The Top 5 Takeaways in AI from Google Cloud Next

At EDB, our experts attend conferences worldwide to stay ahead of the latest trends and connect with other IT leaders paving the future of technology. Tens of thousands of technical professionals and visionaries attended Google Cloud Next in Las Vegas last week to share their insights about the future of business transformation. Here are the top 5 takeaways from our EDB attendees, Cloud Alliances Manager Kennedy Grimes and Cloud Alliances & Strategic Partnerships leader Stephen Cross.

Five key insights from EDB at Google Cloud Next

1. Many companies are using Gen AI, but not across all teams. AI usage often sits within technical teams, yet every silo of an organization can experiment with Gen AI tooling, from HR and Marketing to Sales and Engineering. Cross-enterprise use can unleash the full potential of AI to accelerate business transformation. 
2. Scaled data and AI workloads are key to unlocking innovation. The demands on AI infrastructure are 10x what they were just one year ago, with no signs of slowing down. The modern data infrastructure must be powered by data platforms with high reliability and high scalability to process and utilize all of this data effectively and efficiently. 
3. Companies are taking an incremental approach to AI to drive exponential growth. The top objective for using AI is higher ROI. Companies are approaching this in two steps: first, leveraging GenAI tooling to incrementally augment workloads on a day to day basis. And secondly, building on that step to open up further opportunities to uncover high ROI projects that can leverage AI to further augment or automate.  
4. Security and governance are top concerns. Enterprises require the highest levels of governance and security, and many leaders are worried about the potential risks of using AI and enforcing responsible AI practices. Enterprises must ensure the platforms respect file permissions, guarantee model isolation, and use enterprise grade security. This results in secure, private, permissions-aware, and fully reference-able answers to mitigate risk. Ultimately, the company's governing processes and policies must be able to scale and leverage the platform's comprehensive layers of trust.
5. Companies experimenting with AI often are held back by similar limitations. Knowledge quality of the AI tool is highly dependent on context; the output is only as good as the user prompt. LLMs also often lack enterprise-specific knowledge, making targeted content creation for specific audiences in the brand voice costly and challenging. To solve these challenges, a platform must be equipped to learn the company’s specific AI needs and prompts; it must be able to learn the brand and integrate into workflows. One centralized AI platform with powerful search features and RAG technology is critical.

AI is the topic every business leader is focused on at Google Cloud Next and beyond. Learn how Postgres can revolutionize AI workloads and leverage its full potential to accelerate innovation for your business here.