We were invited to join leaders in Digital Transformation for a conversation on improving business with data. During the Fireside Chat with Dave Findlay, Fuse Data CEO, there were three areas that shined through. The common thread focused on decision making with better data.
To help you pull some usable details, we’ve laid it out for you in these articles:
Considering AI/ML and Generative AI
Listen in to the full Fireside Chat here
We see people get excited about new technologies like AI and think it looks easy. How does considering AI play into the mindset of the Leaders you work with?
In short, it's not. It looks easy and some of the stuff, is easy to go an inch deep on. When you talk about enterprise level applications, it's not easy and that takes time for technology teams to absorb and get good at. And that's okay.
I find there's a saturation point with the business in terms of how much change they can absorb. So a technology team could eventually get to a point where they're moving at a certain velocity but the business is only able to accept so much change at one point in time.
That depends on the industry, the industry's culture and how they absorb change. It could depend on how busy the function is, like, if you're trying to introduce data and analytics capability into a finance function that's going through like a reorg, that's not going to work. They can't absorb any more change. So you have to be aware of that saturation point in the business as well when you're introducing new stuff.
Can you talk a bit about Generative AI and where you see this conversation heading?
It’s generating a lot of conversation. So, for this conversation, there's Generative AI and then non-Generative AI (AI/ML).
Generative AI
On the Generative AI side of things, I find it's a little bit of a distraction at this point. At least I can speak from the data and analytics space where there's a lot of data and analytics practitioners within companies, a lot of technology functions, looking at this technology and then trying to fit it to a problem. And that typically hasn’t worked in our industry. You never start with the technology and then go look for a problem somewhere to apply that successfully. It's kind of like if you have a hammer, everything looks like a nail kind of thing.
It's very cool. But again, going back to, “is it enterprise ready”? Is it even suitable for data and analytics use cases? Time will tell. But again, regardless of whether it's GenAI or it's something else that comes out in 12 or more months,
"let's not take the technology and try to find a problem, let's find the problem and then go back into our toolkit and see what's the best tool to solve that problem".
There's a lot of opportunities when you think about it. In some ways, a Generative AI can take its own knowledge and create data out of something that maybe wasn't data before. Are you seeing that potential and how would you apply it? Have you seen that applied effectively in your business? Like I said, I haven't seen any yet. I've seen a lot of prototypes and we've even prototyped some things to communicate some of the challenges and shortcomings to our customers. But, I haven't seen enterprise level use cases where I would apply in a repeatable and trustable fashion from a GenAI perspective.
Artificial Intelligence (AI) and Machine Learning (ML)
When we talk about more traditional applications of AI, I think, you know, there's tons of great ones. I think the audience as well would be aware of them from like preventative maintenance to quality control to, even like all sorts of classification types of models.
Like those are all fantastic use cases that are rock solid, have real positive and clear ROI. They're trustworthy and you can measure and monitor those models. So I think that's a lot more mature. We have to give the GenAI stuff a little bit more time to see if it gets to that point.
