IT Leadership | News, how-tos, features, reviews, and videos
A recent survey revealed just 36% of CIOs expect IT headcount growth in the year ahead, the lowest figure since 2011. Shifting economic outlooks and debate over AI’s impact are two key factors muddying IT hiring reality.
Stevie-Ann Dovico shares insights on leading digital transformation in a customer-centric bank. Discover how technology is driving innovation, improving customer experiences, and shaping the future of banking.
Many CIOs turn to their peers to learn more about AI, but a small minority believe that other CIOs know more than they do. That’s a problem that perhaps only self-education can solve.
In this issue: We look at how IT leaders are reworking their cloud implementations for better fit to their needs, as well as how they can control cloud costs and ensure cloud security.
As innovation and overseas expansion increased, LEOCH International Technology Ltd. faced many issues while developing new battery products. With SAP technologies, LEOCH created a platform that not only optimized business flow but also ensured enviro
AI gets all the attention these days, but the tech that keeps the business humming and advances (and protects) its core mission too often goes overlooked, unused, and underfunded.
Tech debt is one of the most urgent challenges facing companies, but it can be hard to mobilize a response. Here's how to show the business value of getting out from under it in a way that will galvanize the entire C-suite.
Tallying the devastation from recent storms in Spain is still ongoing. In addition to human loss and damage to homes and infrastructure, it represented a critical time for essential emergency workers and affected people to communicate with each other
Tech leaders have to react and adapt to fluctuating economies impacted by an AI-driven world. And while some view such innovations as sources of turbulence, others see them as pathways to smoother sailing.
Leading NZ tech company takes action to address inequity by giving women preference when hiring.
Data quality is critical for successful AI projects, but you need to preserve the richness, variety, and integrity of the original data so you don’t sabotage the results.
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