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Five Worthy Reads: Greener IT Starts Here: How AI Is Transforming Operational Efficiency

Five worthy reads is a regular column on five noteworthy items we’ve discovered while researching trending and timeless topics. This week, we learn more about how AI transforms sustainability in the enterprise. As enterprises accelerate their digital journeys, IT teams are under pressure to deliver faster, smarter, and more sustainable operations.

Telecom Retention Crisis and Why Leading Carriers are Deploying Agentic AI

Telecom executives face a retention crisis that headcount cannot solve. Customers churn within 48 hours of a service incident, and traditional support models, even AI-powered chatbots, respond to problems rather than prevent them. The carriers closing this gap aren't expanding call centers. They're deploying agentic AI that predicts issues, executes resolutions, and learns from every network signal.

DevEx matters for coding agents, too

The speed at which you can go from making a change in your code, to understanding if it actually works, has long been a popular topic of discussion (and often, humour) for engineers. This remains true in a world with AI. Developer experience isn't just important for humans anymore. Those agents we're all using hundreds of times a day? Feedback cycles matter just as much for them, if not more.

12 Proven Strategies to Improve Warehouse Efficiency in 2026

Doing more with less isn't a slogan; it's the empirical fact for warehouses moving into 2026. Order volumes are fluctuating, customer expectations are constantly increasing, and adding more staff isn't always the way out. Efficiency has been made the most manageable lever. Not by cost-cutting, but through improved decisions: where automation brings value, how layouts reduce wasted movement, and how teams are enabled to work more efficiently. That shift is reflected in investment trends, with the warehouse automation market expected to exceed $41.27 billion by 2029.

How Online Stores Are Letting Shoppers 'Try Before They Buy' Without a Fitting Room

The uncertainty that exists within online fashion retail continues to be the single most significant problem experienced when it first became available. Customers will see spectacularly styled product photographs yet remain apprehensive about actually completing their purchase, asking themselves questions such as: Do I really think this is going to fit? Is the colour the same on my computer as it is when I wear it? Does the material fit and hang the same way, etc?

The Observability Stack is Collapsing: Why Context-First Data is the Only Path to AI-Powered Root Cause Analysis

By Bill Balnave, VP of Customer Success at Mezmo The core promise of modern observability is simple: cut Mean Time To Resolution (MTTR). Yet, despite a boom in tooling and investment over the last four years, the data tells a sobering story: our industry is actually getting worse at finding and resolving issues. Dashboards, once our trusted guide, have become the starting point for a chaotic "dashboard hunt" that rarely leads to the definitive root cause.

Rovo Dev Auto Closing Vulnerabilities | Bitbucket Blitz | Atlassian

Learn how Atlassian uses Rovo Dev to automatically find and fix code vulnerabilities with Rovo Dev and Bitbucket. This capability saves our developers thousands of hours over three months and reduces issue resolution time by half, allowing them to focus on building software and solving problems for our customers. This technology is available to all of our customers. Learn how it works, and start using it yourself.

Elastic at AWS re:Invent: Concluding a year of partnership in agentic AI innovation

Highlights of another laudable year of customer-centric collaboration The integration of Elastic’s capabilities, including vector databases and context engineering, with AWS services helps customers build intelligent, scalable, and secure applications faster and with greater flexibility. Our ongoing collaboration has resulted in another year of notable innovation with AWS. This blog highlights our continued collaboration with AWS throughout 2025 to help you capitalize on the power of AI.

Building a Code Review system that uses prod data to predict bugs

This post takes a closer look at how Sentry’s AI Code Review actually works. As part of Seer, Sentry’s AI debugger, it uses Sentry context to accurately predict bugs. It runs automatically or on-demand, pointing out issues and suggesting fixes before you ship. We know AI tools can be noisy, so this system focuses on finding real bugs in your actual changes—not spamming you with false positives and unhelpful style tips.