This as-told-to essay is based on an interview with Tyler Ashby, president at Agents Only Technologies.
Before joining Google, I spent 20 years in the customer service and business process outsourcing field. Specifically, I launched and ran customer contact centers around the globe. From the start of my career, I was always drawn to the people side of the business. This drove me to take a particular interest in people-first operating principles with an emphasis on creating technology and processes that make the humans involved more valuable.
My time with Bain trained me to tackle customer experience and costs using customer journey mapping, service blueprints and people/process/technology analytics. As a result, I developed a strong passion for identifying and improving efficiency without compromising the end experience. I’ve launched projects across the world for notable companies such as Sprint, Dell, Epson, Citibank, Samsung, Telstra and Virgin.
I joined Google somewhat circuitously. I originally interviewed with Fitbit, but during the hiring process, Google announced its plans to acquire the company. At that point, I had several job options, but I chose Fitbit, primarily because I would have the chance to work at Google down the road.
Initially, the experience at Google was exciting; the opportunities seemed endless. I underwent onboarding from an engineering group, Noogler Training, and had unlimited access to self-paced training and experts from various skills. It seemed like everyone at the company had a “go try it” attitude and there were no restrictions on creativity.
However, I soon realized that Google’s innovation, particularly regarding generative AI, was limited.
“The customer service department had limited to no access to even basic data tools — let alone any of the leading tech being developed.”
When Rick Osterloh, senior vice president of devices and services, presented his roadmap, the priorities and core action plans laid out in it did not align with those of a tech innovator. Although our engineering groups were enabled and encouraged to experiment and developed brand-new applications for our technology through side projects, we in the customer service department had limited to no access to even basic data tools — let alone any of the leading tech being developed within Google.
The customer service teams primarily used outdated third-party tools. Even with the perfect execution of the 18-month roadmap, we would still be technologically behind the leading industry standard. Google’s roadmap lacked any plans for generative AI in customer service.
In 2018, Sundar Pichai, CEO of Alphabet Inc. and its subsidiary Google, showcased Google Duplex’s AI capabilities at I/O, where it successfully called a hair salon and booked an appointment using AI voice and transcription. But in 2020, when I asked about it, no one in customer service had used it or brainstormed applications for Google customer service. In 2021, I gained access to Meena, Google’s AI chatbot, but I was the sole user within my team.
I discussed the matter with engineering mentors and learned about Google’s unwritten stance: Google doesn’t invest in customer experience through service; the company believes in making the product better. I was given several examples from YouTube and Stadia that indicated the limited influence of the services organization within Google. Because of this, I believed Google had no intention to innovate the customer experience.
Neither Pichai nor Osterloh explicitly stated that Meena, a generative AI, wouldn’t be part of customer service. Although Pichai has a positive vision for AI’s potential, there were no explicit plans. Most importantly, our roadmap didn’t pave the way for generative AI integration, which would require significant work on processes, tools, data management and tech stack integration. The focus was on cost reduction — not preparing for implementing this groundbreaking tech. Despite raising questions about this through Dory, Google’s internal Q&A, and other channels, they remained unaddressed.
What’s more, I spoke to four different leaders within my organization — a Fitbit director, direct manager, director of scaled operations and CS tools/transformation director — about the opportunities with Meena and how we could go about working between the engineering org and the customer service org.
They confirmed what I already knew: Google wasn’t innovative enough for me.
“Google didn’t excite me or give me a sense that I was truly making a difference.”
I decided to leave Google when presented with an opportunity at Agents Only, a gigCX platform addressing contact center issues by employing technology to link brands with seasoned gig agents, forming an instant virtual contact center. Centered around the agents, the company aims to harness top-notch talent to create the best customer outcomes.
My choice between Google and Agents Only was a trade-off between long-term stability and personal satisfaction. Ultimately, I realized that my work at Google didn’t excite me or give me a sense that I was truly making a difference. When I resigned, I sent two of those colleagues I’d spoken with an email referencing the Meena predicament; I told them Agents Only was offering me the chance to use tech to make a difference.
Without the Agents Only opportunity, I would have stayed at Google due to the stability and benefits that were valuable to my family. It was only the appeal of joining a startup that gave me a chance to make a real difference that could have pulled me away.
Additionally, Agents Only’s people-centric philosophy, technological vision and innovation potential were major selling points. I also knew and trusted the founder, Ben Block: He had put his money where his mouth was many times in the past when it came to taking care of agents, so I believed him when he said the company was founded to make agents’ lives better.
I signed on — and it was the right decision.
Agents Only is at the forefront of disrupting the contact center industry and raising the quality of life of the agents.
There is such a huge pool of dedicated talent that cares about doing a good job for themselves, the customer and the client. I want to enable these agents to make a larger share of the money by paying them more and use our technology to remove layers of ineffective command and control management. We are currently operational in four countries, and I’m looking forward to adding agents in every country of the world and creating an OnDemand GigCX solution that will become a part of every company’s customer strategy.
“Internally, we are using generative AI to coach the agents on behaviors that lead to better outcomes.”
To date, we’ve handled over 12 million customer contacts, using 100 million data points to rate agents, and processed $250 million in revenue for our restaurant and hospitality clients. This was done at a cost 40% lower than normal operations while being able to pay the agents almost twice as much as the industry standard.
We’ve also delivered incredible flexibility in terms of staffing — going from 700 agents to over 2,000 agents for a single day to handle restaurant calls during the Superbowl.
Reliability is a key component too. When we allowed 100% of agents to choose their schedule, they successfully delivered on 98% of those hours, surpassing the industry average of 82%. We have achieved an impressively low attrition rate of less than 1% as our agents tend to remain on our platform once they join.
As for generative AI, the access it gives to knowledge and self-learning capabilities is highly valuable. Internally, we are using generative AI to coach the agents on behaviors that lead to better outcomes. They are incentivized for these behaviors and can have in-depth coaching conversations with the AI that allows them to ask clarifying questions, get expert examples or receive objective feedback on their skills.
The most exciting part of generative AI will come when used in tandem with other types of AI. Combining machine learning AI or analytic AI with generative AI will allow human interaction and development that is real-time and takes into account every piece of data available to define and optimize, as well as task automation and real-time augmentation. The result could be a learning and development loop allowing anyone to learn anything — while also putting that knowledge into practical execution via automation.