Google Cloud Next was recently held in Las Vegas, bringing together 30,000 attendees to learn about the latest developments from Google Cloud. The primary focus of the event was generative AI. As a leading vendor in cloud infrastructure and platforms, Google Cloud used this platform to announce a range of AI enhancements aimed at improving productivity across their platform.
The spotlight was firmly on generative AI, with little mention of Google’s core cloud technology. This approach mirrors Salesforce’s focus at their New York City roadshow last year. Google’s core business was only mentioned in the context of generative AI.
Google announced a series of AI enhancements designed to help customers leverage the Gemini large language model (LLM) and boost productivity across their platform. The keynote on the first day and the Developer Keynote the following day were filled with demos showcasing the power of these new solutions.
However, some critics felt that the examples given were overly simplistic, relying mostly on examples within the Google ecosystem, which doesn’t reflect the reality that most companies store their data in repositories outside of Google.
Despite some criticism, generative AI has some compelling use cases, from creating code to analyzing a corpus of content. It can even answer questions about log data to understand why a website went down. Google also introduced task and role-based agents that could help individual developers, creatives, employees, and others take advantage of generative AI in practical ways.
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However, there are many obstacles that could hinder a successful generative AI implementation. While Google tried to make it sound easy, implementing any advanced technology in large organizations is a significant challenge.
Just like other technological advancements over the past 15 years, generative AI comes with promises of potential gains but introduces its own level of complexity. Large companies often move more cautiously than we might think, and AI implementation could be a bigger challenge than Google and other large vendors are acknowledging.
Many factors could prevent companies from adopting technological innovations, including organizational inertia, a brittle technology stack, and internal politics. Vineet Jain, CEO at Egnyte, sees a clear division between companies that have already made a significant shift to the cloud and those that have been slow to adopt new technologies.
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Companies that are late to adopt cloud technology will likely face challenges when trying to implement generative AI. They will initially need to focus on digital transformation and establishing a mature data security and governance model.
The successful implementation of generative AI relies heavily on data. Companies that lack clean data will likely struggle to implement generative AI solutions.
From Google’s perspective, they have built generative AI tools to help data engineers build data pipelines to connect to data sources inside and outside of the Google ecosystem. This should help in cleaning and connecting data, especially in companies further along the digital transformation journey.
Apart from the implementation, AI comes with its own set of challenges, whether it’s an app based on an existing model or a custom model. Governance, liability, security, privacy, ethical use, and compliance are all important considerations.
