Gartner has defined “Hyperautomation” as the #1 Strategic Technology Trend for 2020
The Encyclopedia Britannica defines Automation as the application of machines to tasks once performed by human beings or, increasingly, to tasks that would otherwise be impossible.
Hyperautomation deals with the application of advanced technologies, including artificial intelligence (AI), machine learning (ML), process mining, decision management, natural language processing (NLP) and others to increasingly automate processes and augment humans.
Hyperautomation spans across a combination of tools and technologies, including Robotic Process Automation (RPA), Intelligent Business Management Software (iBPMS) and AI, with a goal of increasingly AI-driven decision.
Intelligent Process Automation is traditional Robotic Process Automation (RPA) rebranded with a flavor of AI and ML – it is essentially a layer of AI automations with routine task level automations. Hyperautomation is much broader than routine and repetitive tasks. It is not only about how you automate your tasks but also about how to orchestrate the end to end process chain to unlock benefits. It helps your organization gain more intelligence about the individual elements of your process by means of process discovery, analytics, process mining etc. It also focuses on how you perform integrations because automations cannot happen in siloes without considering the integrations involved such as APIs, connectors and others. It is about how you can effectively orchestrate all these components together where you could have an end to end intelligent, event driven automation. So, hyperautomation is broader in scale and broader in scope.
Also, agility is of primary importance. You cannot wait for years in planning and scoping but need start small and be nimble to pivot and adapt.
Hyperautomation is not just one technology but a combination of several technologies and tools. It is not just RPA and AI but a broader range of adjacent technologies that come together.
“It is the orchestration of these technologies in an effective manner to drive efficiency, efficacy, and business agility and that is what takes you through digital transformation.” – Saikat Ray, Sr. Director Analyst, Gartner Research
The Gartner Study also points out how hyperautomation often results in the creation of a digital twinof the organization (DTO), allowing organizations to visualize how functions, processes and KPIs interact to drive value. The DTO then becomes an integral part of the hyper automation process, providing real-time, continuous intelligence about the organization and driving significant business opportunities. What this means is that it is not about “Simple RPA” anymore but you have to think of highly sophisticated, AI-based process automation to the level that organizations are building “digital twins” allowing them to complete processes faster, with fewer errors and more efficiently.
Hyperautomation defines the future of work where humans and robots work side by side solving problems, prioritizing tasks, enhancing creativity and increasing productivity. It is no longer about automating simple jobs anymore but employing a vast range of capabilities and resources to automate complex and cross-functional processes across the enterprise.
Robotic Process Automation (RPA) is tactical in unlocking business benefits since it helps you to automate your routine, repetitive tasks. Where businesses have been waiting for months to realize these benefits, RPA could be a quick and easy way when they do not have other viable options. RPA could be a steppingstone into the world of automation, but you need to look at your organization’s broader objectives that could be end-to-end process orchestration, integrations, AI and ML. You could build them piecemeal; it doesn’t necessarily have to be in a sequence. It should be iterative since it’s a long journey.
To summarize, although it starts with RPA at the core, hyperautomation actually helps provide scale since it is about automating end to end processes – not just a series of low-level repetitive tasks performed by robots but bringing in humans to collaborate, so that we can use data and insights for quick and accurate decision-making. That is imperative to scaling and providing an end to end solution.
Hyperautomation provides an extra layer of “intelligence” to automation that makes “smarter” processes. It is the “brains” behind automation.
In addition to extending across a wide range of tools, hyperautomation also refers to the following steps of automation –
Discover & Analyze – One of the challenges experienced by early adopters of hyperautomation is that business leaders don’t know which end to end processes to automate first to realize the most value. They can also find themselves in an automation post-deployment slump when they’re unsure which processes to automate next so that the work queue for the digital workforce remains full and profitable.
Organizations need to have a clear process discovery strategy to identify opportunities, analyze productivity gaps, and optimize human driven business processes that can be enhanced with AI, ML and cognitive technologies to maximize hyperautomation suitability. To optimize an end to end process to increase efficiency and decrease costs, we need to start by understanding how it currently works. That’s where process discovery and documentation become critical.
