This week in Las Vegas, 30,000 people gathered to hear the latest and greatest from Google Cloud. What they were hearing was all genetic AI, all the time. Google Cloud is first and foremost a cloud infrastructure and platform provider. If you didn’t know this, you may have missed it in the onslaught of AI news.
Not to minimize what Google was showing, but like Salesforce last year at its travel show in New York, the company failed to give anything but a passing nod to its core business — except in the context of genetic artificial intelligence, of course.
Google announced a series of AI enhancements designed to help customers take advantage of the Gemini Large Language Model (LLM) and improve productivity across the platform. It’s a worthy goal, of course, and throughout the keynote on Day 1 and the Developer Keynote the following day, Google rounded out the announcements with a healthy number of demonstrations to demonstrate the power of these solutions.
But many seemed a little too simplistic, even considering they had to squeeze into a keynote with limited time. They were mostly based on examples within the Google ecosystem, when almost every company has a lot of its data in repositories outside of Google.
Some of the examples really felt like they could have been done without AI. During an e-commerce demo, for example, the presenter invited the seller to complete an online transaction. It was designed to demonstrate the communication capabilities of a sales bot, but in reality, the step could have easily been completed by the buyer on the website.
That’s not to say that genetic AI doesn’t have some powerful use cases, whether it’s generating code, analyzing a set of content and being able to query it, or being able to ask questions about log data to figure out why a website crashed. Additionally, the task- and role-based agents introduced by the company to help individual developers, creatives, employees, and others, have the ability to benefit from genetic AI in tangible ways.
But when it comes to building AI tools based on Google’s models, as opposed to consuming what Google and other vendors build for its customers, I couldn’t help but feel that they were overcoming many of the barriers that could exist. on the way to a successful production AI implementation. While they tried to make it sound easy, in reality, it is a huge challenge to implement any advanced technology in large organizations.
Big change isn’t easy
Like other technology leaps over the past 15 years — whether it’s mobile devices, cloud, containers, marketing automation, you name it — it’s been delivered with lots of promises of potential profits. However, these developments each introduce their own level of complexity, and large companies are moving more carefully than we imagine. AI seems like it’s a lot bigger than Google is letting on, or frankly any of the big vendors.
What we’ve learned with these past technology changes is that they come with a lot of hype and lead to a ton of frustration. Even after several years, we have seen large companies that perhaps should have benefited from these advanced technologies, even just dabbling in or even staying out altogether, years after their introduction.
There are many reasons why companies 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 corporate naysayers who shut down even the most well-intentioned initiatives, whether legal, HR, IT or other groups who, for various reasons, including internal politics, continue to simply say no to meaningful change.
Vineet Jain, CEO at Egnyte, a company focused on storage, governance and security, sees two types of companies: those that have already made a significant shift to the cloud, and those that will have an easier time when it comes to adopting AI intelligence. and those who were slow and likely will struggle.
He talks to many companies that still have the majority of technology on-prem and have a long way to go before they start thinking about how AI can help them. “We’re talking to a lot of cloud ‘late adopters’ who haven’t started or are very early in their digital transformation effort,” Jain told TechCrunch.
AI could force these companies to think hard about making a path to digital transformation, but they could struggle starting so far back, he said. “These companies will have to solve these problems first and then consume AI once they have a mature data security and governance model,” he said.
It’s always been the data
Big vendors like Google make implementing these solutions sound simple, but like all sophisticated technologies, looking simple on the front end doesn’t necessarily mean it isn’t complex 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 still a case of “garbage in, garbage out,” and that’s even more true when it comes to genetic AI.
It starts with data. If you don’t have your data house in order, it will be very difficult to get it in shape to train LLMs in your use case. Kashif Rahamatullah, Deloitte’s director in charge of his firm’s Google Cloud practice, was mostly impressed by Google’s announcements this week, but admitted that some companies that don’t have clean data will have trouble implementing AI solutions . “These conversations can start with an AI conversation, but that quickly turns into, ‘I need to fix my data and I need to clean it up and I need to have it all in one place, or almost in one place, before I start to you get the real benefit of genetic AI,” Rahamatullah said.
From Google’s perspective, the company has built AI tools built to help data engineers more easily build data pipelines to connect to data sources inside and outside the Google ecosystem. “It’s really meant to accelerate data engineering teams by automating many of the labor-intensive tasks associated with moving data and preparing it for these models,” Gerrit Kazmaier, vice president and general manager for databases, data analytics and Looker at Google, told TechCrunch.
This will be useful for connecting and cleaning data, especially in companies that are further along in their digital transformation journey. But for those companies like the ones Jain mentioned—those that haven’t made substantial strides toward digital transformation—it could present more difficulties, even with the tools Google has built.
All of this doesn’t even take into account that AI comes with its own set of challenges beyond pure application, whether it’s an application based on an existing model, or especially when you’re trying to build a custom model, Andy says. Thurai, analyst at Constellation Research. “When implementing any solution, companies need to think about the governance, liability, security, privacy, ethical and responsible use and compliance of such implementations,” said Thurai. And none of this is trivial.
Executives, IT professionals, developers and others who went to GCN this week may have been looking for what’s next for Google Cloud. But if they weren’t looking for AI, or just aren’t ready as an organization, they might have left Sin City a little shocked by Google’s full focus on AI. It could be a long time before organizations that lack digital sophistication can take full advantage of these technologies, beyond the more comprehensive solutions offered by Google and other vendors.
