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AI is transforming work, but its not just the tools that matter. Its how teams use them, and who they become in the process, that sets them apart. While many organizations scramble to integrate AI into every corner of the business, the best teams are asking better questions. Theyre not just moving faster; theyre working with greater intention. They protect whats human: trust, creativity, and long-term thinking. And they reshape how they collaborate, communicate, and grow, turning disruption into a durable advantage. For Eric, the SVP of product at a global advertising technology company, the mission was clear: lead AI integration across all business units. But not everyone was on board. Peer teams were skeptical, overloaded, and unsure whether AI would help or hinder their work. Instead of forcing adoption, Eric built a cross-functional AI Champions Circle. Their goal wasnt to become experts overnight. It was to explore, experiment, and learn together. They surfaced use cases, skill gaps, and unexpected opportunities to showcase the companys strengths. One early win, a prototype that automated client reporting, showed how AI could streamline customer delivery, create more space for higher-value work, and give skeptics a reason to lean in. We didnt need people to become prompt engineers, Eric said. We needed them to think more boldly and trust each other enough to try. Through our work advising dozens of companies facing similar dynamics, Kathryn, as an executive coach and keynote speaker, and Jenny, as an executive advisor and Learning & Development expert, we have seen a clear pattern: high-performing teams in this new era follow three simple but powerful habits. 1. Build Skills That AI Cant Replace AI can assist and accelerate work, but it cant replace sound judgment, real curiosity, or ethical discernment. These are distinctly human strengths, and according to a 2025 report by the World Economic Forum, they rank among the top emerging skills for the future of work. What separates high-performing teams is their ability to use AI as a starting point, not a crutch. They ask sharper questions. They challenge assumptions. They make connections across functions and domains. These are not just soft skills; theyre power skills that generate meaningful insight and influence. After launching the AI champions circle, Eric noticed something unexpected. The group wasnt just surfacing use cases; instead, it was improving how the company framed problems. The questions got better, he told us. It wasnt just, What can this tool do? It became, What are we trying to solve, and how could this help? Eric began encouraging teams across the business to build that same muscle. They shifted from asking what AI could do to asking where their thinking mattered most. Team members started exploring: Where does this work require human judgment? Where are we relying too heavily on automation? What are the tradeoffs we need to weigh? That shift gave people permission to think more critically, not just execute faster. The result wasnt just their output. It was their judgment. Teams grew more confident in evaluating options, analyzing risks, and owning what only they could uniquely contribute. 2. Focus on Outcomes, Not Optics When AI speeds up how teams generate content, analyze data, or respond to requests, its easy to confuse motion with impact. Leaders may see faster email replies, more polished updates, or nonstop activity on project trackers, but that doesnt mean the team is aligned or productive. High-performing teams focus less on the illusion of productivity and more on strategic clarity, a key element of organizational health. According to McKinsey’s Organizational Health Index, companies that consistently communicate direction and hold teams accountable outperform their peers. Googles team effectiveness research reinforces this: structure and clarity is a key condition for team success. One leader we worked with, Cheryl, the head of customer experience at a fast-growing SaaS company, noticed her team was delivering with greater speed but less depth. They were generating AI-powered customer sentiment reports, support email templates, and team dashboards, but rarely stepping back to assess if those outputs were actually solving the right problems. Everything looked efficient on paper, she told us, but we werent moving the needle on what mattered. To refocus, Cheryl launched a lightweight Slack ritual called Output vs. Outcome Fridays. Every Friday morning, team members posted one thing they worked on, and a sentence about how (or whether) it advanced a core goal. It gave the team a way to pause and reconnect their effort with their purpose. Over time, this mitigated performative work and reinvested that time into customer-centric improvements. Pro tip: Start the feedback cycle. Now that your team has a rhythm for internal reflection, add an external feedback loop by connecting with your customer whether thats an external client or an internal partner like sales, finance, or HR. Have each team member identify one stakeholder to engage in real-time input. Start with a quick 2-3 question check-in, and add quarterly conversations to deepen the learning. This helps tie your outcomes to the experience of the people you serve and ensures your efforts create meaningful impact. 