Can a single missing toggle switch lead to a $500 million corporate autopsy? This week’s Saturday Strategy dives into the “Ghost in the Machine”—the governance failures behind AI budget disasters. We explore Goodhart’s Law, the “Open Tab” fallacy, and why measuring AI adoption by usage is a trap that leads to waste, not value.
This week, the AI bill came due. As subsidies fade and real usage costs surface, we’re diagnosing the market-wide pattern that led even giants like Microsoft to hit a budget wall. In this edition of The Monday Ledger, we prescribe the architectural rigour and cost mechanics needed to turn last quarter’s innovation into a sustainable, long-term forward posture.
Why do 95% of AI pilots fail to deliver ROI? This week, we dive into the “Planning Fallacy” – from the lessons of HS2 to a 90-year-old’s 10-year plan – and why your AI strategy needs an architect who understands the plumbing.
95% of AI pilots fail to show a measurable P&L impact. For the CFO, technical benchmarks like accuracy mean nothing without economic outcomes. This guide breaks down the four KPIs—LCOAI, EBIT impact, Governance Dividend, and Capacity—that prove AI value.
“Agent washing” is an expensive distraction for professional services. Mislabeled AI agents lead to project failures and security gaps. Learn why data fluency is the only way to protect your firm from financial waste.”Agent washing” is an expensive distraction for professional services. Mislabeled AI agents lead to project failures and security gaps. Learn why data fluency is the only way to protect your firm from financial waste.
Certinia Veda launched April 2026 with 10 agents, 64 actions and a 1.5% EBITDA lift. Here’s why it’s a working blueprint for agentic AI in services.
Focusing on Commvault, the recent developments in AI data security at the RSA Conference emphasise the importance of integrating Data Security Posture Management (DSPM) into AI systems. The “last mile” of governance, where sensitive data may be mishandled, is highlighted as a gap that organisations must address to ensure secure AI operations.
The Commvault SHIFT London event highlighted that data resiliency is essential for effective AI and BI strategies. We need to consider what we do with the data that AI produces, not just the data sources. Without a strong data foundation, businesses risk poor decisions and operational failures. The event advocated for integrating security and data management, deploying tools like Commvault Cloud Unity, and continuously testing recovery plans to ensure organisational resilience.
Microsoft’s latest earnings call indicates a strategic shift towards prioritizing high-margin AI software over infrastructure. With 15 million Copilot users, the focus is on transforming enterprise value through active user engagement rather than mere license purchases. The emphasis on intelligence signals the future direction for organizations investing in AI.
The rise of artificial intelligence brings excitement, yet many organizations fall into the “AI Pilot Trap,” where initiatives fail to yield real value. Statistics show 95% of AI tools never reach production and 72% destroy value. To escape this trap, organizations should focus on governance, back-office ROI, and strategic partnerships.
Datasphere technologies are transforming data management for AI by transitioning from siloed structures to interconnected ecosystems that deliver real-time insights, ensure data quality, and democratize product development. These advancements enable organizations to efficiently leverage data, improve AI deployment speed, and enhance innovation, positioning them for competitive advantage in the evolving landscape.
IBM’s $11 billion acquisition of Confluent represents a strategic shift in enterprise AI deployment, focusing on event-driven platforms for resilience and speed. Confluent’s streaming technology enhances data integration across hybrid environments, fostering robust AI ecosystems. This move positions IBM competitively against major cloud providers, emphasising that data infrastructure is just as important for AI scalability and reliability.