In this episode of the Me, Myself, and AI podcast, Bernard Hampton, head of Bank of America's Academy, discusses the banking giant’s comprehensive strategy to prepare over 200,000 employees for an AI-driven future. Hampton highlights the Academy's focus on blending technical skills with essential human qualities like empathy and judgment to cultivate workforce agility. He explains a three-tiered AI adoption approach tailored to different roles, ensuring employees become proficient in AI while retaining human oversight. Hampton also shares how AI-powered interactive simulations help employees practice real-world scenarios to enhance client service and decision-making skills. The conversation touches on the importance of internal upskilling, thoughtful AI deployment in a regulated industry, and fostering curiosity and continuous learning to stay relevant in a rapidly evolving landscape.

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A consulting manager found herself taking after-hours calls and late-night texts from anxious employees amid corporate uncertainty and AI transitions, exemplifying the growing expectations for leaders to show empathy and compassion. Across industries, employees' stress and worries about economic challenges and AI's impact are rising, increasing the demand for leaders to offer emotional support. However, women leaders disproportionately carry this emotional labor, often dedicating over 30% of their workweek to caring tasks such as listening to anxieties and offering encouragement — an invisible, unpaid 'empathy tax.' This care fatigue, stemming from constantly managing others' emotions, can lead to burnout and attrition among women in leadership roles. Research shows women are expected to demonstrate warmth and compassion, and when they don’t, face negative perceptions, further entrenching this unequal burden. The empathy tax manifests in absorbing others' emotions, being graded on compassion, and sacrificing time essential for core responsibilities. To counteract these effects, women need to set boundaries and reject harmful narratives about self-sacrifice, while organizations must assess care fatigue, support boundaries, and promote empathy in all leaders, including men. Only by recognizing and sharing emotional labor can organizations sustain empathic leadership and retain female talent.

This research, carried out between 2022 and 2025 by Kevin and Ivo alongside 23 Swiss companies, reveals how generative AI can be effectively scaled across diverse industries such as banking, insurance, healthcare, manufacturing, and consulting. Despite substantial investments in large language models (LLMs), many companies struggle to translate these technologies into broad, strategic advantages. The key to success lies in adopting three core practices: expanding use cases beyond isolated tasks, continuously refining these applications, and promptly discontinuing those that underperform.

A central concept introduced is the 'AI spine'—a novel organizational structure that integrates domain experts from different business units into a central team managing the AI portfolio. This flexible spine fosters coordination across functions, streamlines processes, and ensures ongoing improvement and compliance. Examples include a Swiss bank that significantly boosted customer service efficiency with generative AI tools, and a medical coding company that not only cut operational costs but also created new revenue streams by commercializing AI-driven coding solutions.

The AI spine is financed independently and overseen by C-suite executives, ensuring strategic alignment and agility in funding high-impact projects. It contrasts with traditional hub-and-spoke models by centralizing cross-unit business knowledge rather than dispersing technologists across units. This structure also embeds continuous feedback loops involving business owners, tech specialists, compliance experts, and end users to evolve solutions responsive to real-world needs.

While alternatives like GenAI units and squads offer some benefits, they often fall short of achieving enterprise-wide integration and sustained innovation. The research highlights the necessity for top-level commitment, cross-unit collaboration, clear metrics for value, and ongoing dialogue between technology and business stakeholders. Leaders seeking transformative impacts with generative AI must architect such collaborative frameworks to unlock and sustain economic value at scale.

Christian Gralingen discusses the challenges and strategies around governing artificial intelligence (AI) as it scales within organizations. From 2022 to 2025, in-depth interviews with senior leaders across major financial and regulatory institutions revealed that effective AI governance requires adaptive frameworks tailored to the type of AI system and associated risks. The paper identifies the necessity to shift from static compliance measures to integrating risk controls directly into operational workflows, incentivizing conclusive judgments across multidisciplinary teams, and fostering governance systems as dynamic, learning entities. Key risk areas arise both during AI development—such as data bias and model validation—and after deployment, where AI interacts with complex environments and human operators, leading to risks like model drift and propagation of errors across systems. Different types of AI require specific controls: rules-based measures suit narrow, static models, while adaptive systems demand ongoing alignment and propagation-risk management. Examples from banking and market surveillance highlight the importance of embedding governance in daily operations and ecosystem-wide cooperation. Ultimately, organizations that treat AI governance as an evolving capability, not a static checklist, will better manage risks and unlock the technology's full potential.

Christian Gralingen's research explores why artificial intelligence (AI) has not fundamentally transformed finance functions despite high expectations. Through multiyear action design research and extensive interviews with CFOs and senior finance professionals, the study reveals that the pace of AI adoption is hindered less by technology and more by the leadership practices within finance teams. AI promises enhanced forecasting, shorter closing cycles, and better risk management, yet many initiatives stall due to leadership dynamics that favor traditional control over experimentation and adaptive thinking.

