In order to hyperautomate within your organization, you might need a strategy for co-creation that would involves everyone in your workforce. You also might need a platform to build your ecosystem that allows anyone to design a conversational experience without having to write code.

Once you get started, be prepared to fail fast and often as you continuously evolve your solutions and their ecosystem.

The answer is, you have to move fast, you have to take risks, you have to fail, and you have to keep moving forward. In terms of organizational readiness, hyperautomating may seem especially daunting because it typically requires a deep understanding of AI, machine learning, and robotic process automation (RPA) and other advanced technologies. But there’s an easier way to hit the ground running through advanced conversational technologies.

No-code rapid development tools and conversational technologies are literally changing how companies build software, who can build it, what they can build, and how fast they can do it. This dramatically lowers barriers to sequencing advanced technologies, helping companies accelerate hyperautomation.

No-code and conversational technologies allow anyone within an organization to be able to utilize or contribute to the creation or evolution of advanced software solutions, irrespective of whatever technical skills or domain expertise they have.

Conversational technology creates bridges that allow people and machines to communicate and collaborate through human language. During the creation of an automated ecosystem, the possibilities for rewarding results grow exponentially when humans and machines can communicate and collaborate through conversation.

What this means in practical terms is that when humans can converse with machines verbally or through text, things get accomplished faster and anyone in the organization can weigh in. As a result, productivity accelerates.

Combine this with today’s advanced code-free technologies for building automated ecosystems through visual, drag and drop programming, and you’re not only able to equip everyone in the organization with access to advanced technology solutions, they can actually take part in designing them. This accelerates your organization’s ability to hyperautomate.

Conversational technology keeps humans in-the-loop throughout the evolution of your ecosystem, to cover and close the gaps on automated tasks and processes. In this scenario, humans are readily available to assist bots when they run into problems. There’s no need to strive for autonomy right out of the gates. With conversational AI and a code-free building system doing the heavy lifting, automation grows quickly and organically, with humans and machines working together to deepen and expand its reach.

This Journey Has Been Personal

Sequencing technology requires a point of view. Technology that allows anyone to create advanced conversational applications has to stay focused on being truly useful. Questions like, Who is it built for? What problems will users need to solve? How might they employ our solution? are central to your journey. The answers to these questions and others should be apparent to anyone involved in the project or who’s using the technology and tools.

These are the questions that resonate at the core of OneReach.ai. Our first conversational AI platform, Communication Studio G1, was our best guess at how to answer these questions. We knew better than to build strictly around technology. We built around user needs.

Through thousands of use cases and tens of thousands of user stories we learned a lot about what we could be doing better—often learning the hard way, even with examples that seem obvious in hindsight (more syllables help with speech recognition; design to have as few interactions as possible; store context for future conversations …).

Due to the inherently complex nature of the tasks, the lack of maturity in the tools, and the difficulty in finding truly experienced people to build and run them, creating better-than-human experiences is extremely difficult to do, or, as Gartner calls it, “insanely hard.” Over the years we’ve watched many successful and failed implementations (including some of our own). Automating chatbots on websites, phone, SMS, WhatsApp, Slack, Alexa, Google Home, and other platforms, we formulated our point of view on how to build and manage primitive conversational applications. Patterns began to emerge from successful projects. We began studying those success stories to see how they compared to others.

The data and best practices we share on Journal have been gathered over the course of 2,000,000+ hours of testing with over 30,000,000 people participating in workflows across 10,000+ conversational applications. We are also drawing from over 500,000 hours of development—all of it part and parcel to the evolution of our code-free conversational AI platform, GSX. Of course, it’s important to remember that having a platform to build such experiences does not guarantee success. You also need processes, people, tools, architecture and design that work in a coordinated way.

We’ve formulated an intimate understanding of what it takes to build and manage intelligent networks of applications, and, more importantly, how to manage an ecosystem of applications that enables any organization to hyperautomate.

As companies wake up to the fact that they’re already in the race toward hyperautomation, a sound strategy for building an intelligent ecosystem is what will lead them to the finish line. Just like a website needs content strategy to avoid becoming a collection of disorganized pages, successful hyperautomation requires a sound strategy for building an intelligent ecosystem and the willingness to quickly embrace new technology.

While many of today’s advanced technologies are disruptive, conversational interfaces, AI, code-free design, RPA, and machine learning are force-multipliers that can make companies that use them correctly impossible to compete with. The scope and implications of these converging technologies can easily induce future shock—the psychological state experienced by individuals or society at-large when perceiving too much change in too short a period of time. Organizations currently employing bots, conversational applications, or AI-powered digital workers in an ecosystem that isn’t high-functioning are likely experiencing some form of this.

Our hope is that sharing the best practices and insights we’ve gleaned can make the crucial difference for enterprise companies struggling to balance the problems that come with those random acts of bot-building. A strategy that can put converging technologies to work in intelligent ways can propel your organization into a bold new future.