Having frequently worked with governments around the world over the course of my career, I’ve had all kinds of discussions about the global impact of generative AI. Today, I’m publicly wading into those waters to deliver my perspective, and my opinion is that … it’s incredibly hard to predict the future. Done. Wrapped up this entire post in a single sentence.

Data engineers and architects are being asked to do more with their enterprise data than ever before. Yet, the knowledge gap between what businesses want to do with data and how they can accomplish it is growing daily—especially considering today's AI hype cycle. With all that noise in the market, it's easy to see how organizations struggle to keep pace with innovation.

Underperforming AI programs/models built using low-quality or inaccurate data cost companies up to 6% of annual revenue on average.

If you’ve been following our news, you know we just announced free fractional GPU capabilities for open source users, enabling multi-tenancy for NVIDIA GPUs and allowing users to optimize their GPU utilization to support multiple AI workloads as part of our open source and free tier offering.

The potential impact of AI in banking appears boundless. A 2023 McKinsey report found that effectively incorporating generative AI tools into business operations could lead to annual operational savings ranging from $200 billion to $340 billion for the global financial services industry. These cutting-edge technologies can also enhance customer satisfaction, attract more potential customers, and improve employee experience.

In just a few months, enterprise AI has rapidly evolved into a strategic partner for organizations that want to stay ahead of the disruption curve. Whether enterprises are leveraging predictive analytics for strategic decision-making, using machine learning algorithms for supply chain optimization, or applying generative AI and large language models to enhance customer service, it’s clear AI will help business in many ways.

The editorial team of Hitachi Vantara sat down for an interview with our Chief Product Officer, Octavian Tanase, to learn about his vision and strategy for AI in the enterprise.

Think back just a few years ago when most enterprises were either planning or just getting started on their cloud journeys. The pandemic hit and, virtually overnight, the need to radically change ways of working pushed those cloud journeys into overdrive. Cost-effective adaptability was essential. And the companies that could scale up or scale down quickly were the ones that navigated the pandemic successfully. Migrating to the cloud made that possible.

You can now access Gemini 1.0 Pro from inside the BigQuery console, gaining scale and efficiency to ground LLMs with your business data.