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When a project goes off the rails, it’s tempting to focus on the urgent: fix the immediate issues to get things back on track. But unless you uncover the root cause and address it, you’re simply setting the stage for future failures.
Industry analysts predict growth in investment in cloud computing, and three quarters of CIOs surveyed say that it is critical to their operation. But in terms of CIO priorities, the cloud falls behind many other areas.
Ineffective communication, faulty data strategies, and shortchanging the people portion of transformation are just a few ways digital journeys take wrong turns or fizzle out.
Malfeasance (n): Intentional conduct that is wrongful or unlawful. Here are some common examples of how CIOs can handle it when it arises in their ranks.
Essen für Alle is a Swiss NGO whose name translates to Food for All. Every Saturday, thanks to a comprehensive network of partners, volunteers, and donors, it distributes 18 tons of food and essentials to people in need.
The financial services company has taken a containerized approach to achieve agility and flexibility with its workloads, while exploring the long-term benefits of generative AI.
Business leaders believe their data is primed for AI, but IT practitioners spend hours every day beating data into shape, only to miss out on automation opportunities.
From resource management, service delivery, to internal and external relationship building, the storied CIO and thought leader underscores the importance of persistence in the face of leadership challenges.
Commercial generative AI platforms like OpenAI and Anthropic get all the attention, but open source alternatives can offer cost benefits, security, and flexibility.
Kurt Brissett, the chief innovation and technology officer at Transport for New South Wales, was honored as the CIO of the Year at the 2024 CIO50 Awards, and recently spoke with CIO.com editorial director for ANZ Cathy O'Sullivan about deliverin
Establishing ROI from AI projects can be challenging, but getting specific about metrics that matter, aligning data operations to revenue-centric tasks, and ensuring employees make the most of AI can help.
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