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Can AI solve your technical debt problem?

CIO

But t echnical debt can undercut an organizations ability to innovate long term, and the shortcuts taken during initial development likely resulted in a codebase thats convoluted, slow, or difficult for devs to understand. Add outdated components or frameworks to the mix, and the difficulty to maintain the code compounds.

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Accelerate AWS Well-Architected reviews with Generative AI

AWS Machine Learning - AI

As systems scale, conducting thorough AWS Well-Architected Framework Reviews (WAFRs) becomes even more crucial, offering deeper insights and strategic value to help organizations optimize their growing cloud environments. This allows teams to focus more on implementing improvements and optimizing AWS infrastructure.

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Agents are here — but can you see what they’re doing?

CIO

For example, AI agents should be able to take actions on behalf of users, act autonomously, or interact with other agents and systems. As the models powering the individual agents get smarter, the use cases for agentic AI systems get more ambitious and the risks posed by these systems increase exponentially.

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Scaling Startups: The Ultimate Guide For Founders

Luis Goncalves

While launching a startup is difficult, successfully scaling requires an entirely different skillset, strategy framework, and operational systems. This isn’t merely about hiring more salespeopleit’s about creating scalable systems efficiently converting prospects into customers. What Does Scaling a Startup Really Mean?

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What is technical debt? A business risk IT must manage

CIO

A little debt speeds development so long as it is paid back promptly with refactoring. While the term technical debt found its origins in software development, the concept is applicable to a wide range of IT implementations and operations beyond custom code. So, is technical debt bad? Why is technical debt important?

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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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The future of data: A 5-pillar approach to modern data management

CIO

This approach is repeatable, minimizes dependence on manual controls, harnesses technology and AI for data management and integrates seamlessly into the digital product development process. Poor-quality data is as detrimental as a pipeline outage, and perhaps more, as it can lead to bad decisions and provide harmful information to customers.

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