Image credit: Google
This week in Las Vegas, 30,000 people gathered to hear the latest and greatest from Google Cloud. All they heard was AI generating all the time. Google Cloud is first and foremost a cloud infrastructure and platform vendor. If you didn’t know that, you might have missed it in the onslaught of AI news.
Not to diminish what Google had on display, but like Salesforce at last year’s traveling roadshow in New York City, the company failed to give away everything but its core business — except the creative AI perspective. I.
Google announced a number of AI enhancements designed to help users take advantage of the Gemini Large Language Model (LLM) and improve productivity across the platform. It’s certainly a worthy goal, and during the Day 1 keynote and the developer keynote the following day, Google peppered the announcement with multiple demos to illustrate the power of these solutions.
But many seemed a bit too simplistic, even taking into account the limited time they needed to squeeze in keynotes. They mostly relied on instances within the Google ecosystem, when almost every company has most of its data in repositories outside of Google.
Some instances actually felt like they could have happened without the AI. During an e-commerce demo, for example, the presenter called a vendor to complete an online transaction. It was designed to demonstrate the communication capabilities of a sales bot, but in reality, this step could easily be completed by a buyer on a website.
That’s not to say that there aren’t some powerful use cases for generative AI, whether generating code, being able to analyze and query a corpus of content, or asking questions of log data to understand what the web is all about. Why did the site go down? What’s more, the task- and role-based agents the company has introduced to support individual developers, creatives, employees and others have the potential to benefit from creative AI in tangible ways.
But when it comes to building AI tools based on Google’s models, as opposed to using the tools Google and other vendors build for their customers, I couldn’t help but feel that many removing obstacles that may stand in it. How to implement a successful generative AI. Although they tried to make it look easy, in reality, implementing any modern technology within large organizations is a huge challenge.
Big change is not easy.
Like other technological leaps over the past 15 years – whether mobile, cloud, containerization, marketing automation, you name it – it has been delivered with many promises of potential benefits. Yet these developments each introduce their own level of complexity, and large companies proceed more cautiously than we imagine. AI feels like a much bigger lift than Google, or frankly any of the big vendors, are giving it.
What we’ve learned from these previous technology changes is that they come with a lot of hype and a lot of disappointment. Even after many years, we have seen large companies that may be taking advantage of these innovative technologies that are still only flirting or sitting out years after they were introduced.
There are many reasons companies may fail to take advantage of technological innovation, including organizational inertia; A brittle technology stack that makes it difficult to adopt new solutions. Or a group of corporate incompetents blocking even the most well-intentioned initiatives, be it legal, HR, IT or other groups who, for a variety of reasons, including internal politics, simply say no to fundamental change. .
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 those that are not ready when it comes to adopting generative AI. will have an easy time for And those who are slow movers will likely struggle.
He talks to many companies that still have mostly tech on premise and before they start thinking about how AI can help them. “We talk to a lot of ‘late’ cloud adopters who haven’t started their digital transformation journey or started too early,” Jane told TechCrunch.
AI may force these companies to think hard about participating in digital transformation, but they may struggle far behind, he said. “These companies will need to address these issues first and then use AI once they have a data security and governance model in place,” he said.
It was always data.
Big vendors like Google make these solutions easy to implement, but like all cutting-edge technology, looking simple on the front end doesn’t mean it’s uncomplicated 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 when it’s generated This applies even more when it comes to 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 on your use case. Kashif Rahmatullah, a Deloitte principal in charge of the Google Cloud practice at his firm, was mostly impressed by Google’s announcement this week, but he still acknowledged that some companies that lack clean data may need generative solutions. AI solutions will be difficult to implement. “This conversation might start with an AI conversation, but it quickly turns into: ‘I need to fix my data, and I need to clean it, and I need it all in one place. Or almost a place, before I start getting real benefits from generative AI,” Rehmatullah said.
From Google’s perspective, the company has built creative AI tools to help data engineers easily build data pipelines to connect data sources inside and outside the Google ecosystem. “The goal is to really speed up data engineering teams, by automating many of the most labor-intensive tasks involved in moving data and preparing it for these models,” said Gerrit Kazmaier, vice president and general manager for Databases. Data analytics and Looker at Google, told TechCrunch.
This should be helpful in aggregating and cleaning data, especially in companies that are at the forefront of the digital transformation journey. But for the companies Jain cites — those that haven’t taken meaningful steps toward digital transformation — it can 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 implementation, whether it’s an app based on an existing model, or especially when trying to create a custom model. Going, says Andy Thorai, an analyst at Bridge Research. “While implementing any solution, companies need to think about governance, liability, security, privacy, ethical and responsible use and implementation of such,” Thurai said. And none of this is trivial.
Executives, IT professionals, developers and others who attended GCN this week may be looking to see what’s next for Google Cloud. But if they haven’t gone looking for AI, or they’re simply not ready as an organization, they may have come away from Sin City a bit underwhelmed by Google’s total focus on AI. In addition to the more packaged solutions offered by Google and other vendors, organizations lacking digital sophistication can take a long time to take full advantage of these technologies.