During this phase, you also need to identify what AI/ML capabilities you need. AI isn‘t monolithic. The tremendous value that AI creates comes from various AI capabilities, and your proposed solution might need several of them.
An effective process discovery analysis tool can also help determine which processes to automate and could reduce the preparation time for a deployment from weeks to hours. A process discovery tool can assess, qualify and prioritize processes for hyperautomation readiness and ease, as well as potential business value. Thorough analysis and prioritization of the “hyperautomation pipeline” is essential. It’s important to start small and not boil the ocean.
Design & Automate – Processes selected for hyperautomation need to be modelled to understand the entire flow, exceptions and hand-offs to another agent (BOT or human). Sometimes, you may need to redesign the process to maximize the benefits from automation. It is important to dive deep into the process and identify all exception scenarios. In some processes, it could be advisable to automate the time-consuming or the complex part of the process first and then build upon it. Develop the automation implementation process in phases – it may not be prudent to automate all scenarios in one go.
Data is key to drive success. The principle limitation of AI is that it learns from the data that is available. Garbage in is garbage out. That means any inaccuracies in the data will be reflected in the results. Machine learning algorithms are also very dependent on accurate, clean, and well-
labeled training data to learn from so that they can produce accurate results. So, quality of the data goes a long way toward determining the quality of the result.
Another very important design consideration are integrations and how they play into the end to end chain.
Running a pilot first always helps. This allows you to observe the effectiveness and overall performance of your plan with an actual process in real-time. Examine the results of the pilot and adjust accordingly. These results can provide an insight into which processes should be included versus the ones that are better left out. This is also the right time to involve the right stakeholders to understand the long-term plan and then plan the next steps. Collaboration and involvement of relevant stakeholders is a must.
Running a pilot first always helps. This allows you to observe the effectiveness and overall performance of your plan with an actual process in real-time. Examine the results of the pilot and adjust accordingly. These results can provide an insight into which processes should be included versus the ones that are better left out. This is also the right time to involve the right stakeholders to understand the long-term plan and then plan the next steps. Collaboration and involvement of relevant stakeholders is a must.
Measure, Monitor & Reassess –Measuring key metrics, monitoring performance of the hyperautomated processes and reassessing them on a periodic basis is crucial to the program’s success.
This is often an overlooked area. Automation is not always a one-time activity. There will be changes in the processes and systems and there should be a good change management process in place. All industries broadly continue to be heavily disrupted through forces of competition, digital transformation and regulation. Our business processes constantly change as a result. Regular measuring, monitoring, reassessing, reprioritizing is not optional but a necessity to realize the full potential of hyperautomation.
Here is an example of some of the metrics we can measure –
| Category | Question | Example |
|---|---|---|
| Traditional Metrics | How are we performing relative to plan? | Time, budget and scope variance to plan |
| Agile Metrics | How frequently are we providing value? | Velocity Metrics Cycle Times |
| Financial Metrics | Are we creating financial value? | Revenue & cost metrics, payback period, ROI, NPV |
| Artefact Creation | Are we creating re-usable artefacts? | Number/Value of artefacts created |
| Competencies Gained | Are team members gaining valuable skillset due to “new & enhanced” processes and technologies | Number/Value of competencies gained |
| Stakeholder Satisfaction | Are my stakeholders satisfied? | Net promoter score, ‘gut-feel’ assessments, surveys |
| Process Performance | How are the hyperautomated processes performing? | Reduction in manual steps, average cycle time, reduction in errors/exceptions |
Expanding measurement beyond just process performance enables you to more holistically evaluate the progress of the initiative and its potential impact. By taking measurements frequently, you can uncover potential issues, shift directions accordingly, and if the metrics paint a very grim picture, you might be able to cut your losses early and focus on processes that are more promising. Moreover, evaluating groups of hyperautomation projects at the program- or portfolio-level can help inform your overall automation organizational strategy.
