We’re living in an era when technology moves in exponential leaps and bounds, growing more powerful, more pervasive, and more sophisticated every day. It’s no coincidence that ninety percent of all the data in existence was created over the past two years, but in many ways this wealth of information represents a failure. To say it’s being poorly leveraged is a bit of an understatement, but this is about to change.

We’re nearing a convergence point that has already begun to reshape the experiences customers and employees have with businesses—putting those massive stores of data into action in ways that will fundamentally alter our relationship with technology.

If you’re thinking “AI,” you’re on the right track. One of the key elements of this convergence involves intelligent and evolving automated systems powered by one of humankind’s oldest adaptations: conversation.

Groundbreaking as they’ve been, recent innovations like Alexa and Google Home hardly qualify as conversational AI. Human conversation is broader than the spoken word, as we have many different ways of communicating our thoughts and needs—incorporating gestures, facial expressions, visual aids, and sounds, just as humans regularly do in conversation with one another. As such, conversational AI goes far beyond turning down the volume on a speaker, setting an egg timer, or retrieving information about the weather. It encompasses a full breadth of multi-turn interactions. Because they are part of an interconnected ecosystem, these interactions can leverage those massive stores of data we’re continually creating—unearthing massive opportunities for personalization and precision.

Multi-turn, or multimodal, means that a text conversation on your phone with an invisible machine might include that machine showing you part of a video to illustrate a point. If you’re asking it to analyze a spreadsheet or data, it can draw you a graph on-the-fly to help visualize data points. If the interaction is ongoing and you’re about to start driving, the interface can move to voice command. These multimodal experiences mirror normal parts of conversations between people, and that sophistication enables humans to wield technolocal functions and capabilities using our most natural interface. They are dialogue-driven, but, just like human conversations, they can include all kinds of audio and visual aids, and even haptic cues.

You could never have an experience this seamless and efficient while digging through nested tabs or apps. When this level of natural conversation becomes the primary interface between machines and the humans using them, the machine becomes invisible as the interface disappears. This line of thinking should be familiar to many practitioners in user experience design (UX), customer experience management and business. One of the hallmarks of successful experience design is an interface that gets out of the way. The further the interface recedes into the background during an experience, the more frictionless that experience becomes. This lightens a user’s cognitive load and helps them to get what they need from the technology more effectively (though it also represents a massive amount of orchestration behind-the-scenes).

With conversational AI, interfacing with machines no longer requires us adapting to the way they communicate, which dramatically reduces friction in our experiences with machines and software.

Another element of this convergence involves structuring technology so that it can react and adapt to individual situations. Sequencing disruptive, advanced technologies to work in concert is something Gartner calls hyperautomation. They coined the term in 2019, and by Gartner’s estimation, “technology is now on the cusp of moving beyond augmentation that replaces a human capability and into augmentation that creates superhuman capabilities.”

Hyperautomation has the potential for disruption on a scale similar to the advent of the printing press, the industrial revolution, and the dawn of the computer age itself. What’s different is that the rate of change will most likely be much faster.

Hyperautomation is really an application strategy that goes beyond the development of AI and into how you sequence it with other disruptive technologies to solve complex problems as part of an organization-wide experience strategy. Technology exists right now that can help you move toward hyperautomating business processes, workflows conversations, and tasks to offer better-than-human experiences (BTHx), but most companies haven’t yet tapped into it.

“A lot of people think of this as disruption, like the taxi industry is being disrupted by Uber,” Harvard Business School Professor Marco Iansiti says with regards to hyperautomation. “It’s not disruption … Firms have been designed with management and labor since the Industrial Revolution. This is a fundamental change in the means of production, and it’s affecting every industry across the board.”

It’s common for organizations to focus on disruptive technology in myopic ways—in this case, seeing automation as a means to handle simple tasks on human terms. We’re going to automate a coffee maker so that it brews a fresh pot of coffee at 8:45am. What if, instead, the coffee pot was part of a better-than-human experience that not only adjusts the time it brews coffee and the amount it brews, but also cross references company calendars and brews an extra strong pot of coffee in anticipation of a client coming straight to the office off of an international flight. Better yet, imagine a nimble financial institution streamlining operations and eliminating overhead by hyperautomating tasks like approving loans, conducting credit score checks, and dispensing financial advice. This is how China’s Ant Financial Group operates. They are a hyperautomated powerhouse whose mobile payment service boasts more than 450 million active users (Apple Pay sits at about 12 million).

When we think about AI we often think about the notion of singularity—the hypothetical point in time when a powerful superintelligence will surge past all human intelligence. There’s also the notion of machines gaining General Intelligence and thus the ability to learn any intellectual task that humans can. These versions of superintelligence won’t be the product of some super algorhythm.

The perception of singularity or General Intelligence is more likely to emerge from an ecosystem of algorithms and technologies sequenced in intelligent ways to work in concert, and likely made up of contributions from different pieces of software engineered all over the world.

While the arrival of singularity could be many decades away, we’ve already quietly passed a significant milestone. Users are now having experiences with conversational AI that are far more rewarding than what their human counterparts offer (BTHx). For evidence, we’ll turn again to Ant Financial, who told MIT Technology Review that customer satisfaction with their chatbots had surpassed human performance. “There are many, many chatbot companies in Silicon Valley,” Yuan Qi, Ant’s Vice President & AI Chief Scientist, noted at the time. “We are the only one that can say, confidently, [our chatbots perform] better than human beings.”

There’s also the example of Lemonade, a tech-minded startup that has disrupted the rental insurance market with low prices, a program for donating premium surplus money to charity, and a Better-Than-Human customer-facing conversational AI.

“The biggest thing that pushed me to convert to Lemonade was the ut- terly charming AI chatbot,” Juliette van Winden writes in a Medium post dedicated to their chatbot, Maya. “24/7, 365, day or night, Maya is there to answer any questions to guide the user through the sign-up process. Unlike the drag of signing up with other providers, it took me a total of two minutes to walk through all the steps with Maya … What intrigued me the most, is that it didn’t feel like I was chatting with a bot. Maya is funny and charismatic—which made the exchange feel authentic.”

Imagine this kind of reality playing out in another context: your router goes down. You place a call to your service provider and are guided through all the necessary system checks quickly and elegantly by a conversational app. Better yet, their hyperautomated ecosystem detects that your router is down and their conversational app reaches out to you before you even notice that you’re offline. The IDW is programmed to isolate the issue by running background tasks while it speaks with you to verify your account and location. This intelligent digital worker can have you message a photo of the blinking lights on your router while simultaneously looking at your connection status internally. It assesses and course corrects in seconds and your router is back up and running inside of five minutes. Best part? You didn’t have to wait on hold for a human operator, because there are unlimited digital agents at the ready, and they called you, perhaps even detecting the issue before you did. After experiencing this kind of BTHx firsthand, you’ll never want to go back to the old way of troubleshooting.

These are the kinds of experiences that will arise out of the convergence we’ve been talking about. As conversational AI is sequenced with other technologies to contextualize massive amounts of data within an ecosystem that can give customers and employees access to elevated problem solving capabilities, the world as we know it will change fundamentally.

What’s important to remember throughout is that the potential created by hyperautomation is so vast that the marketplace advantage can be staggering for companies with these ecosystems already in place. Making the most of this hyper-disruptive moment in history (and not being left behind) requires a holistic undertaking that touches on all aspects of your business. Random acts of technology—like deploying disparate chatbots that exist in isolation—will underwhelm your workforce and customers, leading to low adoption rates. A fully integrated approach, however, can bring about a totally new paradigm of productivity with unprecedented potential.