Recently, eWeek Senior Editor James Maguire interviewed our COO Krishna Subramanian about her latest thinking on AI and data management. Watch the full video here and read our highlights below!
JM: It’s been a fast two years since ChatGPT came on the scene. There’s already been so much evolution with many companies scrambling to make the most of AI with some success or limited success in some cases. What do you see now in terms of businesses and where they’re focusing their AI strategy?
KS: A lot of companies have invested in training their own models. That’s where we are seeing innovation right now. As these models are stabilizing, enterprises are starting to figure out how to use them with their own corporate data. That’s what we mean by inferencing. Let’s say you have a model that can recognize images, so it knows if James is in an image or if the eWeek logo is in an image. Now you want to give it all of eWeek’s images to find those that have James doing a podcast with eWeek’s logo in it. That’s an example where the model was first trained to recognize images using a different data set, but now you’re using the pre-trained model with your own corporate data. Inferencing is nine times larger than the training market.
JM: People are trying to figure out how to use AI with their corporate data and there are two big challenges. The first one is, anybody in the company should be able to use AI, so the chance of data leakage or improperly sharing sensitive data increases exponentially. The second problem is, how do you help people find and feed the right data to AI and how do you do it with control? You don’t want a shadow AI movement, right?
KS: Correct. IT needs to deliver systematic management of the AI data workflow with oversight. We do an annual survey to find out the top issues for unstructured data and for the last two years, the feedback we received is: we’re exploring AI but not really using it. Most companies are kind of paralyzed by AI; they don’t know how to use it with their data. The good news is that there is a place where you can start right now. The first step is just indexing the data. Then you at least know what data you have and you can create systematic data workflows around it with automation. There are data management solutions that are starting to address this problem.
The first step is just indexing the data. Then you at least know what data you have and you can create systematic data workflows around it with automation.
Read more: AI Inferencing: What Your Data Platform Needs a Makeover
JM: What are some future areas where you see AI and data management evolving??
KS: A fascinating area where we’ll see more work is in the human AI interaction. Right now, people are thinking. oh AI is going to take over and then people will get cut out. The reality is that there are going to be some AI helpers to assist a human and there’s going to be human oversight over everything the AI does. So how do you make that interaction easier and how do you enable iteration over it? There will be a human-enabled AI workflow evolving to address this.
I also hear so much these days about agentic AI. The AI agent doesn’t give an answer like ChatGPT but it does a series of tasks and becomes a digital worker.
JM: How can Komprise help customers with this journey, in a nutshell?
KS: Komprise analyzes, indexes, moves and manages unstructured data–meaning any data that’s not in a database. If you want to understand and classify your unstructured data or you’re wondering how you can build data workflows for it, those are all the things that Komprise can do.
Learn about the Komprise Global File Index.
Learn about Komprise Smart Data Workflows.
