In 2021-2022, CareRx experienced rapid growth by tripling its business through multiple acquisitions, each introducing different systems and cultures. This pace overwhelmed employees, leading to increased turnover and frustration. Leaders at CareRx recognized the urgent need to manage how employees experience change rather than just focusing on operational shifts.

The article emphasizes three key leadership practices to successfully guide teams through constant change:

1. Prioritize Open Dialogue: Leaders must actively listen and engage with employees, incorporating their feedback into change initiatives to foster trust and reduce resistance.

2. Create a Unified Change Narrative: Leadership teams need to agree on and clearly communicate a shared story about the company’s past, present, and future to avoid confusion and disengagement.

3. Manage Change With Employee Capacity in Mind: It’s crucial to sequence changes thoughtfully to prevent overwhelming staff, recognizing that trying and ending initiatives with transparency helps maintain motivation and productivity.

The experience at CareRx, Hilton, and other organizations illustrates how leaders who care about employee sentiment and foster communication can turn turbulent transformations into opportunities for growth and engagement. The pace of change won’t slow, but those who lead with empathy and strategy can empower their people to drive change rather than drown in it.

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If you're aiming for senior leadership but are told you're "not enough of a visionary," it's likely not about lacking vision, but rather about making your strategic thinking visible. With deep industry knowledge and experience, the challenge is not what you think, but how you demonstrate it. Boards and promotion committees judge based on what you consistently communicate in interactions, not what you hold in your mind.

High performers often excel in precise, operational responses, yet visionary leadership demands showcasing your hypotheses about the industry's and company's future. For example, if a board member asks about market share, go beyond figures: highlight why the region matters, the competitive landscape, and strategic steps ahead. This shows your reflective, future-focused reasoning rather than just present facts.

Visionary leaders tolerate ambiguity and continually test and adapt their assumptions. Make this thoughtful approach visible, connecting present challenges to future opportunities, and actively inviting others into your strategic thinking. By doing so, you demonstrate humility and rigor, earning recognition as a true visionary.

The MIT Sloan Center for Information Systems Research conducted in-depth research at Caterpillar, revealing critical lessons on the role of CEOs in driving successful data transformations. When Jim Umpleby became CEO in 2017, he aimed to boost profitable growth through digital services, but faced the challenge of fragmented and incomplete customer data. Recognizing data transformation as a strategic business initiative—not just an IT project—he committed to building an enterprise digital platform, Helios, designed to unify and manage data across the company.

Caterpillar's leadership set ambitious revenue goals linked directly to data initiatives, made senior executives accountable for data quality and usage, and fostered collaboration with internal teams and external dealers. The Helios platform consolidated data from silos, enabling seamless customer experiences like real-time fleet management and e-commerce. Alongside restructuring data governance, the company invested in AI to enhance data accuracy and unlock new capabilities.

By treating data as a corporate asset and involving top leaders, Caterpillar not only improved data quality and insight but also significantly grew its services revenue from $14 billion in 2016 to $24 billion by 2024. This case underscores that executive commitment, clear goals, collaboration, and strategic technology investments are essential to turning data into a competitive advantage.

B2B sales in the industrial equipment sector is fiercely competitive, requiring vendors to offer tailored value-based solutions that demonstrate clear benefits to customers. However, many companies struggle to scale these solutions beyond pilot projects. Industrial equipment manufacturers (IEMs) face challenges such as heightened competition and growing environmental regulations, pushing them towards value-based models that deliver profitability and sustainability. Successful value-based solutions are customized integrations of products, services, and digital technologies designed to improve operational efficiency and reduce environmental impact. Our extensive research involving 157 interviews and case studies of major global manufacturers and their ecosystems reveals that while initiating value-based solutions is feasible, scaling requires developing specific capabilities across two phases: establishing a strong strategic foundation and executing at scale through ecosystem management, financial viability, and market expansion. Key hurdles include aligning the value proposition with customer expectations, modularizing solutions efficiently, managing partner ecosystems, innovating revenue models, and adapting delivery organizations. Companies that methodically address these practices are better positioned to scale solutions profitably while meeting customer needs and regulatory demands.

In this episode of Me, Myself, and AI, Andrew Palmer, senior editor at The Economist, discusses the thoughtful experimentation with generative AI within organizations, highlighting how The Economist integrates AI to enhance editorial and publishing processes while maintaining high standards through human oversight. Andrew shares insights from his role hosting the Boss Class podcast, exploring management and workplace issues, particularly the nuanced effects of AI on jobs and productivity.

Andrew emphasizes the need for measured adoption rather than rushing headlong, noting both the promise and limitations of AI tools in their early stages. He recounts a personal experience of rapidly prototyping a style-checking tool using AI, which then required significant refinement and governance before practical deployment. The conversation delves into challenges like managing AI-driven workloads, maintaining quality, and avoiding organizational bottlenecks.

The discussion also covers recruitment, transparency in AI use, and the balance between automation and human judgment in hiring and management. Andrew advocates for systems thinking and self-awareness as essential skills in navigating the evolving workplace. Ultimately, the episode underscores the importance of nuance and clarity in integrating AI, promoting experimentation with caution and a strong grounding in human factors and organizational dynamics.

