We live on a planet covered mostly by water, yet only a tiny fraction of it is usable fresh water—about 0.5%. Growing global demand, population increases, pollution, and climate change are straining this vital resource, making water scarcity and quality urgent concerns for companies and communities alike.
Many businesses currently treat water as a basic utility rather than a strategic asset. This mindset is increasingly risky as droughts, pollution, and regulatory pressures mount globally. Water-intensive industries such as AI data centers, green hydrogen production, mining for critical minerals, food and beverage, and textiles are facing growing challenges. Even smaller businesses and sectors like construction, healthcare, and tourism cannot afford to ignore water risks.
Effective water management requires understanding complex local water cycles and dependencies along supply chains. Uniform policies are insufficient; companies must empower site managers with localized risk assessments and stakeholder engagement.
Strategic responses include shifting water from a utility expense to a priority managed cross-functionally, leveraging tools and standards like the CEO Water Mandate and Aqueduct, investing in efficiency and recycling technologies, and contributing to water source replenishment efforts. Ignoring water’s strategic importance heightens risks to health, economic stability, and corporate resilience.
Most leadership guidance focuses on beginnings—starting initiatives, building momentum, and scaling ideas. However, how leaders handle endings is equally crucial. Ending moments—such as closing a team, project, or partnership—are often rushed or treated as mere administrative tasks. Yet, these endings reflect a leader's values: honesty, respect, dignity, and preservation of what truly matters.
Leaders should clearly and plainly communicate what is ending, avoiding euphemisms. Providing a straightforward explanation without overexplaining helps people understand the reasons and consequences without feeling manipulated. It's important to honor the achievements and impact of what is ending before shifting focus to the future.
Ambiguity during endings causes stress, so leaders must clarify what is known and what remains uncertain. Emotions will be mixed—pride, anger, relief, disappointment—and leaders should allow space for these feelings without forcing a positive spin.
Preserving valuable knowledge, lessons, and relationships prevents the loss of meaning after closure. Finally, endings should be marked with dignity, offering clarity and recognition rather than just efficiency. Good endings show that work mattered, truth was told, and people’s dignity was protected.
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The Research
This article draws from two key research streams: a qualitative study on AI-assisted discovery highlighting four pathways AI uses to generate insightful analytical breakthroughs, and an examination of organizations as algorithmic assemblages—shaped by data accessibility, configured capabilities, and agency distribution. The core value of professionals lies in generating insight—a fresh perspective or connection that reshapes understanding. Expert knowledge, while powerful, can limit discovery by framing what is seen and overlooked. AI, particularly agentic AI, moves beyond simple prompting to proactive, configuration-based analysis that sustains deep, systemic insight.
Two Ways of Working With AI
Most users engage AI conversationally—asking, refining, and interpreting responses. Agentic AI requires configuring context, capabilities, and orientation to enable persistent, autonomous analysis. This allows professionals not just to prompt but to direct, orchestrating multiple agents that analyze the same data from different perspectives, producing richer insights.
Four Discovery Approaches
- Use Multiple Lenses: Apply competing frameworks simultaneously to reveal contradictions and new questions.
- Surface Silences: Identify vital organizational issues not named in official discourse by comparing data and conversations.
- Bridge Levels: Trace problems from symptoms to root causes across organizational layers.
- Stress-Test Categories: Compare formal classifications with operational reality to uncover hidden problems.
Skillful Directing
Effective AI direction requires designing systems for discovery, valuing unexpected findings, evaluating patterns as proposals, and tracking analytical decisions. Agentic AI enhances but does not replace professional judgment; the best outcomes come from framing problems to reveal new insights and leveraging AI as a powerful tool for discovery.
Since early 2025, some organizations, influenced by the current U.S. administration, have dismissed earlier commitments to diversity, equity, and inclusion (DEI), claiming their hiring practices are now purely meritocratic. However, portraying DEI and merit as opposites ignores that abandoning DEI doesn’t guarantee merit-based hiring. Effective DEI practices expand recruitment beyond traditional networks without replacing talent assessment with demographic criteria. In reality, merit-based hiring is often elusive and filled with errors, despite good intentions. "Merit" in hiring is actually about validity—selection methods must be proven job-related by scientific standards. Many commonly used criteria like educational requirements, years of experience, or subjective impressions often lack validity and favor majority groups, creating a "merit mirage." True merit requires using validated, standardized assessments, but many managers rely too much on intuition and unstructured interviews, which are less effective. Additionally, valid assessments may lack face validity, leading to skepticism, which can hinder their adoption despite their effectiveness in promoting diversity and merit simultaneously. Leaders should invest in HR expertise, embrace empirically supported methods, and understand that diversity and merit are not mutually exclusive but can coexist to strengthen hiring outcomes.
