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AI fluency is quickly becoming the new leadership divide: Some executives are already embedding it into strategy, while others are still asking what it means. The gap is wideningand its shaping who gets hired to lead. Thats why AI fluency is becoming a top priority in leadership searches. Not deep technical mastery, but a practical understanding of how these tools work and where they apply. Companies want leaders who arent just talking about transformation but are actively engaged in it. People whove run pilots, evaluated risks, collaborated with product and tech, or led adoption efforts in their function. They dont need to be engineers. But they do need to know what these tools can (and cant) doand how to help others use them responsibly. How executives are actually using AI Executives at the forefront are already putting AI to work in meaningful, strategic ways. According to Salesforce, top-tier leaders are leveraging AI for critical tasks: running high-stakes market analysis, stress testing new business ideas before launch, and anticipating market shifts before they happen. A recent TechRadar piece reports that 74% of executives now trust AIs input more than that of colleagues, with 44% willing to let it override their own decisions. AI has become more than a dashboardits a boardroom copilot. Behind the scenes, back-office leaders are increasing AI spending: 92% of executives surveyed plan to ramp up investments in AI over the next three years, and 55% expect a boost of at least 10%. Yet execution is uneven. A recent IBM study found that while CEOs expect AI investment growth to more than double in the next two years, only 25% of AI initiatives have delivered expected ROIand just 16% have scaled enterprise-wide. Similarly, PwC found that while 79% of senior executives are adopting AI agents, many see success only when implementations are tied directly to measurable productivity gains in targeted areas. But high adoption doesnt always mean high impact. MIT researchers recently found that 95% of generative AI pilots fail to deliver measurable ROI, often because theyre launched without clear objectives or integration into core workflows. Meanwhile, another study warns of workslopa proliferation of low-quality output from poorly managed AI usage. These findings underscore a growing reality: AI fluency among leaders isnt just a nice-to-haveits the difference between pilots that fizzle and initiatives that scale. Leaders who understand both the capabilities and constraints of these tools are far better equipped to unlock value while avoiding the hidden costs of misuse. What leaders who use AI well do differently Heres what separates AI-fluent executives from the rest: Hands-on experimentationThese leaders gain firsthand experience with generative AIunderstanding not just the techs capabilities, but its limitations. Visible, scalable fluencyHarvard Business Publishings new study shows that employees with fluency arent just dabblingthey integrate AI into daily workflows. In “best-in-class” organizations, 98% of AI-fluent users are confident in using tools and report significant team performance gains. Strategic, not siloed, useAI isn’t just owned by the CTO. Leaders from across the organizationfrom chief human resources officers (CHROs) to CFOsare embedding AI literacy into their domains, turning it from a technical specialty into leadership capability. Intentional oversightEven when AI is applied, responsible use is rare: Infosys found that 95% of executives experienced AI mishaps, and only 2% of firms meet responsible-use standards. Dont just hire fasterhire toward the future Most companies today arent ignoring AItheyre trying to figure out how to keep up. They know they cant afford to fall behind, especially when competitors are investing aggressively in AI across operations. The challenge is finding people who can lead that shiftnot just within their function, but across the business. Thats the conversation Im having with clients right now. Not how do we hire someone fast? but how do we hire someone who can take us where we want to go? Takeaways for talent teams and leaders Screen for real fluency. Ask candidates to share where theyve deployed tools, navigated roadblocks, coled adoption, and managed both opportunity and risk. Favor handson experience, not academic abstraction. AI fluency is demonstrated, not talked aboutfrom pilot artifacts to team processes. Insist on governance and oversight. Pair fluency with accountability. Use AI, yesbut responsibly. Prioritize curiosity and adaptability. Leaders dont need to master every tool, but they do need to stay agile, ask questions, and foster a culture of experimentation. AI will keep evolving, and so must the people leading its adoption. Leaders arent expected to be coders. But they must know how to marshal AI, translate insight, and guide adoptionbalanced with judgment. The future of leadership is not running from change. Its defining it.