There are several automation platforms in the market today that offer automation at scale. UiPath’s Connect Enterprise Hub with its Robotic Operations Center (ROC) serves as a central place to manage and track digital transformation with
The Connect Enterprise Hub enables an organization to –
*Uipath’s Connect Enterprise Hub
According to the Economist’s recent report on automation, while 91% of surveyed organizations use automation technologies, only 51% make extensive use of them.
From a vendor perspective, there is a convergence of these technologies coming together, so in future you might get all of these in a single platform. – Gartner
Hyper-automation does not just refer to implementing tools to manage tasks. It requires collaboration between humans so that decisions are made using data and logic.
Hyper-automation is not about one software package or application but entails a slew of tools. Businesses will have to adopt tools that are “interoperable” and can work and communicate with one another. Not only will you want your solutions to be easy-to-use and scalable, but you will also need to consider how the addition of a tool will work with your existing methods of operation. If you’re looking for a new tool or platform, an important consideration is one that is easy to use, scalable, and works across platforms and systems. You’ll want to introduce tools that are “plug and play” solutions, which can pull data from different sources and can use APIs to talk to your existing software.
“Hyperautomation requires selection of the right tools and technologies for the challenge at hand” – Gartner
Gartner calls selection of the right tools as “architecting for hyperautomation.” What it means is that “organizations need the ability to reconfigure operations and supporting processes in response to evolving needs and competitive threats in the market. A hyperautomated future state can only be achieved through hyperagile working practices and tools.”
A pertinent question is how will hyperautomation affect your job or business and what can you achieve by adopting it. It is important to understand that the point of automation is to augment human capabilities, not to replace them. Hyperautomation should not be a threat to the workforce. It provides
an opportunity for reskilling, allowing more time for people who can now shift their focus on higher-level roles.
The case of hyperautomation and automation tools continue to grow. The path forward is to adopt and stay ahead of the curve by continuing to evaluate your current processes and make sure that the tools and technology stack you are using is working well.
“Robots aren’t here to take away our jobs, they’re here to give us a promotion.” – Gartner Research Director Manjunath Bhat
Hyperautomation will empower and elevate your workforce by training them in the latest business and technology skills so that they can perform their roles efficiently. Instead of spending time on low-level, mundane tasks, they will remain engaged with their jobs as problem solvers providing creative
solutions.
Hyper-automation offers your business the following:
“By 2024, organizations will lower operational costs by 30% by combining hyperautomation technologies with redesigned operational processes,” Gartner Predicts 2020: RPA Renaissance Driven by Morphing Offerings and Zeal for Operational Excellence report
Hyperautomation is a reality and here to stay. It can be both a boon and a bane. Thinking of leveraging automation without being aware of the process and just following the trend could prove disastrous to your business. The need of the hour is to identify areas where hyperautomation is really needed.
However, the benefits far outweigh the risks. This ‘intelligent’ layer with its myriad AI technologies (from Natural Language Processing (NPL), that lets bots interpret human speech, Optical Character Recognition (OCR), that lets bots convert images to readable text, and Machine Learning (ML), that lets bots identify patterns in data) is imperative for growth and innovation. When combined with the right automation software, it can dramatically expand the automation possibilities and benefits for your enterprise.
We at Exavalu can help develop a “Hyperautomation” strategy for your organization by combining workflow automation and data-driven machine learning to deliver an efficient business process that is augmented by today’s digital workforce. We can help identify key areas within the business that could benefit from Hyperautomation and provide business process discovery, analysis, selection, prioritization, design and deployment of processes that will be help you achieve your intelligent automation goals.
Jayati Hazra is a Director with Exavalu and has 20 years of experience in Enterprise Solutions, Business & Technology Consulting in Lifesciences, Public Sector and Manufacturing industries. You can reach her at Jayati.Hazra@exavalu.com
Exavalu is your strategic partner on high impact Digital transformation relevant for your Industry. We’re a unique Business Advisory & Technology Consulting firm run by seasoned Industry veterans that are former executives, CIOs, CXOs, and Consulting Principals. We deliver meaningful change and sustained value aligned with your desired business outcomes leveraging our Industry experience and Solutions capability.
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