Nicole Steller from the European Academy of Management, Albena Björck of ZHAW School of Management and Law, and Guido Möllering from Witten/Herdecke University explore the rise of the chief purpose officer role in organizations.
A recent survey by Bain & Co. involving 951 large companies highlights a significant gap between AI cost-saving expectations and actual outcomes, with executives finding results underwhelming.
Explore how adopting a firm no-negotiation stance on job offers can help create fairer pay structures and maintain equity. Learn effective strategies to communicate this policy persuasively to candidates, ensuring you attract and retain the best talent without losing out to competitors.
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.
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.
CEOs often avoid outright apologies for mass layoffs, but what they can do is stop justifying these tough decisions with excuses. This shift in accountability could transform how leadership handles workforce changes.
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.
Software development has become accessible to everyone, thanks to rapidly improving AI tools. What once took large teams years to build can now be delivered by small, intelligent groups in weeks. However, the true challenge lies not in building software quickly but in understanding what to build and why it matters to customers. The key differentiator in today’s market isn’t speed, but coherence — the alignment of an organization’s product, people, and strategy. Coherence ensures consistency in user experience and strategic decisions, which gains favor both with consumers and emerging AI agents that evaluate products digitally. Companies that maintain coherence gain competitive advantage, while those lacking it risk fragmented user experiences and lost loyalty. Essential questions for organizations include aligning team priorities, ensuring consistent product experiences, and evaluating digital presence integrity.
