From Niche Technology to Enterprise Core: Key Takeaways from NLP Logix on the Rise of Language AI

Remember that time when “AI” sounded like something straight outta Star Wars? Yeah, me too. Feels like eons ago, right? Fast forward to now, and it’s like AI is everywhere, kinda like those squirrels hoarding nuts, but instead of nuts, it’s our data.

But hold your horses before you go full-on AI-obsessed. In a recent episode of Emerj’s “AI in Business” podcast, Matt Breseth, the co-founder and CIO of NLP Logix, dropped some serious truth bombs about the rise of what the cool kids call “language AI.” Think natural language processing (NLP) and those large language models (LLMs) that seem to be everywhere these days. Matt’s insights are like gold for business leaders trying to navigate this whole AI thing.

So, grab your coffee, tea, or heck, even a green smoothie, and let’s dive into the juicy bits of this convo.

Prioritize Value and Focus in Technology Investments

Okay, first things first. We all know tech can be seriously tempting, like that last slice of pizza. But just like you don’t wanna overdo it on the pizza (unless you’ve got an iron stomach), you gotta be smart about tech investments.

Don’t chase technology for its own sake

Matt dropped a major truth bomb here: Don’t go chasing the latest shiny tech just because it’s, well, shiny. It’s gotta solve a real business problem you have, not a problem you think you should have because it’s trendy. Think about what your customers actually need. Are they struggling to navigate your website? Is your customer service team drowning in emails? Find the pain points, then look for tech solutions that can actually ease the pain.

Continuously assess the value of AI investments

AI isn’t a “set it and forget it” kind of thing. It’s more like a Tamagotchi (remember those?). You gotta keep feeding it data and making sure it’s actually thriving. Regularly check in on your AI projects: Are they delivering the results you expected? Are they worth the investment? Be honest with yourself. If something isn’t working, don’t be afraid to pull the plug or pivot to a different use case.

Adopt an engineering mindset

Think of AI as a work in progress, kinda like that sourdough starter you’ve been meaning to get back to (no judgment!). It needs constant tweaking and refining. Adopt an iterative approach, using data to train your AI models and make them smarter over time.

Start Small and Scale Gradually

Jumping headfirst into AI can feel overwhelming, like trying to understand the plot of “Inception” after a long day. The key is to start small and build momentum gradually. Kinda like dating, but with less ghosting, hopefully.

Demonstrate tangible value quickly

Nobody wants to wait around for results. NLP Logix found success by focusing on projects that delivered clear, measurable results within a reasonable timeframe, like 90 days. Think of it as a proof of concept that shows the potential of AI to your stakeholders. Once you’ve got their buy-in, you can start scaling things up.

Embrace the evolution of NLP

Remember when chatbots were all the rage, but they were kinda clunky and frustrating? Yeah, NLP has come a long way, baby! It’s becoming increasingly sophisticated and integrated into our everyday lives, often without us even realizing it. The key is to stay ahead of the curve and embrace the ever-evolving nature of language AI.