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The CTO: Evolving The Role of Chief Technology Officers

N2Growth Blog

Shifts in CTO Responsibilities Over Time The Chief Technology Officer (CTO) role has evolved dramatically over the past few decades, driven by rapid technological advancements and shifting business landscapes.

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Redefining customer experience: How AI is revolutionizing Mastercard

CIO

Our commitment to customer excellence has been instrumental to Mastercard’s success, culminating in a CIO 100 award this year for our project connecting technology to customer excellence utilizing artificial intelligence. We live in an age of miracles.

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3 steps to get your data AI ready

CIO

As they embark on their AI journey, many people have discovered their data is garbage, says Eric Helmer, chief technology officer for software support company Rimini Street. One of the foundational principles is cybersecurity, which is the primary concern for CIOs, according to Hardy.

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Cybersecurity Automation: Leveling the Playing Field

CIO

By Leonard Kleinman, Field Chief Technology Officer (CTO) ) Cortex for Palo Alto Networks JAPAC Many things challenge how we practice cybersecurity these days. Let’s look at some of these cybersecurity challenges and how automation can level the playing field.

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10 top priorities for CIOs in 2025

CIO

Double down on cybersecurity In 2025, there will be an even greater need for CIOs to fully understand the current cybersecurity threat landscape. The pace of change in the global market and technology landscape demands organizations that can adapt quickly.

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What is an IT consultant? Roles, types, salaries, and how to become one

CIO

Among the recent trends impacting IT are the heavy shift into the cloud, the emergence of hybrid work, increased reliance on mobility, growing use of artificial intelligence, and ongoing efforts to build digital businesses. As a result, for IT consultants, keeping the pulse of the technology market is essential.

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Build a strong data foundation for AI-driven business growth

CIO

In the quest to reach the full potential of artificial intelligence (AI) and machine learning (ML), there’s no substitute for readily accessible, high-quality data. If the data volume is insufficient, it’s impossible to build robust ML algorithms. If the data quality is poor, the generated outcomes will be useless.