Generative AI or Gen AI is seemingly everywhere these days. It’s no longer just a buzzword among tech folks. It’s mainstream. It’s no longer the future. It’s here and now. We read about it in the news. And it’s a priority topic in boardrooms across the globe.

Doing what I do, I spend a lot of time talking with people about this subject. I usually ask if they have any initiatives from senior leadership to start bringing Gen AI into their organizations. And without fail every single one of them says, “Yes.” Their CIO or CXO says it’s important and they need to start looking at ways to bring Gen AI into their enterprise data.

Before digging into that, let’s take a step back.

Why AI and Gen AI defined

So, why AI and why now? The growing volume of data, the variety of data types, and the complexity of managing this data across locations can be overwhelming. Gleaning insights from this vital corporate asset can be challenging. Now with AI, you can unlock insights from your entire data estate.

Gen AI is a type of artificial intelligence (AI) that uses machine learning (ML) and deep learning algorithms to produce new content like images, text, music, video, and computer code in response to queries.

Ok, back now to bringing in Gen AI into your business.

Deployment challenges of Gen AI

With the emergence of cloud computing, there was a term called “shadow IT.” Folks could put down a credit card and get a virtual machine or workload. Databases could be spun up quickly. Or, they could try to provision it through their internal infrastructure services, which might take a week or so.

We’re seeing a lot of parallels in this space and the emergence of “shadow AI.” Now, people can give a credit card to open an application API key and start to download an application off GitHub. Or they can start building their own. Or they can go to a SaaS provider and put their API key in and start using it.

It’s that simple. Or is it? There are tools out there that can improve your overall quality of life in day-to-day operations. But you need to step back and think: is it safe for me to do this with my data?

Building out frameworks and policies

It’s important to know that Gen AI is not just going to impact IT and applications. It cuts cross-functionally across an entire enterprise. Once you think about it in those terms, you’ll realize that you need to build out frameworks and procedures for implementation. Here are three recommended steps to follow.

  1. Build cross-functional internal committees to help govern access, procedures, and policies around AI/ML and Gen AI. Once you get all the key stakeholders together, you can start talking about classifications of data, types of data, types of applications, and types of use cases that you want to be able to develop to help bring into the organization. Once you’ve got your cross-functional committee, the next big challenge is data. Data is key to these strategies and initiatives.
  2. Find where your data exists—across all over your enterprise sprawl. It needs to be consolidated. And it needs to be deduplicated. And you need to identify which copy of that particular file is the right one to use.
  3. Determine data access. Once you have the data, and you know it’s the single source of truth, you need to figure out whether it should be allowed to be used externally with models. Or not? And if it is to be accessed, what sort of kind of governance do I need to apply to this? Who in my organization should be allowed to access this?

Building out these frameworks, procedures, and policies will make things easier as you further on this journey to integrate AI with your business.

Cohesity, AI, and what’s next

Last year, to address the rapidly evolving needs our customers have with AI and data, we unveiled Cohesity Turing—our comprehensive, patent-pending collection of AI capabilities and technologies that are integrated into our multicloud data platform and solutions.

We see AI—using your own data, securely and responsibly—as a game changer. Remember what I said above: AI cuts cross-functionally across your entire enterprise. And, as new AI use cases emerge, we’ll continue to advance our portfolio of technologies powered by Cohesity Turing.

Now, we invite you to the next step in this AI journey. Please join us for a one-hour virtual event on February 28 at 10 a.m. PT to learn how to power smarter business decisions, faster, with conversational AI. Want to unlock deep insights and learnings from your own data? Tune in to find out how!

Learn more

And, if you want to hear more about Gen AI, be sure to watch my recent “Tech Insights” conversation below, with Theresa Miller, director of the technical advocacy group here at Cohesity.

Greg Statton headshot
Greg Statton
Greg Statton headshot
Greg Statton

Office of the CTO - Data & AI

Greg has spent over 20 years architecting enterprise IT strategies and building next-generation data center solutions and products. Since joining Cohesity, Greg has held various positions in technical marketing, field architecture, and now runs a team of SAs and FTDs.

See All Authors
Office of the CTO - Data & AI
Greg Statton headshot
Greg Statton
Office of the CTO - Data & AI
Greg Statton headshot
Greg Statton

Office of the CTO - Data & AI

Greg has spent over 20 years architecting enterprise IT strategies and building next-generation data center solutions and products. Since joining Cohesity, Greg has held various positions in technical marketing, field architecture, and now runs a team of SAs and FTDs.
See All Authors

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