The rapid advancement of AI, especially generative AI, poses significant uncertainty about the future of entry-level white-collar jobs, with a large portion potentially disappearing soon. Industry leaders like Dario Amodei of Anthropic predict major reductions in hiring, while companies like Goldman Sachs are already cutting thousands of jobs each month. Former Google CEO Eric Schmidt raises concerns about AI's unpredictable capabilities and suggests extreme caution, even recommending halting AI development if necessary. This situation presents a dilemma where executives, insulated by their status, prioritize short-term profits and shareholder value, overlooking long-term societal impacts and the essential role of nurturing new talent. Drawing parallels to the sluggish corporate response to climate change, the author argues for a shift in corporate strategy to maintain jobs and invest in human capital rather than relying excessively on AI-driven automation. Companies that retain and develop their workforce may gain a competitive advantage in the future. The piece calls for thoughtful business decisions and policy intervention to manage AI's disruptive effects responsibly, emphasizing the importance of balancing innovation with collective well-being and sustainable economic growth.

Many everyday innovations—from curb cuts to automatic doors—originated as disability accommodations but now benefit everyone. Often, workplaces are designed around the idea of an "ideal" able-bodied worker, ignoring the needs of broader and aging workforces. When teams include people with disabilities, coworkers tend to spot inefficiencies and rethink how work is done to improve safety, ease, and effectiveness for all employees. Instead of just giving individual accommodations, redesigning tasks can reduce physical strain and errors for the entire team. For instance, replacing heavy lifting tasks with assistive tools benefits all workers by lowering fatigue and injury risk.

Research shows that exposure to disability prompts coworkers to generate more creative and practical solutions to improve workflows, workspace setup, communication tools, and brainstorming processes. Innovations like documented workflows, captioning in meetings, and structured idea sessions emerged from inclusive practices that address diverse needs, boosting productivity and collaboration.

Managers concerned about slowing down work or singling out individuals should focus on small, reversible changes aimed at improving jobs rather than spotlighting people. Involving employees who experience challenges, running assumption reviews on high-friction tasks, and treating accommodation requests as opportunities to redesign jobs can lead to more sustainable, inclusive workplaces. Embracing disability as a natural diversity factor inspires continuous, practical innovation that benefits everyone—in ways large and small.

Operational efficiency ensures financial success and customer satisfaction by optimizing resources and minimizing delays. However, highly efficient systems often lack resilience, making organizations vulnerable to disruptions. This tension is evident in industries like airlines, where tight schedules maximize asset use but increase the risk of delays that cascade through networks, frustrating passengers and harming reputations. Beyond airlines, supply chains, healthcare, and call centers face similar challenges balancing cost, throughput, and customer experience.

Our research reveals that efficiency and resilience are not mutually exclusive. Three strategies help organizations achieve both:

1. Measure What Matters to Customers: Instead of relying on traditional metrics that don’t capture true service quality—like airlines' on-time performance (OTP), which can be misleading—organizations should adopt customer-centric metrics. For example, tracking passengers’ actual travel times, including delays and missed connections, better reflects customer experience and incentivizes resilience alongside speed.

2. Avoid One-Size-Fits-All Buffers: Strategic use of buffers, tailored to risk levels and disruption impact, can prevent minor delays from escalating. Airlines, hospitals, call centers, and supply chains can all improve resilience and efficiency by allocating resources dynamically based on data and disruption likelihood.

3. Curate Customer Options: Offering too many choices increases complexity and risk. By narrowing options to avoid risky itineraries or scheduling slots, organizations can reduce delays and disruptions without significantly compromising efficiency or customer satisfaction.

Implementing these strategies requires data-driven insights, cross-functional collaboration, and balancing short-term efficiency with long-term reliability. Companies that design operations for both resilience and efficiency rather than reacting post-disruption will better meet rising customer expectations and thrive in competitive markets.

MIT Sloan Management Review and Boston Consulting Group (BCG) convened a panel of AI specialists to examine responsible AI implementation. They highlight that responsible AI goes well beyond just verifying system outputs—it's about integrating human judgment throughout AI's lifecycle. Human experts must interpret context, design evaluations, audit workflows, set usage thresholds, and decide when AI should or shouldn't be trusted. Eighty-four percent of these experts agree that without cultivating human proficiency to verify AI, responsible AI efforts fail.

Context is key and inherently human, as machines alone cannot fully grasp societal, cultural, or legal nuances. Human verification is crucial especially in edge cases and new scenarios where AI can break down. The erosion of human expertise risks organizational capacity to govern AI effectively, threatening accountability and safety.

Total reliance on humans to verify every AI output is impractical at scale. Instead, experts recommend a hybrid approach where automated tools extend human oversight, focusing human judgment on critical and complex AI decisions. Oversight and responsibility remain pivotal, with humans accountable for AI outputs and ethical governance.

Organizations should embed human verification at each AI development stage, balance automation with human judgment, invest in maintaining expert skills, critically evaluate AI learnings, and treat verification as a strategic priority. This comprehensive approach ensures responsible AI not only mitigates risks but supports sustainable, ethical, and effective AI deployment at scale.

Imagine you’re Gabrielle, a senior leader at a rapidly growing tech company, grappling with two top performers whose rivalry and behaviors threaten the team’s success. Internally, Gabrielle faces a push-and-pull between wanting to maintain harmony and delivering results. This tension reflects a deeper psychological truth: our minds consist of multiple, interacting parts. The concept of a "multiple mind" draws on psychological theories from Freud and Jung and guides leaders in understanding contradicting internal voices. The Internal Family Systems (IFS) model, developed as a therapy method, provides leaders with tools to identify, listen to, and harmonize these conflicting inner parts, recognizing that no part is inherently "bad" but trying to help in its own way. At the core is the "self"—a calm, wise center that can lead these parts to work together, fostering inner clarity and resilience. Leadership grounded in this self can balance compassion, courage, and clarity under pressure. Through a step-by-step IFS process, leaders can become aware of their internal voices, cultivate compassion toward them, and integrate their wisdom, ultimately improving decision-making and team dynamics. This approach transforms leadership from reactive to self-led, turning inner conflicts into opportunities for growth and effective action.