3. Leave Room for Strategic Play AI can tempt teams to over-optimize for efficiency, often at the expense of creativity, judgment, and long-term thinking. But without space to think and try, teams become reactive, not strategic. Weve seen the most effective teams treat curiosity like a business advantage. They embed space to explore like dedicated days of the week to experiment with new tech, or sprint retrospectives where team members share quick experiments and insights. They carve out time to try new tools, test ideas in low-stakes ways, and share what theyre learning. That kind of structured experimentation isnt a distraction; its a discipline. And it often surfaces key insights long before any formal pilot begins. McKinseys research shows that cultures rooted in learning and innovation are more adaptive and resilient, especially during periods of disruption and uncertainty. Cheryl applied the same thinking to how her team explored AI. She introduced a recurring segment to her teams sprint retrospectives called AI Experiments. Each week, a different team member shared one thing they tested: a new prompt, a time-saving tool, or even a failed experiment. The goal wasnt to be right; it was to get curious. This created a low-stakes, high-learning environment. People started volunteering ideas, sharing small wins, and building on each others discoveries. The team didnt just become more efficient. They became more creative, collaborative, and resourceful, increasing their collective confidence. Pro tip: Create a shared AI experiments tracker. Start a lightweight hub (like with Notion, Google Docs, or a dedicated Slack channel) where team members can log quick notes on what theyre testing. Keep it simple and informal, no slides, no pressure. Suggested fields: Tool or prompt used What worked What didnt What wed try next The goal is to normalize small bets, shared learning in real time, and build momentum across the team. The Real AI Advantage is Human AI is only as powerful as the people who use it with intention. The most effective teams arent winning because they have mastered the latest tools: they stand out because they have adopted the right habits, redefining how they think, decide, and learn together. They prioritize judgment over automation, experimentation over perfection, and shared purpose over performative productivity. What gives teams a lasting edge isnt access to better technology. Its the courage to slow down, ask better questions, and lead with what only humans can bring: discernment, trust, and adaptability. In the age of AI, your competitive advantage isnt artificial. Its deeply human.
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E-Commerce
As more companies demand employees spend more days in their workplaces each week, some critics claim that tightening return to office (RTO) rules in part aim to provoke resignations from employees unwilling to give up their remote or hybrid work arrangements. Data now suggests employer use of that indirect quiet-firing manner of reducing head count is far more widespread than previously suspected and often involves tactics that go beyond ordering people back to their desks. Reinforced RTO mandates, especially by large companies like Amazon and Starbucks, sparked accusations that management’s tighter in-office requirements are a cover for pushing flexibility-loving workers to quit. It turns out, businesses are also using other methods to trim head count, including cutting worker benefits, increasing workloads, delaying pay raises, or gradually isolating targets in the workplace until they resign. A recent survey of 1,128 U.S. business leaders by CV writing platform ResumeTemplates found 42 percent of respondents admitted to having used those quiet-firing strategies this year, with an additional 11 percent saying they plan to do so in coming months. The main reason that total of 53 percent of participants said theyd employed the stratagem was to avoid severance, legal, and other costs that layoffs usually generate. They also credited the ruse with averting the bad blood and even worse reputational damage that firing people can create. In addition to establishing that a small majority of employers have or intend to resort to quiet firing this year, the survey also identified the most common methods that managers use to passively push