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Artificial intelligence has shifted from an experiment to an expectation. Boards push CEOs about ROI. CEOs launch enterprise rollouts. Leaders invest in tools, platforms, and governance. Yet adoption still stalls. Work-arounds spread. Risk grows. Value lags. The failure rarely sits with the technology. The breakdown sits in adoption design. Many organizations treat AI as an IT rollout or a standard change initiative. Tools gain approval. Policies circulate. Training launches. Whats missing is the rigor leaders apply to external products. Employees receive tools without a clear value proposition. Managers face delivery pressure without added capacity. Governance favors control over learning. The result is predictable. Hesitation rises. Burnout grows. Execution fragments, especially in the middle of the organization. Dana, a VP leading AI enablement at a global business-to-business services firm, lived this firsthand. The mandate was clear: deploy approved AI tools across marketing, sales, and customer success within eight months. Legal and PR aligned. Training sessions were launched as well as dashboards to track usage. On paper, the rollout looked disciplined. Usage dashboards showed logins, prompts, and license activity. In practice, teams struggled to use the tools in live client work. Approved platforms added steps, limited outputs, or failed to match real workflows. Under delivery pressure, some teams tested briefly and moved on. Others complied superficially. Many shifted core work to external tools that felt faster and more flexible, while using approved systems only enough to register activity. Dana ran into what we call the mandate trap. Leaders mandate AI from the top. The work of making it usable lands in the middle. We didnt have a resistance problem, Dana reflected. We had a design problem. Her experience reflects what we see across organizations and in AI adoption workshops with C-suite and senior leaders. Teams revert to familiar workflows. Learning time disappears as daily delivery targets crowd out capability building. Worse, often, leaders label this gap as a resistance to AI, rather than identifying the underlying problems and solving them. Through our advisory work and research, Jenny as an executive coach and learning and development expert, and Noam as an AI strategist, we see three practices separate the organizations that are able to scale AI within their organizations from the ones that have stalled rollouts. Reframe ‘Resistance’ as a workflow problem Leaders often label hesitation as a mindset issue. In reality, hesitation reflects risk. Employees disengage when expectations are off, outputs feel unattainable, or policies feel unclear. Under delivery pressure, people choose speed and safety. When AI complicates execution rather than simplifying it, adoption stalls. Middle managers absorb the strain. They must deliver faster, coach new behaviors, manage risk, and hold uncertainty, without changes to incentives, capacity, or decision rights. Adoption breaks where pressure concentrates. The issue is not motivation. It is an internal product-market fit problem. Internal product market fit exists when a tool solves a real workflow problem well enough that teams keep using it under real constraints. This insight shifted Danas rollout. She stopped pushing compliance and paused deployment to focus on solving the problems internal teams were running into. What leaders can do: Diagnose hesitation: Identify where trust breaks. Unreliable outputs. Unclear revision paths. Slow approvals. Fix friction before pushing usage. Start small: Focus on one workflow, one outcome, one team learning together. Name the fear: Address job loss concerns directly. Clarify what stays human-led and how AI fits workforce plans. Psychological safety creates engagement. Relieve pressure: Protect learning time. Reset targets or adoption stays surface level. When leaders treat resistance as a design signal, adoption moves from compliance to progress. Treat Employees as ‘Customer Zero’ Leaders who succeed stop deploying AI and start selling it internally. Strong AI adoption follows a different playbook. Leaders anchor change in outcomes, redesign workflows, involve employees as cocreators, and invest in learning as a core capability. Dana pulled in platform teams, product marketing, communications, and functional leaders. Teams receive a clear value proposition tied to real workflow friction, not feature lists or policy decks. Trust grows when people understand how outputs form, how risks are managed, and where human judgment remains essential. Early wins rarely show up as profit. They show up as faster cycles, higher-quality work, fewer errors, and less rework. Tools gain traction when they simplify work. Dana ran short discovery sprints with marketing, sales, and operations. She stopped asking whether teams used the tools. She asked where work slowed, where rework piled up, and where judgment mattered most. What leaders can do: Anchor on outcomes: Define what should feel faster, easier, or more reliable. Build trust early: Set clear governance and human-in-the-loop guardrails. Reimagine workflows: Integrate AI into existing systems and execution moments. Cocreate with employees: Involve teams in discovery and testing. Treat learning as core work: Protect time to experiment and build confidence. When leaders treat employees as customer zero, adoption shifts from compliance to sustained change. Protect the Middle to Unlock Learning AI adoption breaks most often in the middle. Managers must change how work gets done while hitting the same targets. Meanwhile, managers drive most team engagement while carrying the heaviest strain. When learning competes with delivery, delivery wins. Effective leaders redesign these conditions. They reset expectations to protect time to learn. They reward experiments that reduce risk over time. Before scaling, they ask two questions: Does this remove real workflow friction? Do people trust it enouh to use it? Dana acted on this insight. She gave managers protected time to test workflows and share findings. Early wins became simple playbooks. Only proven practices scaled. Managers moved from firefighting to coaching. Governance shifted from gatekeeping to enablement. Dana narrowed focus instead of widening it. Teams submitted real workflow tests. Dana selected only those with clear impact and protected a full quarter to run them end to end. Some tools removed friction and earned trust. Others added noise. She scaled the winners and retired the rest. What leaders can do: Spot what works: Identify teams who are already using AI to reduce friction. Turn those efforts into repeatable practices. Reward learning: Recognize managers for building capability and sharing insights, not tool usage. Run disciplined experiments: Require clear hypotheses, small pilots, and documented learning. Hold the bar high: Reward honest reporting of failures so scale stays credible. AI transformation is an organizational design challenge, not an IT rollout. The mandate trap is avoidable. Leaders escape it when they stop pushing adoption and start earning it.
