Flag

We stand with Ukraine and our team members from Ukraine. Here are ways you can help

Home ›› Artificial Intelligence ›› No More Random Acts of Bot-Building

No More Random Acts of Bot-Building

by Lance Christmann
4 min read
Share this post on
Tweet
Share
Post
Share
Email
Print

Save

RandomBotting_Slider

The race toward hyper-automation is marked by pitfalls and staggering opportunities

So your organization has a bot problem. Have no fear, this is a common malady in the brackish waters where AI and automation swirl together. As UX practitioners know, these intertwined technologies will figure heavily into the future of every organization on the planet. Chatbots are easy to set up and seem to check both boxes, but even an entire fleet of independently operating chabots can’t be thought of as intelligent automation.

If your bot problem is just that—a group of bots working in their own little towers and providing very limited value to your customers or your workforce—you likely feel like you’ve been chasing windmills. These random acts of botting are consistent pain points for nearly every organization that commits them, but there is a way to bring bots together in harmony. Leveraging many of the processes experience designers are already familiar with, you can create the type of automation that works within the framework of your company, giving customers meaningful experiences and reducing the number of menial tasks team members have to deal with.

To stay competitive, companies need to avoid random acts of technology and adopt a strategy for building an intelligent ecosystem of digital workers. A well-designed ecosystem can interact directly with customers, team members, and with one-another, automating services and systems. Creating one is no small undertaking, but tools exist that can leverage the strengths and knowledge of your entire workforce to achieve what Gartner calls hyper-automation.

What is Hyper-Automation and How Does an Organization Achieve It?

The best way to think of hyper-automation is as the sequencing of disruptive technologies to automate tasks within an interconnected ecosystem of high-functioning bots. The tools an organization uses to develop an intelligent ecosystem of digital workers are novel in their own right, making the process more sophisticated, but also more engaging and manageable. Here are three of the key components you need to get it right:

Conversational AI 

The impact of this technology—not just on automation but on our daily lives—warrants it’s own book. Alexa, Siri, Google Voice, et al are only scratching the surface when it comes to the full potential of conversational AI. A seamless interface that works on human terms, not in computer language. This is one of the disruptive technologies that is sequenced to achieve hyper-automation. Sequencing conversational AI effectively takes it well beyond bots interacting with customers in a limited capacity. To build an intelligent ecosystem, team members converse directly with bots—or intelligent digital workers (IDWs)—teaching them how to perform complex tasks and giving them the context necessary to make independent decisions.

Novel Co-Creation

With the power of conversational AI, team members co-create with IDWs to automate the systems they know best. As they design microservices that sequence into automated services, they are contributing to a shared library that can be modified to perform new tasks while still working within the ecosystem at large. The process is guided by a core-creation team that brings the entire organization into the fold.

Shared Strategy

For these efforts to succeed, there needs to be a constant flow of ideas and direction. An organization’s strategic liaison moves between camps, assisting and evangelizing to designers, stakeholders, and the departments within an organization that are being automated. Realizing that hyper-automation will affect everyone working for the company and that all departments will pull from a shared library of skills, the person in this role spends their days moving between departments, analyzing roles and tasks, and translating those jobs into a framework of automation.

The Right Tools

Creating an ecosystem like the one we’re describing requires a code-free system for building a shared library of microservices that can be endlessly reconfigured and sequenced into useful services. A no-code approach makes it vastly easier for every member of your organization to contribute to the automation of tasks that they understand best. This is what allows hyper-automation to take root within your organization and continue to grow.

How Big is the Payoff?

While the ROI on creating an intelligent ecosystem of digital workers would be hard to overstate the bigger incentive here is that companies implementing hyper-automation successfully are putting themselves in a different league than their nearest competitors. These organizations are creating experiences for customers and internal users that are more than just rewarding, they are transformational.

Hyper-automated companies are not only accomplishing far more with far less effort, it’s also easier for them to further automate new and more sophisticated processes and tasks. So in the short term, the payoff is that your organization gets to remain competitive. The long term-dividends, it would seem, have the potential to compound exponentially.

 

Want to learn more about hyper-automation? Check out the rest of our mini-white paper, No More Random Acts of Bot-Building.

Source:  No More Random Acts of Bot-Building, OneReach.ai

post authorLance Christmann

Lance Christmann

AI researcher, technologist, designer, and innovator. Lance Christmann is the head of experience design at OneReach.ai, where his strong background in interface design and design management, extends the user-centered design approach across all departments. Lance has created  many products and enterprise applications and conversational AI experiences over his career for brands such as  FedEx, Boeing, and the design of the highly acclaimed eBay Desktop application which won an Abode MAX award. Prior to joining OneReach.ai, Lance served as chief experience strategist at EffectiveUI, a full-service user experience agency acquired by WPP/Ogilvy.  

Tweet
Share
Post
Share
Email
Print

Related Articles

Is true consciousness in computers a possibility, or merely a fantasy? The article delves into the philosophical and scientific debates surrounding the nature of consciousness and its potential in AI. Explore why modern neuroscience and AI fall short of creating genuine awareness, the limits of current technology, and the profound philosophical questions that challenge our understanding of mind and machine. Discover why the pursuit of conscious machines might be more about myth than reality.

Article by Peter D'Autry
Why Computers Can’t Be Conscious
  • The article examines why computers, despite advancements, cannot achieve consciousness like humans. It challenges the assumption that mimicking human behavior equates to genuine consciousness.
  • It critiques the reductionist approach of equating neural activity with consciousness and argues that the “hard problem” of consciousness remains unsolved. The piece also discusses the limitations of both neuroscience and AI in addressing this problem.
  • The article disputes the notion that increasing complexity in AI will lead to consciousness, highlighting that understanding and experience cannot be solely derived from computational processes.
  • It emphasizes the importance of physical interaction and the lived experience in consciousness, arguing that AI lacks the embodied context necessary for genuine understanding and consciousness.
Share:Why Computers Can’t Be Conscious
18 min read

AI is transforming financial inclusion for rural entrepreneurs by analyzing alternative data and automating community lending. Learn how these advancements open new doors for the unbanked and empower local businesses.

Article by Thasya Ingriany
AI for the Unbanked: How Technology Can Empower Rural Entrepreneurs
  • The article explores how AI can enhance financial systems for the unbanked by using alternative data to create accessible, user-friendly credit profiles for rural entrepreneurs.
  • It analyzes how AI can automate group lending practices, improve financial inclusion, and support rural entrepreneurs by strengthening community-driven financial networks like “gotong royong”.
Share:AI for the Unbanked: How Technology Can Empower Rural Entrepreneurs
5 min read

Curious about the future of AI? Discover how OpenAI’s “Strawberry” could transform LLMs with advanced reasoning and planning, tackling current limitations and bringing us closer to AGI. Find out how this breakthrough might redefine AI accuracy and reliability.

Article by Andrew Best
Why OpenAI’s “Strawberry” Is a Game Changer
  • The article explores how OpenAI’s “Strawberry” aims to enhance LLMs with advanced reasoning, overcoming limitations like simple errors and bringing us closer to AGI.
  • It investigates how OpenAI’s “Strawberry” might transform AI with its ability to perform in-depth research and validation, improving the reliability of AI responses.
Share:Why OpenAI’s “Strawberry” Is a Game Changer
3 min read

Tell us about you. Enroll in the course.

    This website uses cookies to ensure you get the best experience on our website. Check our privacy policy and