For modern businesses, there is a constant pressure to control costs and improve efficiency, which becomes extremely challenging for teams that are still largely reliant on manual workflows.
Even teams that have begun to automate certain tasks and workflows will eventually hit a plateau with what they can achieve through basic automation.
In many cases, humans will need to be involved to kick off, finish, or analyze these workflows, meaning teams are still susceptible to inefficiencies and bottlenecks.
Hyperautomation takes these capabilities to the next level with interconnected systems that can run autonomously. When implemented successfully, certain workflows will run from start to finish without human intervention, and strategic workflows will be reserved for human employees.
Hyperautomation definition
Hyperautomation is an organizational strategy in which teams aim to automate every process possible using advanced technologies such as artificial intelligence (AI), machine learning (ML), and robotic process automation (RPA).
In contrast to standard task or workflow automation, hyperautomation focuses on building an integrated system that can handle sophisticated business and IT processes independent of humans.
Instead of specializing in just a single repetitive, routine task, hyperautomation enables intelligent decision-making across multiple functions and departments.
Key technologies involved in hyperautomation
- Artificial Intelligence (AI): AI in automation helps handle tasks that require cognitive thinking and reasoning, with the ability to ingest and make sense of unstructured data and to learn from it continuously.
- Machine Learning (ML): Completes pattern recognition and predictive analytics to help spot anomalies or outliers in data and improve forecasting abilities.
- Robotic Process Automation (RPA): Tools that help automate repetitive, rules-based tasks.
- Low-code/no-code platforms: Enable non-technical personnel to develop and deploy solutions without requiring programming resources or expertise.
How hyperautomation works
Like with most digital transformation efforts, hyperautomation does not happen overnight, nor does it occur without some level of trial and error.
While implementation will look different for each team depending on its desired use case and existing tech stack, here is a general outline of what this process may look like
- Identify processes: Implementation teams should take time to understand how key workflows in the organization work and the various steps they involve. The goal is to identify workflows that are good candidates for automation and potential gaps or areas for improvement.
- Start with simple tasks: To start off, choose a basic task with a clear process and outcome. This will make it easier to test the automated system without many variables to consider.
- Choose the right tools: Determine which tools and technologies are the best choice to achieve your desired outcome. For instance, RPA will be best for data entry or rule-based tasks, while AI or ML are better for complex decision-making.
- Orchestrate workflows: With the chosen tools, build a first pilot workflow for testing. This will involve creating individual automations for each step in a workflow, then orchestrating them together to handle the process from end to end.
- Iterate and improve: Based on the results of the initial pilot, continue to make tweaks and adjustments to get the system working as intended. Over time, teams may scale the system to handle more applications.
Benefits of hyperautomation
Implementing hyperautomation is a massive shift for organizations toward a more efficient and productive environment. Let’s take a quick look at some of the possible advantages it can provide.
Increased efficiency
For many organizations, the most significant benefit they expect from hyperautomation is efficiency improvements.
Automating both repetitive tasks and complex decision-making means these workflows can be consistently completed around the clock, without relying on human availability or effort to get the job done.
At the same time, humans are reserved for more strategic work because they’re not bogged down with routine tasks. The end result is a team that can get more work done in less time without sacrificing quality.
Improved accuracy
With less reliance on human resources, organizations can enjoy greater accuracy in key workflows. Every manual process is prone to human error, as even seasoned employees can make mistakes.
More accurate workflows mean teams can more readily rely on the results for decision-making purposes. While there’s always a possibility of error, this is significantly reduced when a process is automated rather than handled manually.
Cost reduction
On a similar note, efficiency and accuracy improvements often come hand in hand with reduced costs and wastage.
With less of a need for human resources to complete repetitive, time-consuming tasks, personnel costs can be reduced.
Likewise, automation helps minimize error rates in manual work. This creates additional cost savings as processes only need to be done once without any time or effort spent to correct mistakes.
Enhanced decision-making
An interconnected automated system can strengthen decision-making abilities by providing real-time updates and feedback across functions and workflows.
This way, teams have the information they need to make quick and informed decisions. They don’t need to take the time to run reports or check individual systems manually. Instead, the integrated system provides the necessary data at a glance.
