Executives following quantum computing may think it's best to wait for technical breakthroughs before investing. However, leading companies view it as an enabling technology where business users play a vital role in shaping its value through ongoing experimentation. While milestones like qubit counts matter to engineers, they don't dictate when companies should engage. Economic value from quantum computing emerges gradually via collaboration between technology producers and user-driven innovations, much like electricity and classical computing did.

Quantum computing's true impact depends on active user involvement revealing valuable applications and performance needs through repeated feedback loops. This creates a strategic challenge: companies hesitate to experiment due to uncertain short-term gains, yet such experimentation is essential to discover profitable uses and guide technical progress. Early engagement allows firms to influence development priorities and prepare their processes for future quantum advantages.

Examples include Lockheed Martin's early investment in quantum annealing systems and IBM's cloud-based quantum platform, which broadened access and accelerated learning. Organizations across sectors experiment with quantum approaches to solve optimization and operational problems, often yielding quantum-inspired improvements on classical hardware.

To develop effective quantum strategies, businesses should appoint boundary spanners linking technology to company problems, focus on near-term opportunities that enable learning and experimentation, and create organizational spaces for longer-term innovation beyond immediate financial returns. This approach transforms quantum computing adoption from a passive wait to an active, ongoing process of discovery and adaptation, positioning companies to seize substantial value as the technology matures.

On a rainy Tuesday in London, a consumer goods company's leadership team tackled two key decisions: where to open the next five stores, and whether to pivot the brand towards wellness. Generative AI supported both discussions, yet the results exposed a critical issue — not all AI fits every decision equally. Narrow decisions like store locations have clear objectives and measurable outcomes, making them suited for analytical AI. Wide decisions like brand pivots involve ambiguity, contested goals, and require alignment; generative AI can help synthesize perspectives but cannot replace judgment or commitment. The article explains how organizations often misapply AI by treating all decisions the same, leading to weak results or poor buy-in. Leaders are encouraged to diagnose decisions as narrow or wide using criteria like objective clarity and data readiness, then align AI tools accordingly — analytical engines for narrow decisions and generative helpers for wide ones. Practical steps include inventorying decisions, tailoring AI playbooks, and ensuring decision processes incorporate review and alignment. Ultimately, AI's value depends on matching its strengths to the nature of the decision to drive impact and organizational commitment.

In this episode of Me, Myself, and AI, host Sam Ransbotham interviews Vineet Khosla, CTO of The Washington Post, about the newsroom's transformation through artificial intelligence. Khosla shares how AI is enhancing journalism by enabling personalized news delivery, AI-powered research tools for reporters, and interactive news experiences that explain the significance and context of stories. He emphasizes that journalism as a discipline remains strong, even as consumption formats rapidly evolve with technology. Khosla discusses the challenges of personalization, balancing user interests without creating echo chambers, and maintaining journalistic integrity. He highlights projects like personalized AI podcasts and advanced video analysis tools that assist journalists in uncovering important stories faster. The episode explores the philosophy of “AI everywhere” at The Washington Post, integrating AI throughout the organization while ensuring transparency and trust with their audience. Khosla also reflects on his career journey from AI and natural language processing at Apple and Uber, to leading technological innovation in journalism. The conversation closes with a forward-looking view on how AI will continue to reshape news consumption and the vital role of trust in the future media landscape.

For decades, success in business meant having the best ideas—better ideas led to superior products, more customers, and increased profits. Consulting firms and innovation experts thrived on this belief. But with the advent of generative AI, idea generation has become trivial and accessible to anyone. GenAI can produce countless product concepts instantly, making ideas as commonplace as electricity, which is not a competitive differentiator. The real advantage now lies in how companies identify and frame the problems they aim to solve—what’s called “Question Zero.” The challenge is that most organizations fail to see the right problems because users can’t articulate the underlying frustrations they feel. Traditional research methods only capture surface-level issues, leading to incremental improvements and commodification. AI helps by revealing behavioral patterns at scale that humans can’t easily detect, enabling companies to uncover deeper insights for reframing problems. Examples from startups like Cursor, Speak, and Fireflies.ai demonstrate how reframing a problem rather than generating more ideas leads to breakthrough products and market leadership. Established companies such as Netflix, Duolingo, and Procter & Gamble leverage AI similarly to unearth hidden truths about customer behavior, prompting strategic shifts that redefine their businesses. To leverage this advantage, businesses should focus on identifying the gap between what customers say and do, regularly audit their current problem assumptions, and use AI not just for generating ideas but for exploring alternative problem framings. Ultimately, the scarcity in innovation today is not ideas but the insight that guides the right problem to solve, empowering companies to build products and services competitors haven’t imagined.

