Why is next-gen AI so exciting?

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In our last post, we looked at the Thrio team’s experience in bringing AI capabilities to market

Have you wondered what all the fuss is about with generative AI? Or are you champing at the bit to deploy these capabilities for your agents, supervisors, and admins? No matter where you are on the spectrum of AI excitement, we can all agree that there are a few key things that make this last wave of development in artificial intelligence so exciting. The first is that the barriers to entry have fallen. And the second is that the work required to deploy and maintain these capabilities has shifted toward a less technical approach. 

So first, let’s look at the barriers to entry. Previously, bringing advanced AI into your business may have taken enormous investments and significant amounts of time. Even if you could get the budget approved, there were questions about the training data and the length of time required to get usable results. Some point-solution vendors promised the moon and failed to deliver anything that you could deploy in production. And even if you could get these capabilities to market, they may have been so limited in function that they didn’t add much value to your agent and customer experiences. 

Well, those days are over. The revolution in AI over the last few years is not just limited to the “generative AI” engines that have captured so much attention. While those are certainly useful and have lots of room to develop, other AI capabilities have also seen their barriers to entry fall. One example is in transcription, or speech to text. This was a prime example of a capability that was hard, expensive, and time-consuming to deploy. Big vendors had an advantage in terms of training data, and vendors liked to gatekeep these capabilities to their biggest and most well-heeled customers. But now, we’ve seen a sea change. Smaller vendors not only compete with the big players on price, but equal or outperform them on benchmark tests. Costs have come down while performance has increased. And the need to do bespoke training before deployment is virtually eliminated. So what does this mean for you in practice? 

Speed to (successful) deployment is everything. When senior leaders are either eager for new features yesterday or are keeping the budget tight with a pilot program, you need to deliver outstanding results, with high reliability, and quickly. Next-gen transcription can do that, and it’s also built right into Thrio. This means that the time to solution is incredibly rapid– a few clicks, and you’re ready to go with accurate, flexible AI powered transcription, right there inside Thrio. 

Next, let’s look at the shift toward “citizen developers” and away from highly technical approaches to AI deployment. Previously, when bringing new AI solutions to bear, your team might have had to work closely with the solution provider to gather, refine, and update a training data set. Even if that process could be automated, the training process was time consuming and opaque to you. Some providers used this as an opportunity to run up bills for professional services or take longer to deliver the solution you need. Now, with advanced language models, you may not even need training time before an AI model can be deployed. Any team member with the right permissions can turn on these AI capabilities and have them ready in seconds, delivering value and a return on investment almost immediately. 

Within the generative AI space, a new discipline of “prompt engineering” has emerged. Instead of needing to have developers create complex code to get an AI system returning results you can use, anyone can learn to “ask the right questions.” This means that nearly any member of your team has the power to put AI engines to work for them, for your customers, and your business as a whole. 

So what does this all mean for you? The short version is that it’s easier, faster, and more affordable than ever to use next-gen AI. And any member of your team can engage with these tools without needing to engage technical resources. That adds up to a rapid time to solution and a much quicker time to value as well. With a SaaS model, we may have to reevaluate terms like “return on investment,” since there’s little to no up-front investment required to start using these capabilities. That means you can be more nimble and proactive, and that often leads to happier customers and more satisfied agents. 

And on that note, in our next post we’ll look at how AI can transform agent experiences. 

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