Data, AI, & Machine Learning

AI automation is uneven across roles. Leaders who understand the friction points can better navigate workforce changes.

Hotels and landlords can mitigate legal risks by choosing decentralized revenue management systems that use public data.

Accountability, strategy, and resource challenges can hamper responsible AI efforts — but they can be overcome.

Identifying how the strengths and weaknesses of large language models will impact specific business processes is just the first step to an effective AI strategy.

AI’s ability to create value rests on the philosophy determining how and what it learns.

Analytics must reflect how decisions are actually made, factoring in ambiguity, practical limits, and trade-offs.

AI coding tools boost productivity in the short term, but hidden technical debt can cripple systems over the long term.

Small and medium-sized companies should consider five key cost-related factors before implementing process automation.

Robots and humans are essential partners in most warehouse settings. AI can help them work together more effectively.

What vibe coding with GenAI tools does for programming, vibe analytics does for data: Companies gain insights faster.