RTInsights

Enterprise adoption of AI agents is accelerating, but the move from experimentation to production at scale remains uneven. In fact, 96% of organizations are already using AI agents in some capacity. The focus is shifting from demos to production deployments, and organizations are no longer curious about whether or not they should deploy agents. Now, the question is how to safely and at scale orchestrate comprehensive agentic systems at a reasonable cost.

The rising costs of AI services and hyperscaler cloud compute are driving more organizations to reduce vendor dependence and expand their edge AI capabilities. However, edge AI is fundamentally different from cloud-based frontier models, and their implementation strategies can’t be replicated one-to-one. Think of it as different tools in the same toolbox. You don’t use a saw when you need a screwdriver.

Keeping pace with news and developments in the real-time analytics and AI market can be a daunting task. Fortunately, we have you covered with a summary of the items our staff comes across each week. And if you prefer it in your inbox, sign up here! Real-time analytics news in brief Alteryx announced new AI capabilities across Alteryx One that connect enterprise-grade business logic directly to the AI agents teams already use.

Emerging technology is no longer available solely to enterprise users. The latest technologies, such as artificial intelligence (AI), are not only available to those with the budget and resources to adopt them. It’s becoming a level playing field for all technology users, from developers to consumers, as proven by the explosion of AI adoption. For decades, transformative technologies have always followed the same adoption path.

There’s an interesting analogy I like to draw between the state of enterprise data today and where containers were about ten years ago. Early in the container revolution, organizations started realizing they were supporting an entire infrastructure layer that was exponentially proliferating under them. The solution wasn’t to stop shipping containers or build better containers – it was to build a control plane above them.

Enterprises need to consider several factors when evaluating AI inference servers. For many, a key characteristic is performance measured in raw tokens/second. However, an ideal solution also optimizes latency, throughput, cost, operational complexity, and scalability. Many of those factors depend heavily on where the inference server operates. Hence, enterprises must consider sovereign AI.

Keeping pace with news and developments in the real-time analytics and AI market can be a daunting task. Fortunately, we have you covered with a summary of the items our staff comes across each week. And if you prefer it in your inbox, sign up here! MLCommons announced the results of its MLPerf Storage v3.0 benchmark suite, which measures the performance of storage systems for machine learning (ML) workloads in an architecture-neutral, representative, and reproducible manner.

For years, the direction of AI development has seemed straightforward: bigger models, more data, more parameters, more capability. And in many areas, that pattern has held true. Frontier models have made astonishing leaps in reasoning ability, analytical process, and autonomous task execution. In cybersecurity, those advances are already changing how organizations think about vulnerability discovery, threat investigation, incident response, and defensive automation.

Like the housing market, or airfare, prices for IT equipment have always gone up and down according to shifting marketplace dynamics or macroeconomic trends. And when prices go up, if there’s a need, IT still buys because that is what IT does. The job of IT is to keep the business running smoothly.

Keeping pace with news and developments in the real-time analytics and AI market can be a daunting task. Fortunately, we have you covered with a summary of the items our staff comes across each week.