value and risks of AI adoption. McKinsey reports that while nearly 70% of businesses have adopted at least one AI capability, fewer than one in three have embedded AI into core strategies with measurable returns. The World Economic Forums 2025 Future of Jobs report projects that by 2030, automation and other global shifts will create about 170 million jobs, while displacing around 92 million, for a net gain of 78 million roles. This makes equity, transparency, and upskilling urgent priorities for leaders. The Back on Track Foundation, a not-for-profit case study, illustrates both the potential and the pitfalls. By introducing AI tools to support case management, they improved efficiency but faced immediate questions about data privacy and oversight. Their experience is a reminder that automation is never just about efficiency. Its also about accountability, transparency, and public trust. And it doesnt just impact nonprofits. Manufacturers rolling out automated quality control or banks deploying AI in credit decisions face the same ethical crossroads. How do we balance efficiency and productivity gains with fairness, transparency, and responsibility? Why leaders cannot wait for regulation Unlike the European Union, many countries have not yet built comprehensive legal frameworks for AI. Existing laws around privacy, consumer protection, and workplace rights still apply, but theres no dedicated safety net. That means every board, CEO, and executive team needs to lead with their own ethical compass. Transparency is nonnegotiable. Customers, staff, and stakeholders deserve to know when automation is involved, what guardrails exist, and how to handle recourse if (or when) things go wrong. In my own work with clients, I use AI tools for research, analyzing reports, and preparing strategy briefs. The responsibility and decisions remain mine, but using these tools has reinforced how transparency builds trust. Leaders need to hold themselves to the same standard and be open about where and how they use automation. Equity and the human-first lens Equity is the ethical line thats most at risk. Without deliberate design, automation can deepen divides: between city and region, skilled and unskilled, and insiders and outsiders. PwC estimates that up to 30% of jobs in OECD countries are at potential risk of automation by the mid-2030s, with lower-skilled roles most exposed. A factory that automates its production line might save millions, but what happens to the workforce whose jobs disappear overnight? If you dont reinvest savings into re-skilling or transition, inequity widens. This is the human-first lens. Technology can (and should) amplify the roles of the people in the organization. What it should never do is replace their dignity or the critical and creative lens humans bring. Ethical automation aligns with company values and extends them into every workflow and algorithm. Ethical AI adoption Leaders who are looking to adopt AI in an ethical way should consider taking the following steps Audit your automation footprint. Map where automation already touches your business, who it impacts, and what risks are in play. Create governance frameworks. Decide who is accountable, how they will explain decisions, and what ethical standards apply. Invest in literacy. When it comes to training, you need to go beyond technical staff. Boards, executives, and frontline teams all need a baseline understanding of automation. Googles AI Works 2025 report found that organizations investing in AI training achieved productivity gains of up to tenfold. Measure more than ROI. Track trust, transparency, equity, and social impact alongside efficiency metrics. Be transparent. If automation influences a customer outcome or an employee process, disclose it. Trust grows in the open. Automation is inevitable. Ethical leadership is optional, but only in the short term. Regulation will eventually catch up, and those who embed human-first, transparent practices now will be far ahead of the curve. Ethical automation isnt just about managing risks. It is a competitive advantage. Organizations that lead with equity and transparency will be the ones attracting talent, investors, and customers in the years ahead.
Category:
E-Commerce
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