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IT leaders: What’s the gameplan as tech badly outpaces talent?

CIO

Gen AI-related job listings were particularly common in roles such as data scientists and data engineers, and in software development. According to October data from Robert Half, AI is the most highly-sought-after skill by tech and IT teams for projects ranging from customer chatbots to predictive maintenance systems.

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AI & the enterprise: protect your data, protect your enterprise value

CIO

I wrote, “ It may be even more important for the security team to protect and maintain the integrity of proprietary data to generate true, long-term enterprise value. This has made data even more of a target for bad actors and increased the damage resulting from malicious or accidental exposures. Years later, here we are.

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Lessons learned turning machine learning models into real products and services

O'Reilly Media - Data

Why model development does not equal software development. Today, just 15% of enterprises are using machine learning, but double that number already have it on their roadmaps for the upcoming year. So what should an organization keep in mind before implementing a machine learning solution?

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Are you ready for MLOps? 🫵

Xebia

Gartner reported that on average only 54% of AI models move from pilot to production: Many AI models developed never even reach production. Both the tech and the skills are there: Machine Learning technology is by now easy to use and widely available. Big part of the reason lies in collaboration between teams.

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Unlocking the full potential of enterprise AI

CIO

Research from Gartner, for example, shows that approximately 30% of generative AI (GenAI) will not make it past the proof-of-concept phase by the end of 2025, due to factors including poor data quality, inadequate risk controls, and escalating costs. [1] Reliability and security is paramount.

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Scaling AI talent: An AI apprenticeship model that works

CIO

Along the way, we’ve created capability development programs like the AI Apprenticeship Programme (AIAP) and LearnAI , our online learning platform for AI. AIAP in the beginning: Goals and challenges The AIAP started back in 2017 when I was tasked to build a team to do 100 AI projects. To do that, I needed to hire AI engineers.

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When is data too clean to be useful for enterprise AI?

CIO

Once the province of the data warehouse team, data management has increasingly become a C-suite priority, with data quality seen as key for both customer experience and business performance. But along with siloed data and compliance concerns , poor data quality is holding back enterprise AI projects.

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