Information, or content, is nontabular, unstructured data. Sometimes called “dark data” it includes documents, emails, contracts, invoices, media, and other files that exist outside tabular data. Content is also the essential fuel for generative AI (Gen AI).
Generative AI can also be used to better manage and find value in information. In this blog post, we’ll dive into the value of using generative AI to manage content.
Customer use cases provide a real-world demonstration of the value of using Gen AI to manage information. At OpenText, our customer use cases are varied, but there are similarities worth noting.
Most often we see customer wanting to use Gen AI to leverage large volumes of information. Our customers are trying to gain insights from that information to make more informed decisions and to do that faster. The focus in a lot of these situations is about getting rid of digital friction - the unnecessary time and effort and resources that it takes to get to the information that's needed to get work done.
Our customers see digital friction as inhibiting their success whether because digital friction creates subpar employee experience when employees can’t get the information they need or prevents the delivery of proper outcomes to customers.
Most of the early use cases for OpenText customers fall into the realm of knowledge, management, customer self-service, employee productivity, and content creation. Some of our customers investing in Gen AI are using it to better manage and understand content that is important for regulatory and compliance reasons.
What really interests me is the possibility of better servicing customers, whether that's an internal customer or an external customer. As consumers, we know how frustrating it is when we're trying to get an answer and we're having a problem. Despite all the automation and all the knowledge bases, we can’t find the desired answer.
My utopia is quickly getting the answer that I need with Gen AI so I can just move on with the other things I need to do in my day. I absolutely hate waiting on a phone to talk to somebody and explain things repeatedly. That efficiency gained through Gen AI translates to productivity on the on the professional front. Gen AI allows you to get rid of the low-value busy work and provides more opportunities for strategic thinking. It frees my time to develop thought leadership materials, like this blog post, and to work with our customers to uncover new opportunities.
At OpenText, we are doing test projects with Content Aviator, OpenText’s Gen AI tool for all OpenText content platforms. Currently, we are working on conversational search and content summarization/translation. There are a few criteria that help ensure Gen AI implementation is successful.
If you have these elements already in place, you have a leg up on success with Gen AI because you will be well-positioned to realize quick wins.
As we work on Gen AI projects, we're looking for repeatable use cases and measurability of results. We are studying which projects result in the best return in the near term. When experimenting with Gen AI, it’s important to start small and iterate. In other words, fail fast. This allows us to help our customers demonstrate early value and build trust, both with the C-suite and the users.
It’s easy to feel overwhelmed with Gen AI but remember everybody is in the same boat. Your organization’s investment in Gen AI is not a single project. It’s a process. I recommend working with your vendor to engage first in a discovery process to talk through what is possible and how to move forward safely with the technology.
This blog post is based on the transcript of an original AIIM OnAir podcast, recorded on April 22, 2024. Listen to the full episode. AIIM used the Pro version of Anthropic's Claude.ai to convert the transcript to a blog post and then the post was edited by AIIM staff and the author.