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I don’t want to scare you, but the status quo is a death sentence. Most modern organizations are run using systems and strategies that will seem almost comically outdated a few short years from now. That’s because the strategic orchestration of technologies described in this book—conversational AI, hyperautomation, and intelligent automation—will do a lot more than disrupt the ways we’re accustomed to dealing with technology; that strategic orchestration will obliterate existing models.
We’re living in an era when technology moves in exponential leaps and bounds, every day growing more powerful, more pervasive, and more sophisticated. It’s no coincidence that ninety percent of all the data in existence was created over the past two years.
The technologies surrounding conversational AI are heading toward a point of convergence that will fundamentally alter our relationship with machines. Already, the experiences customers and employees have with businesses are being reshaped by the hallmarks of this convergence—putting those massive stores of data into action in ways that have upended entire industries. This might sound hyperbolic—especially given the substandard chatbot experiences endemic to much of the automation happening in the world at present—but it’s not.
The reason that conversational AI in particular feels like a lot of talk is simply because it hasn’t been more widely adopted—yet. The technology surrounding conversational AI has become highly sophisticated, but that hasn’t changed the fact that people don’t like pogo-sticking between ten different machines. For example, let’s say you log on to your home security system website to cancel service. Asking their chatbot a question drops you down a funnel of FAQ menus where you learn, five minutes in, that cancelations can’t be handled online. When you next call the accounts department, you’re confronted by a series of voice automations that feels like another funnel drop, so you start stamping “0” hoping for a shortcut to a live agent. Crap experiences like this can feel less productive than just waiting on hold and hoping you can remember the security PIN you created five years ago.
The “I don’t have time—I’ll do it the long way” mindset is symptomatic of the lackluster conversational experiences users are accustomed to having with machines. But that’s starting to change. One of the key elements of this convergence of technologies surrounding conversational AI involves intelligent and evolving ecosystems designed for accelerated automation powered by one of humankind’s oldest adaptations: conversation. Make no mistake, it’s not that conversational AI isn’t going anywhere—it’s going everywhere.
Groundbreaking as they’ve been, recent innovations like Alexa and Google Home hardly qualify as conversational AI. Asking smart speakers to issue weather reports, set a timer, or play a song are very limited and immature applications of conversational AI, though they hint at its nascent power. Smart speakers have completely upended the speaker industry, to the point where it might be difficult to find a new speaker for sale that doesn’t have built-in conversational capabilities. But how powerful does a smart speaker become when it’s not limited to the things that Siri or Alexa can do? What happens when you can ask your speaker to play “Mr. Roboto” by Styx and then follow up with another request: “I want to buy a copy of the book that Marc Maron mentioned during the intro to his podcast today. I don’t remember the title but see if there’s a copy available from Powell’s before looking on Amazon.”
What will happen is, a few minutes later, a text message could appear on your phone with a link showing a hardcover copy of Camera Man by Dana Stevens available on Powells.com. By replying, “Yes. Please buy” via text, you’d be communicating with the same interface that you initially spoke to—an umbrella conversational interface that has become your primary interaction point with most of the technology in your life. Once this scenario is possible, you won’t think of technology in terms of different apps, because you’ll rarely need to open and interact with an app. Domo arigato, Mr. Roboto.
Of course, human conversation is broader than the spoken word, as we have many ways of communicating our thoughts and needs. Humans frequently incorporate gestures, facial expressions, visual aids, and sounds in conversation. As such, conversational AI encompasses a full breadth of what I call “multi-turn” or “multimodal” interactions. Because they are part of an interconnected ecosystem, these multimodal interactions can leverage those massive stores of data we’re continually creating—unearthing massive opportunities for personalization and precision.
To explain what I mean by “multi-turn” or “multimodal”: having a text conversation 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 technological functions and capabilities using our most natural interface. These micro UIs, as you might call them, are dialogue-driven and, 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—and many of the world’s leading companies are coming around to this fact. Salesforce didn’t just acquire Slack. Their CEO has openly admitted that they are rebuilding their entire organization around Slack. Microsoft is doing similarly with Teams: they’re betting that an integrated communication platform and a unifying conversational interface—one machine that connects to everything—will benefit custom- ers, employees, and organizations in big ways.
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 most experience design practitioners. 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 that we adapt to the way they communicate, which dramatically reduces friction in our experiences with machines and software. Conversational AI will go anywhere and everywhere—meaning that invisible machines will be, for lack of a less grandiose term, omnipresent. You’ll be able to turn to your phone, any nearby smart speaker, or any voice-enable appliance and enlist the help of an invisible machine. This ties into another element of this convergence, which involves sequencing technology so that it can react and adapt
to individual situations. Invisible machines galore, connected to ecosystems built for optimized problem solving.
Sequencing disruptive, advanced technologies to work in concert is something Gartner calls “hyperautomation,” and it’s as intense as it sounds. Gartner coined the term in 2019; by their estimation, “technology is now on the cusp of moving beyond augmentation that replaces a human capability and into augmentation that creates superhuman capabilities.”
I can’t help but reiterate that these changes to business are already underway and they’re going to accelerate in astonishing ways. The “hyper” is there for a reason, and the sequencing of disruptive technologies inside ecosystems built for hyperautomation will unleash hyperdisruptions across all industries, emerging suddenly and in ways that we won’t always be able to predict. But while the hyper aspects of these disruptions are new, the sequencing of technology to solve complex problems has been with us for centuries.
It’s long been in our nature to innovate by orchestrating technologies in our favor (the printing press is a great example—combining presses used to extract olive oil with ink, paper, and moveable type). Hyperautomation represents a new era wherein anyone with an idea can have a voice. With that voice, they can orchestrate disruptive technologies to accomplish things others haven’t even dreamed of.
Assembling a diverse team and fostering a culture that champions change are keys to success in a landscape that will be continually disrupted. Embracing change lets you use speed and iteration to offset any fear of failure. Sometimes the best way to learn and make progress is to just start—adopting a practice of failing forward fast. Big changes on top of big changes are on the way, so alarm, urgency, and action are all merited. For those just getting started or who are already up to their necks, rest assured, there are practical ways to achieve hyperautomation.
The above is an excerpt from Chapter 1 of Age of Invisible Machines: A Practical Guide to Creating a Hyperautomated Ecosystem of Intelligent Digital Workers (Wiley).
Written by Robb Wilson.
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