Potential challenges of hyperautomation
Despite the impressive benefits of hyperautomation, teams may encounter serious challenges and roadblocks during implementation.
Reliance on legacy systems
From a practical standpoint, teams that still rely on outdated or disparate technologies will have some additional work to do before they can fully embrace hyperautomation.
Legacy systems don’t always have the necessary infrastructure for basic automation; nonetheless, an integrated system with other automated tools.
As such, these organizations may need to first focus on upgrading their existing tech stack before they can start to optimize their workflows with automation.
Resistance to change
Additionally, the push for hyperautomation may come from a few key leaders within the organization. Those most affected by hyperautomation in their day-to-day activities may be less welcoming of the new technology and the disruption it will cause.
For starters, they may feel threatened by the tool and worried it will put them out of a job. Besides that, it simply takes time, clear communication, and adequate training for employees to get used to a new system or standard operating procedure.
Technical expertise
Achieving hyperautomation requires advanced technical skills to implement, oversee, and maintain. For smaller organizations, this may not be something that they are equipped to handle with the resources they have in-house.
On top of that, AI and machine learning are relatively new technologies, making experienced professionals in this space hard to find. Recruiting the experts that do exist is often challenging, as they are in high demand and warrant competitive compensation.
Use cases for hyperautomation
What’s possible with hyperautomation is still being determined as AI and ML capabilities continue to advance.
Here’s a quick look at some of the possible applications of hyperautomation within an organization:
In finance and accounting
Businesses may automate end-to-end finance and accounting functions that require little to no human intervention to streamline operations and reduce errors and costs.
This includes accounts payable and receivable processes, starting with automated invoice processing, purchase order matching, data entry, and digital payment processing.
In other words, with the right tools, the business could receive an invoice, verify it for accuracy, and make the necessary payment without a human ever getting involved in the process.
Similar use cases for hyperautomation in finance and accounting include loan processing, bank reconciliations, and risk management.
In customer service and support
Likewise, customer service teams across industries can use AI and machine learning technologies to automate support tasks, reducing their reliance on human resources.
Automated chatbots to streamline responses to FAQ-type questions are nothing new. However, emerging technologies enable support teams to create AI agents that can handle complex inquiries and troubleshooting beyond basic triaging.
In practice, this could look like an AI support agent that interacts with the customer in plain language, offering personalized responses based on the unique details of their query.
From there, the agent could complete necessary data entry, conversation tagging, ticket routing, and escalation based on the set rules of the system.
In the supply chain
Automation is already deeply ingrained in supply chain and logistics workflows. However, the appetite for organizations to embrace hyperautomation will be transformative for this industry.
In practice, this could mean building on existing automations to reduce repetitive tasks such as inventory checks and order fulfillment. This may be paired with intelligent decision-making to optimize shipping routes, analyze supply chain disruptions, forecast orders, and more.
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Frequently asked questions
What is the difference between AI and hyperautomation?
Artificial intelligence (AI) and hyperautomation are often mentioned together, though they are not interchangeable. Specifically, AI is one of the several technologies that power hyperautomation along with machine learning, robotic process automation (RPA), and business process management (BPM).
What is the difference between automation and hyperautomation?
There is not always a clear delineation between automation and hyperautomation in practice. In theory, basic automation focuses on completing a single repetitive task autonomously. These tools are built to specialize in a single task. Hyperautomation differs in that it involves an interconnected set of automated tools to power intelligent decision-making across functions or departments.
What is the future of hyperautomation?
Hyperautomation is not a brand-new topic, though many companies are still in the early stages of implementing basic automation. Because of this, the future of hyperautomation is rather dynamic as the underlying technologies continue to evolve and teams experiment with possible use cases in their organizations.
Will RPA be replaced by AI?
No, robotic process automation (RPA) will not be replaced by AI. These are two separate technologies that work together in many automation applications but play distinct roles. Put simply, RPA is best at automating tasks with rule-based guidelines, while AI is better at tasks that involve cognitive reasoning.
What are the disadvantages of hyperautomation?
The possible disadvantages include the technical skills and personnel required to implement such systems, which may not be feasible for all organizations. In addition, there are ethical concerns with hyperautomation, such as replacing human workers and automating a portion of the population out of work.
