What’s most important is really business’s ability to adapt around this technology. The horse is out of the barn and if you don’t adopt now, you will miss the boat. Organizations also need to focus on innovation. Generative AI has got a lot of employees thinking creatively and coming up with ideas, and culturally organizations should be fostering that. Additionally, data is going to become more important than ever and continue to be a sticking point for the success of this technology.
I think a lot of executives right now don’t want to say they don’t have a good handle on their data. But I think there is a mindset that a lot of organizations capture data, put it in a lake, store it somewhere but then it’s not really managed or invested in. Right now everyone wants to focus on the new shiny thing with generative AI, but we need to really shift and start thinking about what we can do with data to drive the value of generative AI.
In addition to my role as a Chief Data Officer, I’m also an adjunct professor and I think a lot about generative AI in training and education. This technology is really only as good as it’s training, and if we are using it the right way. If the model isn’t trained with the knowledgebase we’re trying to tap into, then we aren’t going to get the information we’re looking for. The data has to be more refined and well-curated. One of the great things about this technology is it can really speak to different ways people want to learn. With the right knowledgebase, it can be tailored to the way you want to learn, and generative AI can be a great tool for augmenting learning. I think that’s a really good use of generative AI for advancing humanity, versus this idea of it putting us out of jobs.
An example is let’s say there’s a training people are required to take but a lot of them are just letting it run in the background and taking the test without having listened to any of the content. They may pass the test but they didn’t really listen to the information. In a generative AI world, we can make these trainings a dialogue or a chat that is interactive and walks individuals through concepts. In this case, you’re learning as you go and having the conversation in the style you want to have. I think in use cases like that, there is a lot of opportunity to boost compliance.
Another example would be a phishing email. With generative AI, we could have content produced that helps you understand why it was a scam. Now people could interact with custom content learn more about the situation and get coaching to prevent it from happening again rather than having to retake a long training course. There is a lot of opportunity for coaching and nudging versus a traditional training setting.
I think the quality of content now becomes more important through all of this. Instead of being concerned with the production of information, now we can focus more on what is being said and if it’s valuable for consumption. From the audience’s perspective, it may also allow them to interact with more content the way they want to, such as the modality. Generative AI is really a transformation in terms of how we learn and consume information. We aren’t entirely sure at this point where these capabilities take us, but it will really bolster self-service. There is also a conversation to be had about what we are seeing if it can be trusted and if there is bias or false information. As we move forward, we may need to develop ways to validate information with generative AI. For example, being able to check if something was a peer-reviewed article. At this point, the future is wide open and we don’t know what’s going to happen next. We have this amazing technology and now we need to think about how we can advance it while protecting ourselves.