The AI and data landscape is evolving at an unprecedented pace. Organizations are not only grappling with the challenge of managing massive volumes of data but also seeking ways to harness it for AI-driven innovation. As we enter 2025, the intersection of data and AI will continue to transform, unlocking new opportunities and reshaping the future in exciting and unexpected ways.
To better understand the innovation and changes in the future, Cloudera hosted a panel discussion consisting of industry experts who shed light on the biggest trends and changes they see surrounding data and AI. Moderated by Cloudera’s Senior Director of Product Marketing, Wim Stoop, the panel included fellow Clouderans Manasi Vartak, Chief AI Architect, Christopher Royles, Field CTO - EMEA, and guest speaker and Principal at SanjMo and former Gartner Analyst, Sanjeev Mohan. The panelists covered a broad range of trends—from the use of AI agents to effective governance.
You can watch the entire conversation here. A myriad of topics were covered, so in this blog, we’ll explore some of the questions we didn’t have time to answer on the live webinar.
What can we expect with regard to AI agents in the coming year?
AI agents are a key part of AI’s evolution, designed to operate autonomously and mimic human decision-making, problem-solving, and learning. Over the past year, they’ve gained traction across industries, handling tasks from customer support to streamlining internal operations.
One area where AI agents show significant promise in the coming year is security. Security operations centers (SOCs) are grappling with increasing demands and overwhelming alert volumes. AI agents powered by GenAI have the potential to enhance SOC capabilities and reduce the cognitive load on analysts. These capabilities include the ability to autonomously monitor for threats in real time, automate routine tasks with minimal human intervention, and provide contextual decision-making support. More on the security implications of AI agents here.
Considering AI governance is still evolving, what are some best practices, and how should we expect it to develop?
New AI models are coming out nearly every day. Given the rapid evolution of AI models, AI governance needs to keep pace and adapt along with it. While data governance practices have been articulated heavily over recent years, AI governance has not fully taken off.
But there are still a series of steps and best practices that organizations can, and should follow to be successful. These include establishing clear guardrails around the development and deployment of AI systems, as well as implementing robust monitoring and evaluation frameworks. When implementing these practices the focus should come back to a few key areas like transparency, mitigating biases, maintaining data security, and enforcing accountability at every stage of the AI lifecycle. As AI governance continues to develop, we expect to see more standardized frameworks that integrate these best practices into model development and deployment processes.
What’s the more effective route for my business to manage our data and AI initiatives? Single or hybrid cloud?
The answer here is actually more nuanced than an either-or scenario. Taking a hybrid cloud approach alone is not enough and only comes with more complexity and challenges. What organizations should be working toward is ‘true’ hybrid. But what does that mean?
One of the biggest differentiators for true hybrid is the ability to operate as a single platform across both data center and cloud, and at the edge. When examining your own operations, there are several areas to focus on to achieve true hybrid, including a distributed model and portable and interoperable data services. You can see what else is needed and the necessary steps to take in this checklist.
How is success with AI defined through the lens of social and environmental well-being?
Success with AI, through the lens of social and environmental well-being, is defined by its ability to drive meaningful, measurable impact beyond business objectives. While AI can enhance productivity and efficiency, it can do a lot to mitigate our environmental and social impact.
One key measure of success is environmental impact, including reductions in energy consumption and carbon footprints. By optimizing workloads and resource allocation, AI can help organizations minimize their environmental footprint—an approach we actively support by enabling customers to run more efficient, sustainable operations.
We’ve also been able to support customers who are making major contributions to society, leveraging Cloudera’s platform in fields like pharmaceutical research, for example. When it comes to measuring success, the path forward has increasingly become about finding a balance between both of these elements.
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Thank you to everyone who attended our webinar! The future of data and AI is full of possibilities, and businesses and organizations of nearly every type and size are gearing up to take advantage. As the industry navigates what’s next, Cloudera is already deeply ingrained in shaping that future, providing customers with capabilities that enable them to attain the trusted data needed to harness AI.
Interested in learning about what else our expert panel thinks is in store for 2025? Watch the entire webinar here, then find out how Cloudera can help you dive deeper into data and AI in the new year.
