Hyperautomation is an emerging trend where businesses seek to automate as many operations as possible.
According to Gartner, hyperautomation will lower operational costs by 30% through 2024. The good thing is that hyperautomation can be used across numerous industries and use cases.
Our guide highlights the importance and applications of hyperautomation today. First, we define it.
Defining hyperautomation
Hyperautomation is the process of using multiple advanced technologies to automate business functions.
In most cases where we have studied organizations that have embraced this concept, the typical technologies include:
- Artificial intelligence (AI)
- Machine learning (ML)
- Robotic process automation (RPA)
- Low code and no code tools.
To achieve hyperautomation, these technologies must integrate seamlessly throughout an organization. They can apply to manufacturing processes, market research tasks, cybersecurity, and numerous other aspects of business.
Also Read: Creating an AI Strategy for Your Business
Hyperautomation vs. Automation
In the traditional sense, automation means processes that use a specialized software to function within a computer, e.g., an email marketing solution. In the case of emails, for example, automation makes it easier and faster to schedule and distribute email messages en masse.
In terms of manufacturing, where a lot of automation occurs, it simply means using automated equipment instead of manual labor to increase production capacity. The equipment is programmed using automation software to power the machines to precision.
Hyperautomation is different from both definitions of automation above. It means adding advanced technologies to support or augment human work, rather than replacing it. For example, an employee can use a chatbot to help with their workflow, not allow the chatbot to perform the entire workflow. Hyperautomation can empower employees to perform better at their tasks. It can also increase accuracy and output in robots or machines in processes such as manufacturing.
Hyperautomation vs. RPA
The distinction between Robotic Process Automation (RPA) and hyperautomation lies in their scope and depth of application within business operations. RPA forms a part of the broader concept of hyperautomation but with a specific focus. It is typically used for automating straightforward, repetitive tasks by emulating human interactions, particularly useful in activities such as data processing, automated filling of forms, and generating reports.
Conversely, hyperautomation expands well beyond the realm of RPA's capabilities. It not only automates tasks but also enhances human tasks and the decision-making process. By combining RPA with more sophisticated technologies like AI, ML, and in-depth analytics, hyperautomation covers a wider range of functionalities. This includes not just automating tasks but also providing advanced decision support, predictive modeling, and managing more intricate processes that require intelligent and adaptive approaches, surpassing simple repetitive tasks.
To put it simply, RPA is a critical element within the hyperautomation framework, but hyperautomation encompasses a more extensive, organization-wide approach to automation that includes and goes beyond the functionalities of RPA.
Also Read: Guide Into IT Process Automation
Advantages of hyperautomation
Since hyperautomation is not a single software or solution, it requires a strategic approach to implement it in a business. Let's consider an order fulfillment workflow as an example, which can be hyperautomated using the following solutions:
- A robotic process automation (RPA) tool to create orders and invoices
- A machine learning tool to capture data in each order and invoice
- A chatbot to interact with customers concerning their orders
- A business analytics tool to identify and address gaps in the ordering process
- A no-code collaboration tool to unify sales, marketing, finance, logistics, and other stakeholders involved in order fulfillment.
This combination of tools can keep a business competitive and provide an enhanced customer experience. Hyperautomation can offer even greater benefits to an organization.
Let’s look at the top benefits of hyperautomation:
1. Hyperautomation reduces errors
If your business handles large volumes of data, you can use hyperautomation to improve accuracy.
AI and ML tools can identify mistakes, correct errors, and refine details faster than manual data analysis. They can also ensure that no errors are introduced into business data during input processes, e.g., scanning invoices or programming internet-of-things (IoT) devices in the workplace.
2. Hyperautomation reduces costs
Hyperautomation tools help businesses to perform predictable, consistent processes. You can integrate them with both legacy and cloud-based systems without disrupting operations.
These tools essentially make it so easy to scale up or down depending on the prevailing market demands. All these factors contribute to cost savings in terms of time, capital, and human resources.
Don’t forget that AI and ML tools also keep learning over time as the company generates more data. This means operations keep improving and cost savings increase in the long term.
