This week in Las Vegas, 30,000 people gathered to discover the latest news from Google Cloud. What they heard was generative AI, all the time. Google Cloud is primarily a cloud infrastructure and platform provider. If you didn’t know, you may have missed it in the flurry of AI news.
Not to downplay what Google had on display, but much like Salesforce last year during its traveling road show in New York, the company made only a passing nod to its core business – except in the context of generative AI, of course.
Google announced a series of AI enhancements designed to help customers take advantage of the Gemini Extended Language Model (LLM) and improve productivity across the platform. This is of course a laudable goal, and throughout the keynote on the first day and the developer’s keynote the following day, Google peppered the announcements with quite a few demos to illustrate the power of these solutions.
But many seemed a little too simplistic, even considering that they had to be squeezed into a time-limited keynote speech. They primarily relied on examples from within the Google ecosystem, when almost all companies have a large portion of their data in repositories outside of Google.
Some examples actually looked like they could have been done without AI. During an e-commerce demonstration, for example, the presenter called the seller to complete an online transaction. It was designed to show off the communication capabilities of a sales bot, but in reality the step could have been easily taken by the buyer on the website.
That’s not to say that generative AI doesn’t have powerful use cases, whether it’s creating code, analyzing a corpus of content and being able to query it, or being able to ask questions about log data to understand why a website went down. Additionally, the task- and role-based agents that the company has introduced to help individual developers, creatives, employees, and others, have the potential to leverage generative AI in tangible ways.
But when it comes to building AI tools based on Google’s models, instead of using the ones that Google and other vendors create for its customers, I couldn’t help but think that they ignored many obstacles that could arise in the project. means of successful implementation of generative AI. Although they tried to make things look easy, in reality, implementing advanced technology within large organizations is a huge challenge.
Big change is not easy
Just like other technological advancements over the last 15 years – whether it’s mobile, cloud, containerization, marketing automation, etc. – it was accompanied by numerous promises of potential gains. Yet these advances each introduce their own level of complexity, and large companies are proceeding more cautiously than one might imagine. AI appears to be a much bigger asset than Google, or frankly any of the big vendors, are letting on.
What we’ve learned from these previous technological changes is that they generate a lot of hype and lead to a ton of disillusionment. Even after several years, we have seen large companies that perhaps should benefit from these advanced technologies simply try, or even not use them, years after their introduction.
There are many reasons why businesses may fail to take advantage of technological innovation, including organizational inertia; a fragile technology stack that makes it difficult to adopt newer solutions; or a group of business opponents who shut down the most well-intentioned initiatives, whether legal, HR, IT or otherwise, who for various reasons, including internal politics, continue to say no to substantial changes.
Vineet Jain, CEO of Egnyte, a company that focuses on storage, governance and security, sees two types of companies: those that have already made a significant shift to the cloud and will have an easier time adopting the Generative AI, and those that have been slow and will likely struggle.
He talks to many companies that still have the majority of their technology on-premises and still have a long way to go before they start thinking about how AI can help them. “We talk to a lot of cloud ‘late adopters’ who haven’t started or are very early in their digital transformation journey,” Jain told TechCrunch.
AI could force these companies to think seriously about digital transformation, but they might struggle to get that far back, he said. “These companies will need to solve these problems first and then use AI once they have a mature data security and governance model,” he said.
It was always the data
Large vendors like Google make implementing these solutions appear simple, but like any sophisticated technology, looking simple on the front end doesn’t necessarily mean it’s simple on the back end. As I’ve heard often this week, when it comes to the data used to train Gemini and other large language models, it’s always a case of “garbage in and garbage out”, and that is even more applicable when it comes to generative AI.
It starts with data. If your data house is not in order, it will be very difficult to get it in shape to train LLMs on your use case. Kashif Rahamatullah, a principal at Deloitte and head of his company’s Google Cloud practice, was very impressed by Google’s announcements this week, but still acknowledged that some companies that lack clean data will have difficulty implementing generative AI solutions. “These conversations might start with a conversation about AI, but it quickly turns into: ‘I need to fix my data, I need to clean it, and I need to have it all in the same place, or almost, before I start reaping the true benefit of generative AI,” Rahamatullah said.
From Google’s perspective, the company has created generative AI tools to more easily help data engineers create data pipelines to connect to data sources inside and outside the Google ecosystem. “It’s really about accelerating data engineering teams, automating a lot of very labor-intensive tasks involved in moving data and preparing it for these models,” Gerrit Kazmaier, vice-president President and CEO of Databases, Data Analytics and Looker. at Google, told TechCrunch.
This should be useful for connecting and cleaning data, especially in companies that are further along in the digital transformation journey. But for companies like those Jain mentioned — those that haven’t taken significant steps toward digital transformation — it could present more challenges, even with the tools Google created.
All of this doesn’t even take into account that AI comes with its own set of challenges beyond simple implementation, whether it’s an application based on an existing model, or especially when is trying to create a custom model, says Andy Thurai, an analyst at Constellation Research. “When implementing either solution, businesses should consider the governance, accountability, security, privacy, ethical and responsible use and compliance of these implemented,” Thurai said. And none of this is trivial.
The executives, IT pros, developers, and others who came to GCN this week may have been looking for what’s next in Google Cloud. But if they haven’t pursued AI, or they’re just not ready as an organization, they may have left Sin City a little shocked by Google’s complete focus on AI. AI. It may be a long time before organizations lacking digital sophistication can take full advantage of these technologies, beyond the more comprehensive solutions offered by Google and other vendors.
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