workers toward the door. Delaying promised raises topped the list, with 47 percent of participants citing the tactic. That compared with 46 percent of respondents who said theyd tightened RTO or other workplace rules, 45 percent who increased workloads without compensation, and 32 percent who cut wages or benefits to sour employees on their jobs. Micromanaging workers or, inversely, cutting them out of projects and consultations entirely was another method mentioned, as was ignoring toxic environments that undermine the well-being of people designated for departure. The scenarios for which the passive maneuver to lower head count was used varied. Around 41 percent of participating managers said theyd deployed it against specific employees theyd deemed troublesome, problematic, or simply unwanted. That included 47 percent of respondents who said theyd embraced quiet firing to get rid of underperforming employees, and 41 percent whod used it with staff who complained about or resisted strengthened RTO rules. Just over a third of participants said the method allowed them to sidestep severance payments that often accompany formal layoffs, with an equal 34 percent crediting it with lowering the attendant legal headaches. Around 32 percent said the passively manipulative approach allowed them to avoid the bad external publicity that can arise when businesses cut staff. But company owners and executives who answered the survey also noted there were broader economic and business reasons that made lowering head count necessary. In many cases, quiet firing was ultimately selected as the means to achieve that. The most frequently cited of those external pressures was slowing revenue and sales, at 50 percent. Adjusting to increased costs from import tariffs was second, at 46 percent. Nearly 40 percent of participants said staff reductions had already been or will be made this year in anticipation of a recession, and the same percentage pointed to wage inflation as a factor in cuts. But in spite of the frequent and increasing recourse to quiet firing, its results received mixed reviews from survey respondents. A whopping 85 percent of participants called it a somewhat or very effective way of influencing employees to leave with lower costs and legal hassles. But 98 percent of respondents acknowledged the technique also had a negative effect on workplace morale, with nearly 40 percent describing that erosion as considerable. For that reason alone, Julia Toothacre, ResumeTemplates’ chief career strategist, warned that quiet firing is a double-edged sword employers should think entirely through before using. From a business perspective, quiet firing can seem like an efficient way to reduce head count without triggering layoffs, bad press, or severance costs, Toothacre said in comments about the survey’s results. But its shortsighted. Creating an environment that pushes people to quit inevitably damages morale, productivity, and trust. It can also negatively impact hiring in the future. And lets be honest, when this tactic is applied broadly, companies risk losing high performers, not just underperformers. Another possible result is businesses not losing anyone at all. Survey respondents reported that employees targeted by quiet firing dont always quit as hoped especially in an increasingly challenging labor market. Indeed, 77 percent of participants said some of their quiet-firing efforts had failed when workers decided that finding a new job elsewhere would be more arduous than roughing it out where they were. One reason for that fatalism, 53 percent of responding managers said in their rather depressing view of the modern workforce, is that a large number of current workers simply tolerate poor treatment rather than quitting. That may wind up leaving manipulating executives with more than they bargained for. Because refusals of targeted employees to quit can have potentially negative consequences for the workplaces they remain in and the employers who wanted them gone. Many workers will stick it out right now not because theyre engaged, but because the job market feels uncertain, Toothacre said. Theyre weighing the stress of a toxic workplace against the risk of landing a new job that pays less. This calculator puts employees in survival mode, which will ultimately impact productivity. In other words, although quiet firing is spreading among businesses, its lower costs and confrontations up front may well be offset by lower workplace happiness and productivity over time. By Bruce Crumley This article originally appeared on Fast Company‘s sister publication, Inc. Inc. is the voice of the American entrepreneur. We inspire, inform, and document the most fascinating people in business: the risk-takers, the innovators, and the ultra-driven go-getters that represent the most dynamic force in the American economy.