3. Hyperautomation can boost employee satisfaction
When you integrate hyperautomation tools into processes, you create a cutting-edge working environment. These tools bring visibility into each workflow. They offer management a good view into how every employee adds value to the overall business.
Hyperautomation also eliminates tedious repetitive tasks, e.g., manual data entry. This frees teams to focus on more critical business projects.
4. Hyperautamation leverages business data to improve decision making
Data-driven decision-making means using facts, metrics, and insights to make business decisions.
Hyperautomation tools unlock the true value of business data to help leaders and the rest of teams make swift, accurate decisions. These tools are helping companies to visualize trends in data and adjust business objectives.
For example your company can use a machine learning analytics tool to accurately target customers with marketing content.
Also Read: The Rising of AI Interfaces
Common challenges with hyperautomation
Despite the benefits we’ve just seen, hyperautomation comes with challenges that businesses must overcome:
1. Organizational change management
Hyperautomation is a commitment to a new, innovative way of doing things in a business. This means reorganizing operations to adapt to disruptive technologies like AI and ML.
As with any type of organizational change, implementing hyperautomation can be a major shakeup for employees. You need full buy-in from all shareholders, particularly from teams whose workflows are impacted by the technology. This requires a comprehensive change management strategy and transparency throughout the organization. Otherwise, shareholders may resist the change. Such resistance can slow down operations and lead to losses.
2. Technological investments
Hyperautomation demands significant financial commitment. Businesses must invest in a suite of advanced tools and software for different functions, e.g., document processing, data collection, anti-money laundering (AML), and more.
The licenses and implementation costs for these tools can add up to a huge financial investment.
To avoid incurring unnecessary costs, please take time to analyze and compare the costs of the particular features you need from the various hyperautomation tools. This will help avert a scenario where you end up paying for unnecessary functionalities.
3. Cybersecurity concerns
Since hyperautomation cuts across business systems, cybersecurity becomes a critical factor during implementation.
Each hyperautomation tool must meet security and compliance requirements for handling data and keeping malicious actors at bay. Otherwise, these tools can increase the risk of exposure to threats such as bots, malware, and ransomware.
Also Read: How to Deal With a Ransomware Attack
4. Biases in algorithms
Hyperautomation tools require algorithms to function at optimum levels at whatever they do, e.g., to process data, generate reports, and discover insights.
What we must always bear in mind is that these algorithms are created by individuals who may have deliberate or unintended biases, which lead to inaccuracies.
As the decision maker, you have an important role of ensuring that your hyperautomation algorithms are fair and ethical. This is especially critical when making decisions that affect customers, e.g., loans or insurance policies.
Hyperautomation examples and use cases
Before we look at the use cases, please note that hyperautomation technologies like machine learning and robotic process automation are considered process-agnostic. This means that they are interoperable with different systems and can work in any organization.
Here are some examples of how hyperautomation functions in different types of businesses today.
1. Retail
There are numerous opportunities for hyperautomation in the retail and e-commerce industry.
For example, AI-powered chatbots can help customers to make orders and troubleshoot common challenges with products. Facial recognition software can help to detect suspicious activities in stores. Procurement processes can streamline billing, inventory, and logistics using machine learning.
Hyperautomation can also help retail businesses in the following ways:
- Processing returned goods and corresponding refunds
- Forecasting customer demand to allocate resources appropriately
- Integrating sensors and vision systems in product shelves to monitor inventory in real-time
- Using robots to sort packages and orders before shipping.
2. Healthcare
Healthcare providers can use AI, ML, and robotics tools together to save costs and improve patient experience.
A good example of hyperautomation in healthcare occurred during the Covid-19 pandemic. Technology enabled healthcare providers to track the spread of the disease, automate lab tests, and disseminate test results on a massive scale.
Other use cases for hyperautomation in healthcare include:
- Documenting patient care using standardized templates and formats
- Creating algorithms to detect anomalies in x-rays, ultrasounds, mammograms, etc.
- Automating patient records for compliance and insurance purposes.
3. Finance
The Covid-19 pandemic also had a significant impact on the finance and banking industry.
Customers moved to mobile and online banking. As they moved, the demand for personalized user experience skyrocketed.
