Maybe you’ve seen the meme knocking around the internet: a photo of British octogenarian David Latimer who bottled a handful of seeds in a glass carboy in 1960 and left it largely untouched for almost 50 years (uncorking it only once, in 1972, to add a little water). His 10-gallon garden created its own miniature ecosystem, and has thrived for more than half a century. [1]


In the realm of technology, closed platforms are like Latimer’s terrarium: they can be highly functional, beautiful, and awe inspiring, but they can only grow as big as their bottles. The current business landscape, however—with businesses attempting to sequence as many innovative technologies as possible, as quickly as possible, to automate business processes, workflows, tasks, and communications—requires an architecture that breaks out well beyond these glass walls.

For something like the original iPhone, a terrarium was just fine. Everything a user needed to enjoy its functionalities was baked right into the original version of iOS. Keeping the system closed ensured the quality of the apps and created a seamless overall experience, which contributed to its success, despite the fact that it didn’t have nearly as much functionality as other mobile devices at the time.

Apple was able to make updates to their mobile ecosystem with new versions of iOS—which usually coincided with a new product drop—but the three-month gap between the launch of the original iOS and it’s first update is an eternity as businesses enter into the inevitable and ultra-complex realm of conversational AI, general intelligence, and hyperautomation.

By Gartner’s definition, hyperautomation involves “the orchestrated use of multiple technologies, tools, or platforms [including] AI, machine learning, event-driven software architecture, robotic process automation (RPA), BPM/iBPMS, integration platform as a service (iPaaS), low code/no code tools, packaged software, and other types of decision, process, and task automation tools.” [2]

That stew of acronyms and jargon has likely made many department heads spin, but Gartner also calls hyperautomation an “unavoidable market state in which organizations must rapidly identify and automate all possible business processes.”

In the rush to implement heavily buzzed-about technology, conversational AI is often the first stumbling block for organizations as they attempt to automate specific aspects of their operations. They typically end up with a smattering of chatbots that operate on their own closed systems, create subpar user experiences, and are unable to become part of an orchestrated effort—essentially a collection of terrariums. This common misstep likely has something to do with a misconception of how advanced technologies work together in an optimized environment.

“Conversational AI can be deceiving because it’s really general intelligence that people are talking about,” says Robb Wilson, founder of, a leading no-code/low-code hyperautomation platform for rapidly developing conversational AI applications. “Then you dig into that and it’s not actual general intelligence, it’s the perception of general intelligence, and that’s something that’s likely going to be experienced through conversation.”



In an ecosystem built for hyperautomation, conversation is actually the tissue that connects all the individual nodes at play. According to Wilson, a state of hyperautomation within an organization is achieved when a collection of advanced technologies are sequenced in intelligent ways to create intelligent automations of business processes. This is something Wilson has been pursuing for well over a decade, with his team logging over 2,000,000 hours of testing on over 10,000 conversational AI applications. (His in-depth strategy for creating ecosystems for hyperautomantion is detailed in a soon-to-be-released digital book, Age of Invisible Machines.)

In the ecosystems he describes, machines are communicating with other machines, but there are also conversations between humans and machines. Inside truly optimized ecosystems, humans are actually programming machines via conversation. This means workers are training their digital counterparts to complete new tasks through conversational interfaces—they’re telling them how to contextualize and solve problems without having to rely on any coding languages.

By extension of the leafy green analogy, a business in a state of hyperautomation is quite the opposite of a terrarium, it’s like a forest—a vast ecosystem of interconnected elements working together in harmony. Users, whether they’re customers or employees, get to experience its splendor in somewhat simple terms, through conversation-based interactions, unaware of the network of infrastructure underfoot that supports it and unconcerned with where the interconnected elements are coming from. 

“These innovations, algorithms, and systems that get sewn together suggest that you have a much better chance of achieving general intelligence, or perceived general intelligence, by having access to everybody’s tools,” Wilson says. “It won’t happen inside some closed system where the tools have to be supplied exclusively by Google or IBM.”

For a tangible example of this, Wilson points to the idea of a car manufacturer. In some ways, it would be easier to manage the supply chain if everything came from one supplier or the manufacturer supplied its own parts, but production would suffer. Ford—a pioneer of assembly-line efficiency—relies on a supply chain with over 1,400 Tier 1 suppliers separated by up to 10 tiers between supply and raw materials, providing significant opportunities to identify and reduce costs and protect against economic shifts [3]. This represents a viable philosophy where hyperautomation is concerned as well. Naturally, it comes with a far more complex set of variables, but relying on one tool or vendor stifles nearly every aspect of the process: innovation, design, user experience—it all  suffers.

