Products You May Like
Presented by Bold360 by LogMeIn
In a pandemic-struck world, digital maturity is essential — and customers are not only using chatbots far more, they’ve come to expect them. Learn about the emerging data and customer service trends you need to make a priority for 2021 in this VB Live event.
“Everyone has been experiencing the shock as a result of the pandemic, but specifically how it’s affecting digital transformation strategies, both on the customer and the employee front,” says Chad Oda, head of consulting and partner at Chat Mode. “Satya Nadella, chief executive officer of Microsoft, recently said they’ve seen two years of digital transformation in the past two months.”
The growth of chatbot implementation
Transformation initiatives haven’t changed, but they’ve vastly been accelerated during this year, Oda says. He notes increased appetite and adoption of chatbots across sectors. There’s been a lot of consolidation in technology stacks as well, he adds, with more fully featured, full-stack platforms that can handle the life cycle of conversational experiences.
They’ve matured from the hype back in 2016 to become validated use cases that are creating value instantaneously for organizations. Additionally, he points to the increased M&A and higher valuations for startups occurring recently as a good leading indicator to show that we’re seeing consistent value in this sector.
“By the end of 2019, NatWest had seen tremendous growth in its customer-facing chatbots, with over 5 million conversations,” say Srini Janarthanam, conversational AI technology designer at National Westminster Bank. “But in 2020, we exceeded 7 million conversations. Customer adoption of chatbots and the solution to getting a quick and reliable answer from businesses is growing. The adoption is real. Customers are expecting us to provide that kind of valuable service through conversational AI.”
The role of AI in chatbot growth
While the advances in AI and chatbot technology over the past four years have fueled this digital transformation, Oda suggests that it’s more simple, well-defined use cases that are driving value today.
“I don’t necessarily think the technology has shifted drastically,” Oda says. “But we are seeing, at least on the platform front, more end-to-end platforms with the middleware integrations all encompassed into one thing. We’re seeing fewer point solutions and more mature platforms today.”
He points out that while advances in Generative Pre-trained Transformer 3 (GPT-3), an autoregressive language model that uses deep learning to produce human-like text, show great promise, and offer a lot of potential down the road from a research perspective, it’s still going to take time to identify pragmatic, and most importantly, safe and ethical use cases to commercialize.
Janarthanam notes that AI is the last piece of the automation puzzle. Automating manual processes that rely on human intelligence has always been the stumbling block in levelling up chatbot technology to the point where it can be leveraged in more sophisticated, valuable ways.
“That’s what AI allows us to do now, especially in conversational AI,” he says. “We would like to automate conversations that are simple, repetitive, and mundane. We don’t want to have our human agents doing those jobs.”
Conversational AI has stepped into that gap, taking over where a human intelligence processor has previously been necessary to identify what strings of words mean, and most importantly, the context behind them. AI can now do that reliably, he says. With conversational AI in place, 80% of the traffic can be routed away from human agents, which is a big savings for them.
“They want to work on interesting, challenging tasks, and not repetitive, mundane tasks,” he adds. “By taking away those tasks, we’re putting together a complementary team. AI empowering agents and making them into super-agents.”
Oda agrees, referring to it as a “virtuous cycle.”
“What we see right now is both sides — customer experience and employee experience — being energized together in concert,” Oda says. “If you give your employees more time to focus on higher-value tasks, that ends up improving customer experience, and also the morale of internal employees.”
Again, it has come down to the work companies have done over the past few years on identifying the value-driven use cases, he says, which are the less complex, more robust tasks that are significantly easier to automate with AI in place.
Additional advantages of digital transformation
Oda adds that there are a number of other valuable advantages in embracing digital transformation, beyond cost scaling and opportunity cost. Firstly, organizations with digital transformation strategies as we move into 2021 are becoming a new breed that take advantage of building data sets that are specific either to their use cases or their verticals.
“Tesla as a company is a prime example of an organization that’s leveraging data, AI, and automation to create this expansive defensive moat,” he says. “Organizations can take some inspiration from that, perhaps on a smaller scale, to be more competitive.”
The other component to the data piece is that it can help solve the service-market fit that product strategy sometimes struggles to find.
“We often talk about the voice of the customer — how do we best align products or services with these different segments we represent?” he says. “The foundation of conversational AI is the data, which is conversational in nature. It can give us a more validated way of identifying new opportunities, perhaps refactoring ones we already have, because we’re seeing what end users are looking for.”
Janarthanam adds to the list of benefits, the way conversational AI and automating conversations allow companies to put their services in front of the customer 24/7, which hasn’t. been previously possible. End-to-end automation allows companies to be available whenever the customer wants them or needs them, without needing to wait.
Another benefit is the ability to serve customers in a more consistent way. Human agents get fatigued, and can make errors. Automated conversations eliminate those errors.
And finally, it adds the ability to personalize services to the customer. Conversational AI allows you to adopt conversations to a customer’s personality, their goals, their constraints, and their preferences in new ways, adapting to each conversation and each customer.
The challenges ahead
Companies are facing some challenges as they implement chatbots and assistants to help with customer service, however.
Among them is paralysis of analysis, Janarthanam says. Platforms only started appearing around 2016 or 2017. Solutions are appearing on the shelf, and with so many options, it can be a struggle to pick the right one.
The newness of the technology is itself a challenge. That includes the lack of standardization, which means it’s impossible to easily define interoperability between solutions, leading to vendor lock-in, and adding question marks around how to develop the best customer experience with the right stack of solutions.
“Choosing a platform is a tactical solution,” Oda says. “Until we see more standardization within the ecosystem, you want to figure out how you can own your data, make a tactical decision today to drive and enable that use case, and position yourself for the future.”
For more on the intricacies of choosing the right solution, making a seamless transition from traditional customer service to chatbots powered by AI, and why conversation design is equally as important as the underlying technology stack and AI pipeline, access this VB Live event.
- Why foundational self-service improvement is a top priority
- How to make digital more effective for employees and customers
- Why AI is a catalyst for digital maturity
- Srini Janarthanam, Conversational AI Technology Designer, NatWest (National Westminster Bank)
- Chad Oda, Head of Consulting | Partner, Chat Mode
- Stewart Rogers, Moderator, VentureBeat