By Jim Palmer
The idea of an Ai Flywheel first took shape in the Spring of 2014 on a whiteboard in a small conference room on the 2nd floor of a nondescript office building in San Francisco. Little did we know then how the momentum would continue to build in 2018 in another conference room at the Dialpad HQ named Apollo. The name felt appropriate. We weren’t trying to be the first humans to land on the Moon, but in 2018 the dream of building real Ai value on business conversations became far more real. Fast forward to last week, when Dialpad asked me to write this intro to our new ebook about the future of Generative Ai, I found myself thinking back on that moment over ten years ago.
I was there with our TalkIQ team. I had co-founded the company with the goal of using state-of-the-art machine learning to make voice conversations easily accessible and understandable to business and customers at scale. Every day businesses generate oceans of customer call data that just sits there forever, unused. Call center managers can listen in on a certain number of calls in order to help agents and find issues. But no single manager can listen to, let alone interpret, all the calls in a high volume call center. Finally, an opportunity to better understand communication at scale.
For many years, I’ve believed Ai has endless potential to help tackle this problem. I can trace my interest back to 1991, when Creative Labs released the speech synthesis program Dr. Sbaitso (SBAITSO = Sound Blaster Acting Intelligent Speech-to-Text Operator). Even with all its limitations, this early example of natural language understanding gave me a glimpse of the future.
I can trace that glimpse directly to what we were building at TalkIQ after 2014. Those years turned out to be an exciting time to be an Ai-fueled startup. Advancements in transcription (Automated Speech Recognition) accuracy in particular made the moment ripe for applying existing state-of-the-art natural language processing (NLP). We were able to do basic call descriptions, extract key moments, generate insights, and much more. We were talking to lots of partners.
Then we demoed for Dialpad that day in Apollo and everything changed. Dialpad’s communications platform made it easier to speak with customers from anywhere. We were using Ai to make it easier to understand what they were saying. It was perfect symbiosis. In two weeks TalkIQ was integrated in Dialpad’s production environment; a few weeks after that, we became one team.
Google had recently introduced the transformer model (with their landmark paper, Attention Is All You Need), which highlighted the potential of Generative AI. We built a prototype, Whole Call Summary, that could take a call recording and produce a one-paragraph summary. It was exciting – Look, we can do this kind of thing! – but it wasn’t real time, the analytics were limited. We had, you might say, half a flywheel.
Four years later ChatGPT showed the world GenAiI’s potential, and reality caught up to our daydream. With GenAi we can finally close the loop – proprietary models generating insights to improve customer service and deliver more organizational value, which lets you grow your business, feed more data to your models, adapt, build more features, earn new customers. Rinse and repeat. Ai today is a true flywheel – for developers, sure, but also for our customers, and their customers, and beyond.
Because the Ai flywheel described in this report isn’t one process but a template for thousands of them, or millions. Forget “one model to rule them all;” what we need in order to realize the astounding potential of GenAi tomorrow are tools that let anyone build the right platform that best suits their company, their data, their goals. There will be Ai flywheels for specific use cases, industries, organizations, languages and more. The goal for solution providers has to be to make this transition to the Ai future accessible, controllable, and above all, fundamentally human.
We keep hearing from a certain sector of the Ai community that human beings are in the way, slowing the tech industry down from producing the wonders of full automation. I don’t buy it. There’s no doubt that Ai will continue to disrupt the market today; there are lots of simple, repetitive tasks that we humans will happily automate. What’s harder to imagine now are the new, better jobs that Ai will create as new industries become possible. The Model T put lots of horse-and-wagon employees out of work, but far more people found employment in the automobile industry. Electronic calculators might have replaced human calculators, but were a force multiplier. The rise of artificial intelligence is simply the next chapter in a long story since the first tool and accelerating at an amazing pace since the Industrial Revolution.
That story is called optimization. We create prosperity when we find ways to do things a little bit better, whether it’s writing less code to perform the same function or a little bit more code to make the process faster or cheaper. The challenge for each new generation is, do we truly understand how our processes are supposed to work? What users actually enjoy? How to build those experiences for them and optimize them safely? Do we even yet have the right tools for all those jobs?
No matter how we wind up answering those questions, the Ai flywheel will continue to gain momentum, helping us build more meaningful feedback loops by polishing our existing tools and finding new and better ones. It’s a privilege to be able to contribute to this exciting chapter in an ongoing story.
Ready to harness the power of the Ai Flywheel for your business?
Download our ebook, The Agentic Flywheel, and unlock insights to revolutionize your business communications.