“Most of the high-profile successes of AI so far have been in relatively closed sorts of domains,” Dr. Ben Goertzel says in his TEDxBerkley talk, “Decentralized AI,” pointing to game playing as an example. He describes AI programs playing chess better than any human, but reminds us that these applications still, “choke a bit when you give them the full chaotic splendor of the everyday world that we live in.” [4]


Goeztel has been working in this frontier for years through the OpenCog Foundation, the Artificial General Intelligence Society, and SingularityNET, a decentralized AI platform which lets multiple AI agents cooperate to solve problems in a participatory way without any central controller.

In that same TEDx talk, Goertzel references ideas from Marvin Minsky’s book, The Society of Mind, “... it may not be one algorithim written by one programmer or one company that gives the breakthrough to general intelligence … it may be a network of different AIs each doing different things, specializing in certain kinds of problems.”

The kinds of networks Wilson, Goertzel, and Minsky describe are made up of a vast number of data points, AIs, and devices that can give rise to cooperative intelligence. Hyperautomation within an organization is much the same: a whole network of elements working together in an evolutionary fashion. As the architects of the ecosystem are able to iterate rapidly, trying out new configurations, the fittest tools, AIs, and algorithms survive. From a business standpoint, these open systems provide the means to understand, analyze, and manage the relationships between all of the moving parts inside your burgeoning ecosystem, which is the only way to craft an proper strategy for hyperautomation.

So if open systems are so powerful, why aren’t more organizations using them to achieve states of hyperautomation?

“Organizations aren’t using open systems because they don’t know that they exist,” Jordan Ratner, former Conversational AI Lead with Deloitte, says. “Coming into the chatbot space, they’re relying on multiple closed systems because each group within an organization is adopting a different type of a build on a different closed platform.”

According to Ratner, this creates costly problems with organizations switching from one closed system to another as they encounter problems that can’t be solved. As they move from system to system, they might solve one small piece of their puzzle, but soon discover they have another hole to fill. And even with simple things like text-to-speech and speech-to-text, it might cost millions of dollars to rip out old systems and implement new ones. But that’s not the only problem. 

“With closed systems, you don’t have the ability to create your own custom functionalities—you can only use what the vendor gives you,” Ratner explains. “If you want new functionality, you have to ask the vendor to add features. In that way, the vendor controls your roadmap. This is problematic because they’re not the ones getting their hands dirty—they don’t even understand what kinds of problems you're trying to solve.”

In this scenario, organizations are forced to wait on vendors to bake new features into future versions of the system. Similar to the iPhone OS example from earlier, this means organizations are left waiting for the next system update … in three months. That timeline is woefully inadequate where hyperautomation is concerned.

“Success here requires real iteration, you can’t pretend to be agile,” Wilson cautions. “Organizations with the shortest iteration cycles who are iterating the most will win.”

Wilson frequently points to the maxim that “variety solves complexity.” What this means in terms of hyperautomation is that with something so complex and vast—simulating human beings and automating tasks—you need to be able to try as many solutions as possible. This is why waiting months for vendors to make updates to closed systems is an impossible solution. An organization making serious attempts at hyperautomation should be making updates on a daily, or even hourly, basis.

“To move closer to hyperautomation, you need to shift the ability to create functionalities and customize them over to the problem-solvers themselves,” Ratner concurs, “and the way to do that is with an open system.”

“Creating an architecture for hyperautomation is a matter of creating an infrastructure—not so much the individual elements that exist within an infrastructure,” Wilson says. “It’s the roads, electricity, and waterways that you put in place to support houses and buildings and communities. That’s the problem a lot of organizations have with these efforts. They’re failing to see how vast it is. Simulating human beings and automating tasks is not the same as buying, say, an email marketing tool.”


Wilson notes that the beauty of an open platform is that you don’t have to get it right. It might be frightening in some regards to step outside a neatly bottled ecosystem, but the breadth and complexity of AI are also where it’s problem-solving powers reside. Following practical wisdom applied to emergent technologies—wait until a clear path forward emerges before buying in—won’t work because one one organization achieves a state of hyperautomation, their competitors won’t be able to catch them. 

“Organizations come to us trying to evaluate what they should use, but that’s not really something we can answer,” Wilson says. “By choosing one flavor or system for all of your conversational AI needs, you’re limiting yourself at a time when you need as many tools as you can get. The only way to know what tools to use is to try them all, and with a truly open system, you have the power to do that.”