Finance operates under a paradox: it must maintain reliability and control while simultaneously embracing curiosity and adaptability. AI can enable finance teams to detect early signals and experiment with new approaches, but success depends on leadership that encourages vigilance, routine experimentation, strategic scenario planning, and the sharing of innovative practices.

Examples from real companies show how shared responsibility for noticing change, legitimizing small-scale trials, framing future uncertainties through scenarios, and fostering the spread of useful innovations can help AI become integrated into finance routines. The CFO’s role is crucial in setting a tone that values learning and permits experimentation, which ultimately shapes whether AI remains a promising tool or becomes a transformative asset.

The research calls for a shift in leadership — from a top-down command model to one embracing shared leadership in everyday finance work — to fully realize AI's potential. Questions for CFOs include who feels empowered to spot emerging trends, how safe experimentation is encouraged, and how local innovations become widely adopted. Without these shifts, AI risks remaining on the periphery instead of driving meaningful change in corporate finance.

Jean-Christophe Jaunin took over as CEO of Nespresso North America in 2026, coming from a role as global chief customer and technology officer. In an interview at the NYU Stern Center for Sustainable Business forum, he discussed how Nespresso integrates sustainability into its business model. Jaunin emphasized that sustainability is integral to maintaining the coffee's quality and future-proofing the business against environmental changes. Over the past two decades, the company shifted from conventional farming, which weakened soil and flavor, to methods that safeguard biodiversity and enrich the ecosystem, enhancing coffee quality. Nespresso supports over 150,000 farming families through its Sustainable Quality Program, offering training, direct partnerships, and income diversification initiatives like beekeeping, which not only benefits the environment but also fortifies farmers' economic resilience. The company consciously chose aluminum capsules for their ability to preserve coffee freshness and be recycled endlessly, committing to costly yet necessary recycling efforts including infrastructure investments in multiple states to ensure capsules can be fully recycled. This sustainability focus is tightly woven into the brand promise and long-term supply strategy.

In the latest episode of the Me, Myself, and AI podcast, Carla Goulart Peron, Philips’s chief medical officer, discusses how AI is revolutionizing healthcare by enhancing access, refining diagnostics, and enabling clinicians to devote more attention to patients. Drawing from her experience in Brazil’s public health system, she highlights AI-powered imaging and remote collaboration as solutions to critical care gaps. Carla also addresses challenges like trust, bias, interoperability, and the importance of women's health data, offering a grounded global view on making healthcare more human through technology.

Multinational companies invest heavily in cultural training for expats, but recent research shows that knowing local customs plays a minor role in their successful adjustment. A comprehensive meta-analysis involving over half a million individuals revealed that the biggest challenges for expats stem from stressors like discrimination and navigating unfamiliar systems, rather than cultural differences themselves. Social support is critical, especially the backing of a direct supervisor. Supervisors provide key benefits: fostering a sense of belonging, clarifying roles, reducing bureaucracy struggles, and promoting inclusion to combat discrimination. For organizations looking to improve global talent outcomes, focusing on manager support is essential—training leaders to actively engage and support expats early on can make all the difference.

The human element has always been a critical vulnerability in security systems, and the rise of AI has intensified this risk by influencing human decision-making. Employees today face three main AI-related threats: personalized deception, the spread of confidently presented false information, and diminished independent reasoning as reliance on AI grows. To counter these risks, a straightforward protocol named "Think First, Verify Always" (TFVA) has been introduced. It encourages workers to first form their own opinions before turning to AI and to consistently verify AI outputs with independent sources. This approach strengthens judgment and verification habits, significantly lowering the risk of manipulation. In a study involving 151 participants, a brief three-minute training on TFVA boosted decision-making quality and ethical discernment substantially. Implemented at RSM France with positive early results, TFVA can be integrated into employee training and AI policy frameworks to foster a workforce that engages critically with AI-generated content, viewing it as a tool for informed decisions rather than an unquestionable authority.

How can businesses effectively manage the flood of consumer opinions? Recent research highlights three crucial insights. First, a significant gender gap exists in online reviews: women tend to give more positive ratings but often withhold negative feedback due to social pressures. Encouraging anonymous feedback can help bridge this gap. Second, feedback from user communities, especially niche markets like early-access video game players, may not reflect the broader customer base’s preferences. Acting too heavily on this feedback could alienate mainstream users. Finally, expert ratings, such as Michelin stars for restaurants, can raise expectations so high that customers become harder to satisfy, sometimes lowering their own ratings if those lofty standards aren't met. These findings show that customer feedback is complex and multifaceted, signaling that businesses must carefully weigh and interpret reviews rather than assuming the customer is always right.