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E-Commerce
In early 2023, a couple of months after ChatGPT launched and became the fastest-growing consumer application in history, I remember feeling both excited but also a bit overwhelmed by the rapid pace of AI. The barrage of news, product launches, and innovative use cases was relentless. We held an executive meeting at that time and decided to immediately reassign additional teams from other long-planned initiatives to double down on AI. We saw an opportunity to deliver even more value to our customers. My experience is not unique. Across the board, leaders have been aggressively implementing AI to improve productivity, lower costs, and improve communicationbut the results have been disappointing to date for many organizations. Only 34% of organizations say their AI projects have returned a positive ROI for most or all initiatives, according to Lucids AI readiness survey. Unlocking the tremendous value AI offers isnt a technology problem. Its an operational one. Leaders need to be more intentional about their workflows and practices to realize AIs vast potential. OPERATIONS ARE DRAGGING AI INITIATIVES DOWN In the race to keep pace with AI, businesses are moving quickly. But their emphasis on speed comes at a cost. About 61% of knowledge workers said in the survey that their firms AI strategy is only somewhat to not at all well aligned with operational capabilities. Most are glossing over foundational steps today that jeopardize their chances for success tomorrow. One notable example is documenting company processes and knowledge, a critical input for AI initiatives. The survey found that most organizations lack process documentation for their AI initiatives. Only 16% of survey respondents replied that their workflows are extremely well documented. The top obstacle to documenting knowledge at scale is a lack of time, according to 41% of respondents. Before implementing AI, leaders should ensure their teams understand the importance of documenting processes so that they always make time for it. Teams cant harness AI to its fullest without well-documented, clearly structured processes. If an organization is already well into its implementation but didnt prioritize this upfront, its never too late to course-correct. Its actually critical to do so. The next top barrier to knowledge documentation is the lack of tools (30%). Recently, I met with a Fortune 500 executive whose company is mandating AI to drive significant efficiency and productivity gains, yet relying on a legacy tool to collaborate that was never built for teams and centered on the individual user. If companies want AI to be adopted across the enterprise, they need a common space for brainstorming, decision making, planning, and collaborative documentation. Even with all of AIs transformative capabilities, the fundamentals of successfully integrating technology into a workplace still apply. Companies need the right tools that enable better collaboration and help them document current processes and best practices easily. FRICTION AROUND COLLABORATION LIMITS AIS IMPACT A while back, our executive team tackled a strategic challenge together. A product leader used AI to generate an impressive preparatory memo in a short timeframe, summarizing the challenge, benchmarking solutions, and offering recommendations. But the AI-generated memo was the starting point, not the end. We still needed to debate nuances specific to Lucid’s context, prioritize actions and assign ownership, and document takeaways and define next steps. Even with the amount of work that can be accelerated and automated with AI, collaboration is still critical. The survey found 23% of respondents say collaboration is often or always the bottleneck in complex work. Implementing AI is a major undertaking. Only by consistently engaging key stakeholders for in-depth discussions, clarifying decisions, and ensuring shared understanding can these bold initiatives succeed. THE NEW COMPETITIVE EDGE IN AI The success of a company’s AI strategy is only as strong as its execution, and a large perception gap proves this. The survey found that 61% of C-suite executives feel their AI approach is well considered, but a much smaller percentage of managers (49%) and entry-level employees (36%) agree. Closing this gap requires more than just a good plan; it requires operational readiness. Organizations must build stronger processes, improve documentation, and foster better collaboration to successfully implement AI. Harnessing the power of this revolutionary technology requires a level of rigor most organizations have yet to demonstrate. The new competitive advantage for AI adoption lies in the operational systems behind it. Dave Grow is CEO of Lucid Software.
