In sectors where the buying process is complex and effortful, customers rarely go for the lowest price. They opt for the brand they trust. This fundamental shift means businesses must rethink how they build relationships and value beyond just pricing.

Norway’s Government Pension Fund Global, the largest sovereign wealth fund worldwide, has chosen not to vote on John Elkann’s reappointment to Meta’s board. The fund expressed doubts about whether Elkann, who is Stellantis chairman and Exor CEO, can dedicate sufficient time to the responsibilities at Meta. Norges Bank Investment Management, which oversees the $2.3 trillion fund, highlighted these concerns as part of its governance stance.

Getting promoted or earning more doesn't guarantee clarity. Leadership often comes without a clear guidebook, leaving many in confusion despite new titles or raises.

Artificial intelligence is rapidly transforming the landscape of white-collar jobs, with executive assistants facing significant disruption. Major firms like PwC, Deloitte, KPMG, EY, and McKinsey have begun reducing or relocating assistant roles, influenced by cost pressures and AI-driven automation. Traditionally stable and well-paid, these positions are now vulnerable as AI takes on administrative tasks. Research shows that secretarial roles, heavily populated by women, are particularly at risk and may struggle to transition to new jobs. The impact extends beyond professional services, threatening career pathways especially for non-college graduates who depend on administrative roles as entry points into better-paying white-collar work.

Delaying feedback can create bigger problems down the line that could have been avoided with timely communication.

Over the last twenty years, technical debt has traditionally meant outdated architectures and messy code. However, in the AI age, these challenges have evolved into subtler, more complex debts spread across prompts, models, and data dependencies that are difficult to observe and manage. This new AI debt contributes heavily to project failures, with studies revealing a majority of AI initiatives failing or being scrapped due to these systemic complexities. Unlike traditional bugs, AI failures are intermittent and harder to detect, requiring continuous oversight post-launch.

AI debt now takes form in four key areas: prompt debt, where quick fixes and poor version control create fragile prompt systems; model dependency debt, where reliance on external AI models leads to unpredictability as those models change; retrieval debt, where outdated or messy data results in technically correct but obsolete AI outputs; and evaluation debt, marked by the absence of standardized testing and monitoring practices for AI models. These challenges are compounded by traditional technical debt and the untested deployment of AI-generated code, escalating risks and costs across enterprises.

To counteract AI debt, enterprises must treat prompts like code with robust versioning and testing, embed continuous evaluation into AI workflows, and ensure explainability of outcomes through clear data lineage and audit trails. Leadership commitment and dedicated budgets are crucial to these efforts, akin to past investments in security and cloud modernization.

Ultimately, enterprise AI is a dynamic system requiring ongoing maintenance to sustain reliability and trust. Companies that address AI debt proactively from the outset stand to build enduring AI platforms that drive meaningful productivity improvements.

Ransomware attacks surged last year, but your business might have more strategic responses available than you realize when faced with such a threat.

Using corporate jargon often alienates employees, while straightforward and transparent communication fosters trust and engagement.

In the journey of AI-driven transformation, the major challenge isn't the technology itself—it's when leaders remain silent or unclear just when their teams need transparent communication, context, and reassurance the most.

Carolyn Geason-Beissel/MIT SMR | Getty Images

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.