Artificial Intelligence (AI) has been part of our working lives for long enough that we are now able to see the impact it has made in areas like productivity, efficiency and security. On Episode 5 of the Florida Tech & Growth Podcast, Paul was joined by three special guests who shared their insights on AI in their organizations.
We heard from:
about the impact of AI on software engineering, the future of junior engineering roles, and project management and documentation. Read on for their insights on AI-driven efficiency, or listen to the full conversation here.
Zach: “I think it’s incredible what we’ve been able to do with AI. For me personally, it’s changed how I work day-to-day quite a lot. We use GitHub Copilot, and we’ve found another tool that I think is actually even more powerful when it comes to delivering software, which is Cursor. It’s an AI-enabled code editor that goes one step further to do things like tab autocomplete. You can also use it to talk to your codebase in a new way, so that a new engineer can onboard incredibly fast to the codebase. If you have something that you want to build, you can talk to the software and add tests or functionalities, check for bugs, etc.—even enforce a principle. That’s rapidly increased our ability to deliver prototypes and proof of concepts, which is incredible for us as an early-stage startup because a lot of what we’re doing is trying new features, trying new functionality, and seeing what sticks.
Beyond that, I’ve also used it pretty heavily for anything that interacts with our data warehouse. We use Looker as our BI tool, and it’s attached to our data warehouse. We have to write LookML and other types of SQL to visualize the data. That is incredibly easy now compared to three years ago, because you can give it your data model rather than write the code. I personally use AI all the time for things like summaries and transcriptions. I will very often dictate my own notes and then I will ask it to synthesize my muddy stream of consciousness into something a little bit cleaner, which ultimately produces written content way faster than I could write it myself. It’s insane how quickly AI has become a cornerstone of my personal workflow, as well as something that I evangelize to the team.”
Nack: “Since we’re an early-mid-stage startup, AI definitely affects funding rounds. Since it’s such a hot topic, a lot of VCs are interested in putting money into AI, which is something we need to be careful about because, in my opinion, we shouldn’t use AI just for the sake of using AI. We need to make sure that it’s actually a tool that will help in the long run for the company. Sometimes that differentiation is hard to make because AI is such a craze. But, VCs aren’t really concerned about that.
I do think AI is going to be a more long-lasting thing than previous crazes like the Metaverse or NFTs. However, we need to be careful that, just like any other tool, it’s actually helping the company instead of just being there because you want to slap on the AI label. Right now, there’s underlying pressure to make something work that relates to AI, because there’s a better chance to get more funding in the future if we do.”
Michael: “With regards to AI adoption, I think each company has to assess what their risk appetite is when it comes to a rapidly evolving technology. Your adaption is going to be a little bit different depending upon your organization. One of the things that everyone needs to be keeping top of mind from a technology point of view is that AI is very empowering, but the underlying data that the AI is using has to be of quality, otherwise you’re going to get very misguided information. That’s something that often gets overlooked.
I don’t think AI is like blockchain, where it was a hammer trying to find a nail, but I do think that AI has very key underlying dependencies, which include your data and your ability to leverage cloud services. If you’re trying to use AI based on a private data center, all on your own, you’re going to be in a very difficult position. Companies have to keep in mind some of those key dependencies and assess how far along they are in their cloud adoption journey and how mature they are with data governance and data quality in order to be successful with AI. Otherwise, you may be setting very lofty goals and setting yourself up for failure.”
To find out more about effectively adopting AI into your organization, tune into Episode 5 of the Florida Tech & Growth Podcast here.