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E-Commerce
One thing I genuinely love about my job is mentoring young professionals who are just getting started in their careers. Gaining a foothold in the tech industry is tough, especially in the AI age. And todays new generation of employees are asking compelling questions: How do I focus in complex environments? How do I create a competitive advantage? What happens if I fail? I recently found myself asking similar questionsabout my golf gameto one of the worlds top golfers, Padraig Harrington. It was humbling to be on the other side of the fence, getting insights from a global legend that not only improved my swing, but helped me better coach the next generation of tech employees. Here are four highlights. Commit to a strategy. Pressures on. Its time to step up and take a swing. But then you second-guess your approach. Do you have the right strategy? Should you try something new? Padraig says that changing your strategy at the last minute could ruin your shot; you need to have a plan and stick to it. Your key goal on the golf course, he says, is to not change your mind over the ball. This might seem counter to advice you would typically give young professionals. At SAS we talk a lot about staying flexible and agile amid changing market conditions. But while flexibility has its place, so does confidence and consistency. Once you lock in during the moments that matter most, youll get the job done the way you intended, instead of panicking under pressure. Seek metrics that no one else measures. This one really resonated with me, because in data and AI its tempting to default to metrics that are easy to capture: latency, throughput, conversions. But Padraig also zeros in on his own personal performance indicators that are slightly outside the norm, like how often his first putt is taken from inside the eight-foot marknot because it looks good on paper, but because it increases his chances of converting birdies or rescuing par. As a data and AI organization that helps customers stay ahead of the game, this insight is incredibly useful. We will always track the necessary metrics, but we can also dig deeper to find that extra edge. This is true for individual employees, as well. If you do things a little differently than everyone else, find your own performance indicators, and sharpen your unique skills, youll stand out from the crowd. Lean into the difficult shots. Practice putting from off the green is a strategy Padraig lives by for short game. It sounds simple, but what hes really saying is you should challenge yourself when practicing. Great golfers, he says, practice being under pressure. They give themselves obstacles to overcome and being out of position by practicing from difficult lies or in unfamiliar wind conditions, so theyre ready for anything when its tournament time. In organizations, just like in golf, you dont only win in good conditions. You also win by managing through tough conditions. Its how you adapt when facing a challenge, like overcoming a volatile market, mitigating bias, or managing data quality. If youre just starting out in your career, uncertainty is a given; but if you learn to anticipate it, or even welcome it, youll be ready for anything thrown your way. Love the next shot. Padraigs advice is pivotal when it comes to playing the game: Love the next shot. Dont dwell on past mistakes, especially as theres nothing you can do about them. This is a powerful mindset shift in any profession, especially in tech, where failure is part of the terrain. Ive found that young professionals tend to be harder on themselves; they fear that one mistake could tank their future career. As technology leaders, mentors, or even golf professionals, we need to remind the younger generation not to beat themselves up over the last shot. Just learn, adjust, and commit fully to the next one. With the next SAS Championship PGA Tour Champions golf event happening now, Im already applying these insightsboth on the course and in the conference room. And if I end up in the rough? Thats fine. Ive got another shot to look forward to. Bryan Harris is the executive vice president and chief technology officer at SAS.
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E-Commerce
Artificial intelligence is changing everything: how we work, build, create, and grow. Its unlocking opportunities daily. At Grove Collaborative, weve seen it firsthand. AI helps us move faster, make smarter decisions, and, most importantly, serve our customers better. But heres the part not enough people are talking about: the environmental cost. AI is resource-intensive, especially when rolled out at scale. It uses a ton of electricity and water, drives new forms of e-waste, and complicates carbon accounting. For mission-driven companiesespecially those built on sustainabilitythat creates a real tension. We want to innovate. But we also want to protect the planet we all share. So we asked ourselves a deceptively simple question: Whats our AI footprint? We didnt know the answer. There was no standard. No export from a large language model. No tool. Just a growing impact no one seemed to be measuring. So we built one. A METHOD TO ESTIMATE AI EMISSIONS Partnering with our longtime friends at Gravity, a carbon and energy accounting platform, we developed a science-informed method for estimating AI emissionsfactoring in compute time, server power, and grid emissions. Its not perfect (no model is). But its a practical start that gives us real visibility into the footprint were creating. Our projected 2025 AI-related carbon footprint is 17.8 metric tons of CO2e, equivalent to roughly 6% of our 2024 business travel emissions (299 metric tons of CO2e). This is a first estimate based on our current usage today, but we know this number will grow. And having a baseline is essential to understand our impact so that we can explore how to reduce it over time. During NYC Climate Week, we became one of the first retailers to disclose estimated AI emissions. And beginning in 2026, well include them in our annual sustainability reporting. But this cant just be about us. Which is why were open-sourcing the methodology. Any company, whether a startup or multinational, canand shoulduse it to measure and track their AI footprint. Because the speed of AI adoption is outpacing our ability to measure its impact. Without transparency, theres no path to making AI both powerful and sustainable. This isnt about slowing innovation. Its about making sure innovation and sustainability move forward together, not in opposition. 4 WAYS TO MEASURE YOUR AI FOOTPRINT Heres the playbook were proposing for you to measure your AI footprint: Measure what matters. Dont guess. Track AI emissions with as much granularity as current science allows. Build them into Scope 3 emissions reporting. Mitigate with integrity. Offset what you cant reduce, but dont stop there. Balance AI emissions with high-quality carbon offsets and, based on your measurement, invest in strategies to reduce them over time. Choose more sustainable models. Favor AI platforms that share environmental data, prioritize efficiency and water stewardship, and embrace circular design. Lead through disclosure. Perfect measurement doesnt exist. But transparency builds trust and drives momentum. Of course, this only works if the upstream providersthe companies building the AI infrastructure itselfstep up too. Were calling on OpenAI, Anthropic, Google, Microsoft, Meta, NVIDIA, and others to disclose their tools environmental impact. Without their transparency, no one can truly measure with accuracy. LEAD WITH TRANSPARENCY We know were early. We dont have all the answers. But we believe in leading with openness, not waiting for perfect data, and driving progress over perfection. Our mission at Grove has always been to create healthier homes and a healthier planet. That mission doesnt end with AI, but it does have to evolve to include it. Our current AI emissions are modest. But even small footprints matter. And if we dont measure them, theyll grow unchecked. So heres the challenge Ill leave with my peers: Dont let sustainability lag behind innovation. Measure your impact. Share your findings. Hold yourself accountable. The future of innovation isnt just faster. Its more responsible, more transparent, more human. Thats how we make real progressand make sure it lasts. Jeff Yurcisin is CEO of Grove Collaborative.