Hyperautomation can solve these challenges by utilizing AI and ML in banking, budgeting, and payment apps.
More use cases for this industry include:
- Automating back-office functions like auditing, accounting, and loan operations
- Implementing conversational AI chatbots for 24/7 customer service
- Detecting fraudulent activities, e.g., suspicious transactions or phishing emails.
4. Supply chain management
Similarly, supply chains across industries faced numerous challenges during the pandemic. In fact, many businesses are still feeling its effects in terms of receiving materials on time and adjusting to staffing challenges
This is where robotic process automation (RPA) becomes an integral part of supply chain hyperautomation. It can streamline procurement, vehicle fleets, follow-ups with vendors, and more.
Hyperautomation can also help supply chains by:
- Automatically tracking orders to forecast changes in supply and demand
- Automating data-heavy processes like invoicing, vendor onboarding, and compliance
- Integrating blockchain technology to secure supplier contracts and deliverables.
Key steps for implementing hyperautomation
Our position is that hyperautomation should only be used for specific functions in a business. We don’t think that everything should be automated. This is because there are certain tasks that are best performed by humans rather than machines.
Having said that, please consider these essential steps when planning your hyperautomation strategy.
1. Understand business needs
Start by evaluating your current operations and processes. If these processes are lagging behind industry standards, hyperautomation may help to bridge the gap.
Create opportunities for employees to reveal where their challenges lie, e.g., information silos, workflow bottlenecks, and repetitive tasks. All these are opportunities for hyperautomation.
You can consider low code and no code technologies to address some of these pain points before investing in custom hyperautomation solutions.
2. Gather data
Hyperautomation tools require training data to develop accurate algorithms.
Please evaluate your real-time and historical data, which serves as the foundation for hyperautomation. This data can include:
- Sales and marketing reports
- Website analytics
- Customer support tickets
- Project files
- Invoices, etc.
This data can be used to train hyperautomation models for AI chatbots and machine learning algorithms.
3. Select hyperautomation tools
As mentioned previously, hyperautomation involves a suite of tools, e.g., ML platforms and data analytics software.
These tools must exchange data with each other by using application programming interfaces (APIs) and plugins. These enable all the solutions to connect to each other without changing the underlying infrastructure.
To get it right in this step, consider hiring an external consultant when evaluating hyperautomation providers, such as:
- Automation Anywhere for robotic process automation (RPA)
- OneGlobe for advanced business analytics
- Appian for low code business process automation (BPA)
- SolveXia for financial automations
A hyperautomation consultant can also train employees on how to integrate the technologies into their workflows.
4. Organize employee roles
Since hyperautomation will significantly affect your employees, you will have to plan to assign the right leaders to guide the transition.
One strategy is to assign project leads for each department that is implementing a hyperautomation tool. For example, an IT lead would guide automations in AI application development. A sales lead would guide chatbot implementation.
These team leaders can help front-line employees to navigate the new tools and implement them into their daily workflows.
5. Implement and measure success
The process of hyperautomation generates fresh data about the organization's operations. This data is critical to understanding whether the hyperautomation tools produce the intended outcomes, e.g., better productivity, cost savings, etc.
So you want to set the correct key performance indicators (KPIs) to check the effectiveness of hyperautomation across the board, such as:
- Technical KPIs, e.g., algorithm precision, accuracy, and speed
- Process KPIs, e.g., speed of completing tasks before and after automation
- Business KPIs, e.g., cost savings, productivity improvements, and return on investment (ROI).
These KPIs provide a golden opportunity to learn how to adapt the hyperautomation strategy to suit teams and workflows. They also give employees concrete performance targets that demonstrate tangible improvements in their productivity.
Conclusion
Hyperautomation is all about businesses using disruptive technologies to improve IT operations. For example, it can help in creating a digital twin. With digital twins, you can simulate real-world scenarios and discover hidden ways to add value to your company’s offerings.
Ultimately, hyperautomation extends existing capabilities. It can automate operations from end to end and free up employees to upskill and adapt to new workflows. With the right hyperautomation suite of tools, your company can accelerate growth and performance while saving costs in the long run.