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E-Commerce
The biggest accounting firm in the U.S. just announced a major structural reset: PricewaterhouseCoopers (PwC) will now only hire new associates in its advisory division to work out of 13 offices, down from 72. Yolanda Seals-Coffield, chief people and inclusion officer for PwC US, confirmed the decision to Business Insider, explaining that the move aims to foster a sense of community among workers. “The idea is that we want to bring people together in a connected way for those first couple of years,” Seals-Coffield said. “You may start in Atlanta and then say, ‘Great, I’ve got my two years of experience. I want to go work in Alabama, which is where I’m from and where I really want to work,” she said. The slimmed-down choice of locations isn’t the only major change hitting the company. In recent years, PwC has delayed start dates for some entry-level consulting hires. And in 2025, it became clear that landing a job at the firm straight out of college would become more difficult; it announced it would recruit a third fewer new graduates by 2028. The company has also been making major shifts toward upskilling its workforce in the era of artificial intelligence. On February 5, PwC announced the launch of its “Learning Collective,” a workplace training initiative that it describes as “an ecosystem for accelerated growth built for the possibilities of the AI age.” Learning can no longer wait for the right time, place, role, or ladder, Seals-Coffield said in the announcement. It needs to be a full-immersion experience that accelerates people and their organizations forward with speed. Despite the positive spin on the company’s clear gear shift, it’s hard to imagine that the recalibration doesn’t signal an age of growing uncertainty within the industry. Some experts say it’s a response to economic uncertainty, as well as an ever-changing world that’s grappling with how to best integrate employee capabilities with AI advances. Deepali Vyas, global head of data and AI at global talent partner ZRG, tells Fast Company that in the AI age, “firms have to double down on what technology cannot easily replicate, including judgment, client presence, collaboration, and problem framing.” She adds that they must become “far more intentional about how they manufacture talent.” Overall, that seems to mean entry-level roles are seriously shrinking as tasks typically done by first-year hires are increasingly being handed to AI. For Gen Zers who are hoping to get a foot in the door, the problem feels unavoidable, as some reports estimate that entry-level job postings are down by 35% since 2023. PwC maintains that in a time when so many individuals work remotely for a good portion of the workweek, the move really is about employees getting back to learning from one another in a dynamic environmentwhich has become increasingly relevant during this post-COVID-19 era. Fast Company spoke with a PwC representative who pushed back on the narrative that the shift signifies an industry slowdown and said that employees and the company alike can make big strides with a more collaborative approach. Still, as searching for a job has become a truly anxiety-inducing part of lifeeven for the most competitive of college graduatesany amount of company downsizing is still going to read as a bad omen. When it comes to PwC, the major cut to office space is a highly visible one at that.
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E-Commerce
The legacy of Bad Bunny’s Super Bowl halftime show continues. Streams of his catalog jumped 175% in the U.S. on Monday, the day after the Super Bowl, when compared to the previous Monday, Feb. 2. Thats according to Luminate, an industry data and analytics company that provides insight into changing behaviors across music listenership. Bad Bunny received nearly 100 million streams on Monday in the U.S. that’s 99.6 million in one day compared to 36.2 million streams the previous Monday. That’s noteworthy, too, because Monday, Feb. 2 was the day after the 2026 Grammys, when the artist born Benito Antonio Martínez Ocasio won album of the year. It marked the first time an all-Spanish language album took home the top prize. And as a result, he was already seeing a significant jump in streams: On Feb. 2, his on-demand U.S. streams spiked 117% from the previous Monday, Jan. 26. And globally, Bad Bunnys on-demand streams increased 132% on Monday, Feb. 9, compared to Feb. 2, a difference of 271 million to 117 million. Bad Bunny’s most-streamed songs in the U.S. on Monday, Feb. 9 1. DtMF” with 10.4 million 2. Baile Inolvidable” with 6.7 million 3. NuevaYol with 6 million 4. Tití Me Preguntó with 5.4 million 5. EoO with 4.5 million On Monday, Apple Music, a Super Bowl halftime show sponsor, found that Bad Bunnys show playlist became the most-played set list on the music streaming platform shortly after the performance. The Puerto Rican superstar went on to dominate the Apple Music Daily Top 100 Global chart, landing 23 songs in the Top 100, including nine in the Top 25 and five in the Top 10. His track DtMF rose to No. 1. His album Debí Tirar Más Fotos appeared on album charts in 155 countries, reaching the Top 10 in 128 countries and hitting No. 1 in 46, including Mexico, Colombia, Chile, Brazil, Germany, France and Spain. Spotify found that U.S. streams of Bad Bunnys music jumped 470% on the platform. Thats when examining an hourly increase in U.S. streams between 9 p.m. and 3 a.m. ET on Sunday, Feb. 8, compared to the same time frame the week prior. And Amazon Music reported that streams of Bad Bunny’s music in the U.S. jumped 480% following his performance. Music discovery platform Shazam reflected a similar spike in engagement. Apple Music said Bad Bunny’s performance Sunday marked the biggest day ever on Shazam for any Latin or non-English-language artist. Across Bad Bunnys catalog, Shazam recognitions increased by more than 400% during and immediately following the halftime show compared to the daily average. By Maria Sherman, AP music writer Associated Press Entertainment Writer Jonathan Landrum Jr. contributed to this report.
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