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E-Commerce
The global race for better batteries has never been more intense. Electric vehicles, drones, and next-generation aircraft all depend on high-performance energy storageyet the traditional approach to battery R&D is struggling to keep pace with demand. Innovation and investment alone wont solve the problem, unless we compress the timeline. Speed is now the defining barrier between potential and impact. Even as AI speeds up materials discovery, battery lifetime still dictates success: each charge-discharge cycle lasts about six hours, so proving out 500 cycles can take up to eight months, turning lifetime testing into the key bottleneck for promising chemistries. Thats changing. Physics-informed AI is redefining battery development. National labs like NREL have shown how neural networks can diagnose battery health 1,000 times faster than conventional models, bringing real-time insight into degradation and performance. The Real Cost of Traditional Testing Battery development has always been a waiting game. Consider the mathematics: testing at a standard C/3 rate allows for just two complete cycles per day. Multiply that across different chemistries, protocols, and form factors, and you’re looking at years of validation before a single product reaches market. This isn’t just inefficientit’s becoming unsustainable. While battery researchers methodically work through their testing cycles, the market landscape shifts beneath them. New competitors emerge, customer requirements evolve, and breakthrough technologies risk becoming obsolete before they’re even validated. The industry needs a fundamental shift in how it approaches innovation. Why Conventional AI Isn’t the Answer Many companies have turned to traditional machine learning, hoping to accelerate their development cycles. But conventional AI tools face critical limitations in battery applications: Data scarcity: Unlike consumer tech, battery research generates relatively small, messy datasets that resist standard ML approaches. Black box problem: Correlation-based models might identify patterns, but they can’t explain why those patterns exist, which is a nonstarter in a field governed by strict electrochemical and thermodynamic principles. Regulatory challenges: Engineers and regulators need to understand not just what an AI predicts, but why it makes those predictions. Enter Physics-Informed AI Physics-informed AI represents a fundamental departure from conventional approaches. Instead of learning patterns from data alone, these models embed physical laws directly into their architecture. The result is AI that doesn’t just recognize correlationsit correlates with the underlying physics. This approach transforms how we think about battery development. Rather than waiting months for empirical validation, physics-informed models can simulate real battery behavior with remarkable accuracy. They account for aging, degradation, thermal stress, and mechanical factorsall grounded in established scientific principles. At Factorial, we’ve achieved something that seemed impossible just years ago: predicting cycle life outcomes after just 12 weeks of early testing, compared to the 36 months typically required. Software-Driven Breakthroughs The impact extends beyond faster testing. Using our newly launched Gammatron platforma proprietary physics-informed AI systemwe recently optimized a fast-charging protocol without altering any physical components. The result: a twofold improvement in cycle life, achieved entirely through software. Gammatron, developed to simulate and predict battery behavior with high accuracy, has transformed our approach to development with Stellantis. By forecasting long-term performance from just two weeks of early data, the platform helped accelerate validation timelines and informed protocol adjustments that significantly extended battery lifespan, without changing chemistry or hardware. Were not the only ones seeing this level of transformation. At The Battery Show Europe, Monolith CEO Richard Ahlfeld shared that his company, working with Cellforce Group, is using AI to reduce battery materials testing requirements by up to 70%, while maintaining or even improving discovery rates. These aren’t theoretical savings. Monolith reports 2040% reductions in testing across active partner projects today, accelerating products to market by months. This represents a new paradigm in battery developmentone where software innovations can drive hardware-level gains. As our models continuously learn from new lab data, they evolve in real time, accelerating innovation throughout the entire product lifecycle. his combination of AI and lab data enables a feedback loop that isnt seen in traditional AI models. Transforming Industry Standards Physics-informed AI enables capabilities that were previously impossible: Precision matching: Align specific chemistries with target applications based on predictive performance modeling rather than trial and error. Virtual prototyping: Simulate performance outcomes before investing in physical prototypes, dramatically reducing development costs and timelines. Intelligent optimization: Fine-tune charging protocols for optimal speed and safety without extensive physical testing. Predictive monitoring: Identify potential failure modes early in the development cycle, reducing both risk and cost. Perhaps most importantly, these tools support continuous learning throughout the product lifecycle. As new materials, processes, and data become available, the models evolve, enabling rapid adaptation across diverse battery platforms and applications. The Simulation-First Future We’re witnessing the emergence of a new development paradigmdigital cell design. Tomorrow’s battery breakthroughs will begin not in physical labs, but in sophisticated simulations that combine domain expertise, experimental validation, and intelligent AI modeling. This shift from hardware-first to data-first innovation will separate industry leaders from followers. Companies that can seamlessly integrate these capabilities will unlock longer range, faster charging, and greater resilience, solving what are fundamentally systems challenges rather than just materials challenges. The tools exist today. The question isn’t whether this transformation will happen, but how quickly companies will adapt to leverage these capabilities.
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E-Commerce
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