Video: Where should you invest your AI dollars?

I want to talk to you about ways to be able to level up in terms of your capabilities in AI.

I’ve got three things there that I wanna talk to you about as well.

First of all, the huge promise around natural language processing.

Second what you can now take advantage of from an open source perspective.

And then third, if cloud as if cloud wasn’t enough, there’s now cloud native offering new capabilities and new opportunities that I wanna make sure are on your radar as well.

So first of all, if you’re going to be able to deploy, and if you’re gonna be able to deploy ai, one of the first sets of considerations should be what kind of data do you have and what kind of problems are you trying to solve?

I find that for both of those things, natural language processing, you know, raises some interesting opportunity areas.

In particular, the ability to use now the words of customers to better understand things like customer complaints better understand root causes of customer experience issues.

There’s a huge ability to now unlock all of the potential insights that are in that, in your narrative text that exists throughout your organization, uh, and be able to really harness that.

And NLP as a part of AI enables you to do that.

You can also look at N L P use cases to be able to respond to regulatory requests and for risk management.

I think that this area here about going deeper with NLP is really one of the ways that you level up in terms of your capabilities.

I think the second, uh, point is about, you know, there used to be this huge lock in with established software and technology vendors that really determined how fast you could go with artificial intelligence.

And of course, if you’re reliant on a core provider for key systems, yes, there’s still a degree of lock-in.

But if you get past your core systems and you think about how you analyze data, there’s now new capabilities that are, that are available to you to be able to do types of analysis, be able to put tools in people’s hands without huge upfront investment.

And I think that this is one of the most exciting areas about how rapidly machine learning and data science is evolving.

Then third, I just wanna talk just for a moment about this growing concept behind cloud native. And so it’s not just shifting existing storage and compute from on-premise to the cloud, but really beginning with the cloud, thinking about the cloud first.

And if you think about how easy it is to scale your compute resources and your storage resources, and to be able to, you know, really just sort of switch that on in just a moment it really just enables new ways of tackling problems that used to take teams of people and huge budgets.

And now it literally can be flick of a switch.

Now, there are a couple of potholes that you do have to consider.

One of the most important things in data science, this is a list of key success factors, but really what it comes down to is the people and your team.

And so being able to cultivate the right talent is absolutely the most important factor in your success in AI.

And I think that as you think about that, it means that you can expand past a traditional idea of a data scientist and to be able to leverage other people in your organization if you have the right tools that you can put in their hands.

And so this leads to more of the discussion about low-code and no-code solutions to get business analysts functioning as data scientists.