Why you can't have good gen AI without good data

Even a year into the gen AI era , many executives are still pondering the best ways to put generative AI to use. So we decided to talk to a few hundred of them and — like a good large language model — condensed their feedback into a useful report on the five data trends at the core of effective generative AI deployment. Like pretty much any cloud or AI project that came before it, generative AI is heavily dependent on your data, how good and organized it is, so that’s where we’re focusing, too. You can read some of our main takeaways here, or download the full report .

Your organization may be ready to embrace generative AI, but is your data?

There’s little doubt that gen AI is here to stay. We’ve already seen just how much power this game-changing technology has to disrupt industries and transform our daily lives, but it comes with a caveat — you’ll need the right data to turn all the AI enthusiasm into tangible business impact.

Like all AI technologies, data is the crucial ingredient that enables gen AI models to learn, reason, predict, and improve output quality over time. And ultimately, future success will likely depend on how well companies cultivate their ability to access, manage, and activate data across enterprise systems, regardless of type or format, according to a new survey from Google Cloud .

Based on interviews with hundreds of data, business, and IT leaders about their strategies and goals for harnessing gen AI, the survey sheds light on where gen AI stands to make the most impact in the coming years. These leaders offer valuable insights about the changes we can expect to play out across the enterprise data stack in the AI era .

As we turn our attention from experimentation to wider AI implementation in 2024, here are five emerging trends you should have top of mind — and what they mean for your organization’s data.

Trend 1: Gen AI delivers faster insights for all.?

The vast majority of respondents surveyed indicated they expect gen AI to change how fast and, more importantly, who can access organizational data and necessary information. Some 84 percent said they believe gen AI will help accelerate their organization’s access to insights, and nearly two-thirds of data-decision-makers believe gen AI will democratize access to insights.

Gen AI stands to enable everyone — not just experts — to interact with and analyze data. This increases data literacy across your organization, boosts productivity, and empowers more team members to produce new insights instead of having to rely on the availability of specialized skill sets. Already, 52% of non-technical users are leveraging gen AI to gain insights, and many organizations say they see adoption across all lines of business. Marketing, advertising, and PR (62%), sales (47%), operations (42%), and product management (41%) were among the top types of business users already using gen AI to get insights.

The data stack takeaway: Connecting large language models (LLMs) to your business data will help close data and AI skills gaps and allow your workforce to “talk” to data through natural language. These “conversations” can happen in search-based or chat-based interfaces, which can help find information or support the creation of dashboards, reports, and visualizations.

Trend 2: The barriers between data and AI are disappearing.?

With gen AI enabling new ways to engage with data and AI platforms, 80% of respondents said they see the lines between data and AI roles beginning to blur. Formerly siloed data analytics, engineering, and AI teams are now collaborating more closely than ever before, with many of them seeking the same capabilities to bolster productivity and accelerate the data to AI journey.

Gen AI is also a chance to combat workforce shortages, helping to shore up existing teams so they can keep up with the pace of change. Multimodal models , text embeddings , and other advanced AI and ML technologies are enabling organizations to innovate faster, helping to simplify many of the most time-consuming data to AI processes, redistribute responsibilities, and reimagine workflows.

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