Starling Bank is eliminating approximately 130 positions, about 3% of its 4,000 employees, as part of a strategic restructuring of its banking and technology divisions. Employees were informed that these changes aim to streamline operations, eliminate redundancies, and speed up product development. This downsizing aligns with Starling's increased emphasis on integrating AI technologies and expanding beyond the UK market.

Alibaba has reportedly designated Claude Code as high-risk software, leading to a ban on its use by employees within the company.

Recent data reveals that two-thirds of enterprises had already diversified their AI model strategy before Anthropic's Claude Fable 5 was abruptly taken offline due to a U.S. export-control order. The model, highly regarded but costly, was inaccessible for several weeks, highlighting the risks of vendor dependency. Enterprises are now blending closed frontier models with open-weight models or moving workflows entirely onto self-hosted platforms to avoid disruptions. However, most companies lack the automated monitoring needed to detect AI failures, with only 10% having such systems in place. Organizational challenges persist, including unclear ownership and governance of AI systems. Shadow AI and uncontrolled agentic workloads have led to significant financial losses for many. The disruption has underscored the importance of flexibility, control, and proper governance in enterprise AI deployment.

Many current AI strategies are less about thoughtful planning and more about reacting to boardroom pressure and fear. Leaders are anxious as they face intense demands to perform amid strained teams and unfulfilled ROI expectations. WRITER's recent AI Adoption in the Enterprise report reveals that most executives feel this pressure, with tensions rising internally and widespread fears about job security over AI rollouts. The rush to implement AI tools often results in superficial efforts that don't produce meaningful revenue or change.

True AI success requires rethinking workflows, empowering business teams to lead transformation, and supporting passionate AI champions who drive real innovation. It’s essential to measure progress by leverage—how much new value AI creates—rather than just time saved. Leaders ignoring these fundamentals risk short-term survival at the expense of long-term success. Instead of succumbing to panic, embracing a strategic, people-centered approach will turn AI efforts into sustainable growth.

The conversation about artificial intelligence and employment often centers on AI replacing human jobs to cut costs. However, the reality in 2026 is far more complex, with major tech companies scaling back AI investments due to unexpectedly high computing costs. This shift is revealing an important insight: the human qualities that AI can't replicate—judgment, empathy, relationship-building, and trust—are increasingly valuable and will define career success. Research shows AI is amplifying human expertise rather than replacing it, and jobs requiring human skills are growing faster and commanding higher wages. The true career edge now lies in embracing these uniquely human capabilities that no algorithm can match.

In today's fast-paced business world, agility is not just a buzzword but a necessity. The Project Management Institute surveyed over 700 C-suite leaders to develop a clear roadmap for achieving enterprise agility, highlighting three key insights:

1. Transformation is now a constant, not a one-time event. Organizations must adapt their operating models frequently, with 65% changing their business approach every two years or less. The real challenge lies in closing the gap between planning and execution through continuous delivery capabilities.

2. Accountability needs to transcend departmental silos. For true agility, companies must prioritize long-term enterprise goals and foster cross-functional collaboration, rather than focusing on short-term, siloed KPIs.

3. Leadership plays a crucial role in setting the tone for change. Leaders must cultivate a culture where experimentation and learning are encouraged, helping employees understand the purpose behind transformation efforts.

Across industries and regions, successful enterprise agility requires a mindset centered on curiosity, learning, and adaptability, enabling organizations to stay ahead in a constantly evolving landscape.

(Pierre Le Manh is president and CEO of PMI.)

In an age dominated by artificial intelligence, the qualities that set great leaders apart will be their perspective and sound judgment. As AI tools handle more tasks, human insight will become increasingly invaluable for making thoughtful decisions and guiding teams effectively.

In today’s AI-driven work environment, producing more isn’t the key to success. True professional value lies in exercising sound judgment, practicing restraint, and communicating with clarity. Professionals need to shift focus from simply churning out content to providing thoughtful, strategic insights that earn leaders’ trust and drive better decisions.

Research indicates that as economic growth faces structural constraints, business leaders are calling on policymakers to better coordinate strategies and overhaul workforce development programs. This alignment aims to ensure that lifelong learning evolves to meet the demands of an AI-driven future.

Enterprise AI programs are rapidly expanding but struggling with governance and ownership issues. Most lack a clear, centralized owner accountable for AI oversight across multiple platforms, resulting in a "control gap" where ambition and spending outpace visibility and cost management. About 85% of organizations run multiple AI platforms competing for primacy, yet only 10% employ active monitoring and alerting for model failures—most rely on manual reviews. Shadow AI and unauthorized agentic usage are common, causing significant financial and operational risks. Despite growing AI portfolios, organizations often face fragmented accountability, limited automated detection, and disappointing returns on custom model investments. The core barrier to effective AI governance is the absence of a dedicated accountable owner, not technology or spending. A shift towards centralized ownership and better cross-platform controls is essential to close this widening control gap.