You’ve probably read about AI-generated content getting facts laughably wrong. Or you’re panicking over the prospect of AI rendering many jobs (and maybe yours) unnecessary. 

Or maybe: You’re not freaking out at all—instead, you’re thoughtfully sorting through the hype and  figuring out how to get the most out of AI for your business.

If you’re in the latter group, good for you, because you’ll think first before shelling out the budget for AI adoptions and applications that don’t make sense for your business. Of course, it won’t work for your C-suite to stick their collective heads in the sand either, since the AI revolution is coming to your enterprise whether you want it to or not. 

As cooler heads have discovered, there are immensely promising enterprise applications for generative AI. Generative AI marks the first time that AI has been effectively put into the hands of non-coding employees, just about guaranteeing its adoption. Generative AI also has the power to boost employee productivity and flatten your data and business intelligence workflows. And here’s what else is coming: internal versions of ChatGPT, where people can ask questions and get answers from the enterprise’s own data.

But before the decision makers decide what to purchase, you have to decide what you’ll actually do with it—and if your IT infrastructure is ready for it. Here are a few ideas to jump-start your thinking:

  1. Creating new lines of business. Entire businesses are spun up daily that would not have been possible before generative AI and language models. The ideas may have been floated, for sure—but now they can be developed quickly and economically. It’s important for enterprises to think along these lines—because if they don’t, the competition will.
  2. Data analysis for everyone. Gaining the ability to layer natural language on top of data analysis is huge. It puts the power of learning from data into the hands of everyone in your organization—not just the actual data scientists. The questions can be as simple and straightforward as those you’d pose at a meeting, like, “How have my customers changed in the last three years?” The response will be in plain language and easily understood—no translation from a data analyst required.
  3. Generating images, infographics, and video. With a few prompts, anyone who needs to create a deck in a hurry can obtain stunning images. The use of AI tools to create art can be iterative—meaning designers can use the images as a starting point to save time. This is how open beta generative AI tool Midjourney works, similar to DALL-E from OpenAI and Stable Diffusion. NVIDIA and Salesforce’s joint backing of Runway, which lets you create video clips from text prompts, signals the start of some serious capabilities for teams needing B-roll without the production lead time.

Upload a file to your generative AI system and ask it to read and create visuals and charts based on data in just a few minutes. AI can analyze images and videos, too.

  1. Supercharged search. Internal search can be far more than lists of content. Search results created with generative AI can include answers to related questions, giving you insights that go beyond the simple search term you may have included. It’s the follow-up questions before you’ve even thought about the follow-ups. Google is making its own search engine richer with generative AI, like adding descriptions and reviews to simple shopping searches

“Companies are now building searches specifically for the workplace, built for internal searches that work across your internal system,” said Phu Nguyen, Head of Digital Workplace for Pure Storage, at a recent VentureBeat Spotlight event on generative AI. “This is all very exciting for us because we think of this as part of our employee information center strategy. Previously it was just an intranet and our support portal, but now we have this workplace search that can connect information across multiple systems inside our organization.”

When tailored with permissions to certain data sets, these could take on the role of copilots for mining data—whether for entire departments (sales copilots, or marketing copilots) or roles (CEO and CFO copilots).

  1. Swifter software development. It’s not just about building cool new apps, although that’s definitely part of the benefit of meshing development with generative AI. There’s also the benefit of automating repetitive tasks, detecting security gaps, and documenting code functionality. As McKinsey reports, generative AI can help developers jump-start the first draft of new code by entering prompts in their integrated development environment (IDE) for creating software. 
  2. Savvier SEO. Improve SEO by asking generative AI tools questions about top-performing keywords. It’s a good place to start comprehensive keyword search, get some estimates of search volumes, and see how your competitors are doing.
  3. More marketing ideas. Speed up the creative process by using generative AI for idea spitballing. Need a concept so the team can begin brainstorming? Generate 50 ideas quickly and weed them out from there. Can’t find the images you need on a stock image site to support campaigns? Ask generative AI to spin up a few to match your copy.
  4. Easier learning and training. There’s enormous potential to customize training using generative AI—or simply make it easier for learners to sift through volumes of training content. For example, Pure’s legal team is testing generative AI to summarize policy and regulatory changes to stay up-to-date on compliance laws as they evolve. The Association for Talent Development reels off many uses besides the usual like idea generation, but that’s just the start. HR and professional development professionals can tap into AI to analyze feedback from learners, create summaries of class material, or create role-playing scenarios to help learners practice their skills. 
  5. Streamlining office space. With fewer people commuting to work, office spaces need a re-think. Design tools for viewing spaces and iterating layouts have been around for a long time, but now generative AI can speed up that process enormously. Generative AI tools can gather data about how many people use an office space, fluctuating temperatures, light exposure, and much more to suggest designs that suit the ways people are working. Global architecture firm Zaha Hadid Architects has a specific practice area dedicated to using AI to design office spaces
  6. Synthesizing everything. The power of generative AI is not entirely knowing what you need—and getting answers anyway. “This generative technology, when we pair it with search, and not just single searches, gives us the ability to say, ‘I’m going on a business trip to X. Tell me everything I need to know,’” said Eddie Zhou, Founding Engineer of Glean, at the recent VentureBeat Spotlight event. “An LLM agent can go and figure out all the information I might need and repeatedly issue different searches, collect that information, synthesize it for me, and deliver it to me.”

Where Does Data Storage Fit In with Generative AI? 

While many exploratory GenAI projects are done in the cloud and via SaaS, we find an increasing number of enterprises making the investment to leverage AI at scale, on-premises. 

Our analysis suggests that most organizations that are exploring Generative AI are investing in Large Language Models (LLMs). Some are building their own LLMs, while many are leveraging existing open source models (e.g. LLaMA 2) as a base, re-tuning and retraining it on their proprietary data, adding guardrails to prevent hallucinations. Their goal is to adapt it to drive outcomes specific to their business needs.

As customers put together their AI infrastructure for GenAI and their other AI workload requirements, they are struggling with inefficiencies, complexity, data growth, and the pain of integrating evergrowing AI tools and applications. Pure Storage can help with every step along the way and has a proven track record of enabling 100+ companies in their AI journey.

Learn more about Enterprise AI considerations as well as the future of AI, learnings and tips for successful AI initiatives from data scientist and AI expert, Dr. Kirk Borne.

Learn more about how generative AI could impact data infrastructures and storage as adoption increases.