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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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CAIOs are stepping out from the CIO’s shadow

CIO

But the increase in use of intelligent tools in recent years since the arrival of generative AI has begun to cement the CAIO role as a key tech executive position across a wide range of sectors. And then there is technology, she says. In many companies, they overlap with the functions of the CIO, the CDO, the CTO, and even the CISO.

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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. When a customer needs help, how fast can our team get it to the right person?

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Agentic AI design: An architectural case study

CIO

From obscurity to ubiquity, the rise of large language models (LLMs) is a testament to rapid technological advancement. Just a few short years ago, models like GPT-1 (2018) and GPT-2 (2019) barely registered a blip on anyone’s tech radar. In 2024, a new trend called agentic AI emerged. Do you see any issues?

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LLMOps for Your Data: Best Practices to Ensure Safety, Quality, and Cost

Speaker: Shreya Rajpal, Co-Founder and CEO at Guardrails AI & Travis Addair, Co-Founder and CTO at Predibase

Large Language Models (LLMs) such as ChatGPT offer unprecedented potential for complex enterprise applications. However, productionizing LLMs comes with a unique set of challenges such as model brittleness, total cost of ownership, data governance and privacy, and the need for consistent, accurate outputs.

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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.

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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. They started using data virtualization, which reduced the need for large data warehouses by decoupling data consumption from origination.

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