The global political landscape is undergoing profound changes marked by conflicts in the Middle East and Ukraine and evolving U.S. trade and foreign policies that disrupt previous alliances. These shifts expose weaknesses in globally optimized supply chains, challenge multinational data governance, and redefine neutrality in geopolitics. Companies face complex choices—whether to exit, reorganize, or relocate—to remain resilient amidst rising geopolitical risks. Examples range from strategic minimal presence in risky markets to adopting polynational structures that emphasize local autonomy and embedding, which better insulate firms from political shocks. Companies like HSBC and Nestlé have restructured for greater regional adaptation, while others like Apple and Microsoft engage in corporate diplomacy to safeguard interests globally. Relocation strategies now focus more on compliance and risk mitigation than simply cost efficiency, with moves such as near-shoring and friend-shoring becoming more common. This era of fragmentation within globalization requires firms to reinvent their operations, balancing political realities with the need for economic interdependence and agile corporate governance.

Across decades of working life, it is not just steady career progression that shapes us, but the impactful moments of adventure—those periods of stepping away from the familiar, diving into uncertainty, and embracing new challenges. These experiences, often perceived as distractions at the time, turn out to be crucial for personal and professional growth. They disrupt ingrained routines, broaden our sense of identity, and create meaningful markers that link our past, present, and future selves. As lifespans and work years extend, the traditional career model based on continuous progression no longer fits. Instead, a multistage career model emerges, one that thrives on reinvention and exploration throughout life. Yet, this runs counter to organizational structures that favor consistency and predictability, making adventure a rare but essential ingredient for adaptability and sustained engagement in work. To support longer working lives, both organizations and individuals need to reconsider how they make room for adventurous experiences—not as indulgences but as vital components for growth, resilience, and transformation. Adventure is what keeps a career vibrant and flexible across decades, encouraging us to explore, learn, and reinvent ourselves continuously.

Carolyn Geason-Beissel explores how leaders can manage organizational chaos, drawing from personal experience and professional insights. The article discusses the challenges posed by a chaotic workplace, including competing priorities and bad behaviors that worsen during stressful times. Key strategies include maintaining ongoing communication between interconnected teams, preserving time in meetings for spontaneous discussion, actively discouraging toxic behaviors, and recognizing the potential growth and opportunities chaos can bring. The narrative emphasizes that while chaos is inherent in modern organizations, effective leadership can transform it into a catalyst for development, cohesion, and even humor among teams.

Democracy is facing serious decline worldwide, with the number of autocratic countries increasing significantly by 2025. Business leaders acknowledge the importance of democracy to economic strength, yet many feel uncertain about how to combat rising authoritarianism. Democracy offers businesses clear legal frameworks, regulatory transparency, and protections for economic freedom—all essential for innovation and market stability. Conversely, authoritarian regimes impose political loyalty and limit these freedoms, damaging economic potential and workplace vitality. With the advancing power of AI, the risks to democracy are intensifying, as authoritarian regimes use technology for mass surveillance and control. Business leaders hold unique power and responsibility to defend democratic institutions by supporting free elections, independent judiciary, and civil society organizations, while avoiding undue political influence. They should promote transparency in AI deployment and foster democratic practices within their organizations, enhancing employee participation and modeling democratic values internally. Such collective action across industries strengthens the conditions for long-term business success and societal health, emphasizing that protecting democracy is both a moral duty and a strategic necessity.

For the fifth consecutive year, MIT Sloan Management Review and BCG have gathered global AI experts to explore how responsible AI (RAI) is practiced across industries. While past discussions concentrated on AI maturity, risks, and governance pillars like accountability and transparency, rising anxieties about AI's effects on employment have come to the forefront. Many companies have reported layoffs citing AI-driven efficiency as a reason, prompting calls to consider workforce implications as a critical part of responsible AI.

A strong consensus among experts emphasizes that responsible AI should not only address system safety and bias but also acknowledge its sociotechnical nature—how AI reshapes jobs, skills, power dynamics, and economic stability. Workforce impact must be integrated into AI governance at all levels, including board decisions. Neglecting this can lead to serious social and economic challenges, including worker displacement, increased inequality, and reduced consumer trust.

Experts advocate for comprehensive strategies involving reskilling and transparent communication with workers, though they highlight that human learning may lag behind technological advances. Responsibility for managing AI’s workforce effects is shared across businesses, governments, and policy makers. Without deliberate leadership and proactive policies, the negative consequences on workers and society may outweigh the benefits gained from AI efficiencies.

To address these challenges, organizations should broaden RAI frameworks to include workforce considerations, embed workforce metrics in AI strategy, evaluate worker impacts alongside technical risks, involve employees in decision-making, and designate clear accountability for workforce-related outcomes.

In a detailed conversation on the Me, Myself, and AI podcast, Peter Koerte, Siemens’ managing board member and chief strategy and technology officer, sheds light on how industrial AI is transforming the infrastructure that underpins daily life. Unlike consumer AI, industrial AI emphasizes near-perfect accuracy and works intimately with specialized domain data to enhance factories, transportation systems, energy grids, and buildings. Koerte explains Siemens’ role in optimizing these vital systems, citing examples like forecasting train door malfunctions well in advance and cutting building energy consumption by 30%. They also discuss the critical role of domain expertise, the challenges of proprietary data sharing, and the significant workflow changes industrial AI brings, including the use of smart glasses to empower workers. Collaboration with tech leaders like Nvidia aims to accelerate simulations and chip design, highlighting the broad impact of AI-driven innovation in industry.