Marketing capabilities—comprising the essential skills, processes, and organizational knowledge that drive customer-related initiatives and adaptability—are widely considered crucial for business success. However, the rapid integration of AI technology is reshaping marketing practices, particularly in content creation, customer targeting, and performance measurement, introducing new roles such as managing generative engine optimization (GEO).
For 18 years, The CMO Survey has tracked marketing trends, and its latest 2026 edition, which gathered input from 308 U.S. marketing leaders, reveals troubling insights. Marketing requirements are evolving faster than ever due to AI, yet marketing teams are struggling to build and sustain the necessary capabilities. This disconnect presents a paradox: companies acknowledge the importance of training and hiring to develop marketing skills but are consistently cutting budgets and headcount, undermining their own progress.
Despite recognizing agility as a key success factor, marketing teams excel more at responding to immediate challenges than at investing in the capabilities that would enable smoother, more strategic responses in the future. Seven core obstacles fuel this paradox, including a widening gap between technology adoption and the ability to use it effectively, a strong present-focused mindset that leaves little room for future planning, weak partnerships with CFOs limiting investment, and a persistent preference for building capabilities internally even as resources dwindle.
AI adoption in marketing is rapidly increasing, but organizational readiness lags behind, exacerbating capability deficits. Moreover, marketers often justify capability investments solely by short-term ROI, ignoring their long-term strategic benefits such as competitive differentiation and talent retention. The resulting vicious cycle of underinvestment restricts marketing's ability to prove value, further weakening executive support.
To address these challenges, companies must protect capability investments from short-term budget cuts, reframe them as strategic assets critical to competitive advantage, and reconsider the internal build-only mindset by embracing external partnerships where beneficial.
Ultimately, the marketing capability dilemma reflects a strategic failure to invest in the very strengths that drive lasting business results. In an era defined by AI-driven transformation, overcoming this paradox is essential for maintaining competitive leadership and organizational resilience.
Over recent decades, growing attention has been given to how organizations and leaders can help employees cope with trauma, particularly childhood adversity known as adverse childhood experiences (ACEs). Approximately 60% of adults worldwide have faced at least one ACE, and these experiences profoundly influence employees’ sense of self, behaviors, and workplace relationships, often to their detriment. ACEs include various early-life challenges such as abuse, neglect, family dysfunction, and economic hardship, which cause lasting psychological and physiological impacts that affect adult functioning.
Trauma from ACEs can manifest as low self-worth, emotional distress, and challenges in job performance and relationships. However, neuroscience indicates that the adult brain remains adaptable through neuroplasticity, meaning workplace experiences can play a pivotal role in recovery. Supportive, stable, and predictable work environments that foster emotional regulation, autonomy, and positive social interactions promote neuroplastic changes that help trauma survivors develop healthier responses and build resilience.
Healthy workplaces offer safe and respectful climates, encourage employee agency, and provide empathetic leadership that helps employees manage anxiety, build self-esteem, and regain control. Such environments reduce defensive reactions, support healthy boundaries, and motivate personal growth. Leaders can cultivate these cultures by modeling respectful behavior, appreciating contributions, encouraging feedback, and offering strengths-based development. Simple, consistent micro-behaviors in daily interactions have powerful effects in healing trauma’s long-term effects and enhancing overall employee well-being.
Dung Hoang/theispot.com
The digital age has vastly expanded access to explicit content, and generative AI now enables users to create explicit images with alarming ease. Governments and the U.N. warn about the rise of illegal AI-generated deepfakes, particularly those involving minors. Despite these concerns, some tech leaders, like OpenAI's Sam Altman, have considered integrating adult content features into AI tools.
