Thomas Godwin

The enthusiasm for enterprise AI is outpacing strategic thinking — and process automation, not more AI tools, is the fix most organizations might be missing.

Becoming an AI-native company is no longer the competitive advantage it was five minutes ago. Now, it's an empirical obligation, and Dell Technologies Inc. made that case to customers at its annual flagship event.

Enterprise AI data strategy is separating the organizations that execute on AI from those that aspire to it, and the difference boils down to whether the data foundation was built before the AI was deployed.

The Tax Court held that a Texas rancher operated with a profit motive, despite “troublingly large” losses more than 77 times his gross receipts from farming and ranching.

The IRS issued a passport notice certification of a “seriously delinquent tax debt” of more than $1.6 million in trust fund recovery penalties.

AI FinOps is busy rewriting the rules of cloud financial management, systematically dismantling its own rulebook, which took nearly a decade to write.

AI cost governance is moving to the center of enterprise cloud strategies, and AWS is responding with a new autonomous agent that's designed to manage the complexity of the change.

AI cost management is bringing a new taxonomy to FinOps practitioners, but the core discipline, understanding what you're using, why, and what it costs, remains the same.

Getting to production-ready AI requires much more than fast storage because enterprises need data-ready AI infrastructure that's built on clean, governed and well-understood data before any meaningful deployment can scale.

Fragmented data, siloed infrastructure and reactive portfolios are problems that every enterprise has to deal with, and fixing them efficiently