The journey from a great idea for a Generative AI use case to deploying it in a production environment often resembles navigating a maze. Every turn presents new challenges—whether it’s technical hurdles, security concerns, or shifting priorities—that can stall progress or even force you to start over.

According to recent survey data from Cloudera, 88% of companies are already utilizing AI for the tasks of enhancing efficiency in IT processes, improving customer support with chatbots, and leveraging analytics for better decision-making. More and more enterprises are leveraging pre-trained models for various applications, from natural language processing to computer vision. For that reason, Cloudera is evaluating a new line of business: Cloudera Integrated Data and AI Exchange (InDaiX).

Imagine a world where your sensitive data moves effortlessly between clouds – secure, private, and far from the prying eyes of the public internet. Today, we’re making that world a reality with the launch of Cloudera Private Link Network. Organizations are continuously seeking ways to enhance their data security. One of the challenges is ensuring that data remains protected as it traverses different cloud environments.

A prominent global bank was thrust into the spotlight for all the wrong reasons. The institution was hit with a staggering fine – multiple billions – for failing to comply with new data protection regulations that ultimately led to a customer data breach. The breach, which exposed sensitive information, not only resulted in financial penalties but also caused significant reputational damage.

Without a doubt, Artificial Intelligence (AI) is revolutionizing businesses, with Australia’s AI spending expected to hit $6.4 billion by 2026. However, according to The State of Enterprise AI and Modern Data Architecture report, while 88% of enterprises adopt AI, many still lack the data infrastructure and team skilling to fully reap its benefits. In fact, over 25% of respondents stated they don’t have the data infrastructure required to effectively power AI.

It’s nearing the end of the summer in North America, and one report has been a staple on my reading list for more than a decade: the Flexera State of the Cloud Report. The annual survey of hundreds of global IT decision makers assesses cloud strategies, migration trends, and important considerations for companies moving to the cloud or managing cloud environments.

The CRN Tech Innovator Awards spotlight innovative products and services across 36 categories, with winners chosen by CRN staff from over 320 product applications. This year, we’re excited to share that Cloudera’s Open Data Lakehouse 7.1.9 release was named a finalist under the category of Business Intelligence and Data Analytics.

By now, every organization, regardless of industry, has at least explored the use of AI, if not already embraced it. In today’s market, the AI imperative is firmly here, and failing to act quickly could mean getting left behind. But even as adoption soars, struggles remain, and scalability continues to be a major issue. Organizations are quick to adopt AI, but getting it established across the organization brings a unique set of challenges that come into play.

Data practitioners are consistently asked to deliver more with less, and although most executives recognize the value of innovating with data, the reality is that most data teams spend the majority of their time responding to support tickets for data access, performance and troubleshooting, and other mundane activities. At the heart of this backlog of requests is this: data is hard to work with, and it’s made even harder when users need to work to get or find what they need.

Over the past several years, data leaders asked many questions about where they should keep their data and what architecture they should implement to serve an incredible breadth of analytic use cases. Vendors with proprietary formats and query engines made their pitches, and over the years the market listened, and data leaders made their decisions.