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E-Commerce
Theres a line I heard recently from Mel Robbins thats been echoing in my head ever since: People do well if they can.Its deceptively simple. The kind of phrase you nod at, maybe even repost. But when you sit with it, really sit with it, it starts to challenge a lot of the assumptions we make every day.Especially when it comes to financial health. Not lazy, just locked out Lets be honest: Its easy to judge what we dont understand. We look at people struggling with money and tell ourselves stories. Theyre reckless. They dont care. They should know better. But heres the thing: Most people do care. They want to pay off debt. They want to build credit. They want to save for the future, buy homes, support their families, live with dignity. What they often dont have is access, or a roadmap. Thats not laziness. Thats infrastructure failure.You wouldnt expect someone to drive to a job interview without a car, a license, or a GPS. So why do we expect people to navigate complex financial systems with zero guidance and very few guardrails? Skill, not will I grew up in a community where financial literacy wasnt part of the conversation; not at school, not at home, not even at the bank. I didnt learn what a credit score was until I had already messed mine up. And let me tell you, the learning curve wasnt gentle.So I get frustrated when financial challenges are framed as a lack of personal responsibility. That framing is lazy.Let me say that again: That framing is lazy. Not the people. Not the effort. The framing. Because once you believe that people are doing the best they can with the tools they have, everything changes. You stop asking, Why dont they just fix it? and start asking, Whats missing from the toolbox? The illusion of equal opportunity We love to talk about equal access in this country, but the truth is, access is rarely equal. Its shaped by geography, race, internet speed, ZIP code, history, policy, and yes, banking systems.You can’t teach people to swim and then throw them into a pool with no ladder. Thats what we do when we say, Just build credit. But we dont acknowledge that millions of people are credit invisible or have a thin file because their rent, utility payments, or side hustle income doesnt get counted.And then we wonder why so many people feel stuck. Lets redesign the system like we believe in people What would it look like if we actually operated from the belief that people want to do well, and will, if given the right support?In my role at FICO, were constantly asking that question. We dont just talk about financial inclusion. Were reshaping how our tools show up in communities, how our education reaches people, and how our partnerships remove friction, not create more.Weve launched programs that meet people where they are. Not just where we think they should be. We partner with nonprofit organizations, elected officials, and even local credit unions to host free credit education sessions, translated, and culturally relevant. Because accessibility isnt just about logging in. Its about feeling safe enough to show up. And what about the kids? This mindset shift isnt just for adults, either. Im a mom. And Ive seen firsthand how easy it is to label kids as difficult, especially neurodivergent kids, when theyre just overwhelmed or unsupported.They dont lack motivation. They lack tools, patience, and sometimes, a grown-up who gets it.Sound familiar?Adults are no different. Most of us are still carrying money habits, shame, and silence from childhood. If we werent taught how to manage money at 7, why do we expect everyone to have it figured out at 37? A better way forward So where do we go from here?We start by telling the truth: – Financial hardship isnt a character flaw. – Credit education isnt a luxury. – Access to opportunity should not depend on what side of the city you live on. And then we build programs, products, and policies that reflect that truth.That means working with communities, not on them. It means bringing empathy into corporate boardrooms. It means seeing people as capable, not broken.Because if we believe people do well if they can, then its on us to make sure they can. A final thought Theres someone out there right now who wants to fix their credit, get out of debt, or open their first savings account. Theyre not lazy. Theyre not unmotivated. They just havent had a fair shot.We dont need to change people. We need to change how we see them, and what we give them to work with.Because people do well if they can. And theyre counting on us to act like it. Rukiya Kelly is global head of corporate impact and engagement at FICO.
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E-Commerce
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