Artificial Intelligence (AI) is revolutionizing industries, from healthcare to finance, but many organizations struggle with where to start. Simply investing in AI tools isn’t enough—companies must ensure they have the right strategy, infrastructure, and data practices in place. This AI Readiness Checklist outlines ten critical steps to prepare your organization for successful AI adoption, with a special focus on software-defined cloud storage and scalable infrastructure.
The rise of Agentic AI is transforming the analytics landscape, but it comes with an often-overlooked challenge: database strain. Traditionally, operational databases are ring-fenced to prevent unstructured, inefficient queries from affecting critical business functions. However, in a world where AI agents dynamically generate and execute SQL queries to retrieve real-time data, production databases are facing unprecedented pressure.
In our previous post, 12 Ways Silk Echo Supercharges AI and Modern Workflows, we explored the groundbreaking capabilities of Silk Echo and its role in revolutionizing enterprise workflows by enabling AI innovation. Today, we’re taking a closer look at one of its most transformative use cases: optimizing Dev/Test environments.
The ViVE 2025 Healthcare Conference, held in Nashville from February 16-19, brought together the brightest minds in healthcare to explore transformative technologies that redefine healthcare delivery. Silk proudly showcased our cutting-edge solutions at our booth standing alongside one of our premier customers, Sentara Health, and one of our top partners, OptaFi. Being there, I had the unique opportunity to experience and reflect on the transformations in progress.
Editors Note: This blog post originally appeared on the Elffar Analytics blog
Organizations are racing to harness the transformative power of artificial intelligence. From training machine learning models to powering advanced analytics, AI thrives on one critical resource: fresh, real-world data. Yet, traditional methods of accessing and duplicating production data are rife with challenges — time-consuming manual processes, high costs, and risks to production stability.
Artificial intelligence (AI) is a game-changer for organizations striving to innovate and operate more efficiently. From transforming manufacturing processes to redefining patient care, AI is unlocking unprecedented opportunities across industries. Top organizations like PTC, Sentara Healthcare, and Franciscan Health are at the forefront of this revolution, leveraging cutting-edge AI technologies to overcome unique challenges and achieve remarkable outcomes.
In the challenging economic landscape of 2025, organizations are under immense pressure to rationalize spending and optimize costs. Even as software budgets are expected to increase and cloud adoption continues to rise, the demand for transparent cost savings and financial justification in purchasing has become paramount. Silk stands at the forefront of this transformation, empowering enterprises to free significant cloud spend for new innovation and growth while enhancing performance and operational efficiency.
For the previous articles in this series, I tested Silk Platform’s capabilities in multiple contexts, both its raw performance in the cloud and suitability for modern enterprise AI use cases like vector search.
In my last blog post I wrote about AI-enhancing your existing applications with low risk – and without a huge upfront effort to see if there’d even be value in it for your business. You can just add new AI use cases to your current applications and data, within the database instances that you already have. This approach is possible thanks to all major database vendors now supporting AI vector search and various “AI API calls” in their latest product versions.