Our research investigates how AI-generated explicit content affects offline sexual crime, using Japan as a case study due to its detailed public crime data. Following the mid-2023 launch of ChatGPT’s mobile app and improvements in AI image generation, Japan saw a significant rise in reported rape cases, increasing from an average of 148 to 265 per month. This rise surpassed other violent crimes such as murder, suggesting a specific link to sexually motivated offenses.
Analysis of activity on a major Japanese online art community revealed that peaks in engagement with AI-generated adult images correlated with increases in rape cases, especially those involving minors. The findings suggest a disturbing connection between AI-generated explicit content and real-world sexual crimes.
While causation cannot be fully proven, the evidence raises serious concerns about the societal impacts of unrestricted AI-generated adult content. Policy measures like watermarking, strict age verification, and enforcement of existing child protection laws could mitigate these risks. The scale of the problem calls for urgent legal reforms and careful consideration from technology companies to prevent facilitating sexual crimes, particularly against minors.
At the 2025 Consumer Electronics Show, Nvidia CEO Jensen Huang predicted a rapid surge in humanoid robots similar to the rise of generative AI. However, our research indicates that humanoid robot adoption will be fragmented and uneven, driven by specialized roles, geographical factors, and human reactions.
Unlike generative AI, humanoid robots are not one-size-fits-all. Each robot is tailored for distinct tasks—whether caregiving requiring emotional sensitivity or warehouse work needing heavy lifting—resulting in varied designs and technology setups. This specialization means companies focus on different market niches rather than producing universal robots.
Geographically, adoption depends heavily on factors like labor costs, regulations, and demographics. Countries with aging populations and labor shortages, such as Japan and South Korea, are hotspots for investing in humanoid technologies. China leads in manufacturing volume due to its comprehensive supply chain and cost advantages.
Additionally, human responses to humanoids vary widely, affecting their acceptance and success. While some studies report positive emotional connections, others highlight discomfort, influenced by personality types and cultural factors. Building trust through empathetic design is crucial.
For organizations, success requires a clear, role-specific approach, targeting suitable geographies and paying close attention to human factors. Leaders should engage in partnerships across industries and prepare employees and customers for this new interaction to harness the full potential of humanoid robots.
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The authors adapted the Big Five personality test to help teams understand personality preferences and improve collaboration. They tested this approach with over 600 teams at IMD leadership programs and with 100 senior executive teams across Europe, Asia, and the US.
Medita Holdings’ executive team struggled with indecision and unfocused discussions despite seeming engaged. Their shared trait of strong intellectual curiosity led to unproductive debates and difficulty in prioritizing. The approach uses personality assessments as a diagnostic tool to foster open dialogue about team dynamics, encouraging members to understand and value their differences.
By plotting personality scores like constellations, teams visualize their collective traits—"team DNA"—that reveal both risks and benefits. Three key patterns can trap teams: like-mindedness causing blind spots, polarized traits that generate tension, and outliers whose unique profiles may be ignored or control group dynamics. Addressing these insights led to improvements such as assigning a "focus keeper" to keep discussions on track and restructuring teams to incorporate diverse perspectives.
Facilitated workshops on personality foster safe, productive conversations that promote self-awareness and mutual respect. Teams can preempt dysfunction or accelerate cohesion by sharing and discussing their profiles. The Big Five's open-access International Personality Item Pool makes this practical even without certified facilitators. Ultimately, understanding personality differences depersonalizes conflicts and enhances appreciation of each member’s strengths, enabling better teamwork and decision-making.
Erik Brynjolfsson challenges the common question about AI's impact by urging us to focus on how we use AI rather than what AI will do to us. In a conversation on "Me, Myself, and AI," Brynjolfsson, a leading economist at Stanford, discusses the critical role human choices, institutions, and organizations play in shaping AI's economic and social impact. He reflects on his earlier predictions about AI progress, the complexities behind AI adoption, and the importance of investment in complementary assets like new skills and business models. Brynjolfsson highlights recent research showing that AI's impact on jobs varies significantly by age and occupation, emphasizing the need to think beyond job automation to the creation of new opportunities. He advocates for mindful optimism, stressing human agency and values in directing AI's development toward economic growth and shared prosperity. The conversation covers the challenges of measuring AI's benefits, the J-curve effect in productivity adoption, and the balance between academic and industry roles in AI innovation. Brynjolfsson calls for active engagement from all stakeholders to navigate AI's transformative potential responsibly.
