Hyper Automation vs Automation: What’s the Difference?
Just like using an electric mixer to whip up a batter, automation enables us to take out the manual effort involved in several repetitive tasks and speed things up.But as humans, we always want more.
I happened to contribute to a project that took things to the next level. The company wanted to optimize its entire order-to-cash process, which involved multiple departments from sales to finance.
The process began with the regular automation of repetitive tasks such as data entry and form-filling. They took things to the next level by using artificial to enhance decision-making and improve process efficiency. AI-powered algorithms were used to analyze past data, predict future trends, and automate decision-making processes. For example, the system could automatically approve orders based on predefined criteria or suggest negotiation strategies based on market data.
Then they cemented everything in place using automation platforms to orchestrate the entire workflow for efficient data exchange between various components of the order-to-cash automation machine. Now this was a whole new level of automation, which required limited human intervention- and that's what hyperautomation is about
Initially, the company could only automate individual tasks like data entry while they manually handled decision-making processes and sometimes data analysis. However, hyper-automation created a more efficient and cohesive system.
What Is The Difference Between Hyper Automation and Automation?
The previous section might have given you some highlights about the difference between hyper automation and automation. However, this section will go into the differences in more detail.
While hyper automation and automation aim to streamline processes and improve efficiency, they differ significantly in their scope, integration, scalability, decision-making capabilities, and approach to continuous improvement.
Scope
Automation
Remember the company I spoke about in the introductory section of this article?
While they were still automating only individual tasks, that was ordinary automation. Automation is limited to streamlining singular tasks or specific steps within a business process like data entry or social media posting.
Hyper Automation
Now when they decided to automate their entire order-to-cash workflow, this meant integrating various technologies and tools to automate the entire process. Beginning with order entry, then invoicing, and finally, payment collection. This is hyper-automation. It takes a more holistic approach and aims to automate entire business processes. This enables you to make your enterprise more agile and adapt to AI trends easily. We explained this in depth in our guide on the new enterprise AI set up
Tools/Tooling
Automation is a rigid and rule-based approach that is well-suited for simple repetitive tasks. These tools follow a predefined sequence of steps and find it difficult to handle complex tasks. They also involve using separate tools for different tasks, leading to a fragmented and less integrated workflow.
Hyper Automation, on the other hand, is a more intelligent and adaptive approach. It leverages AI and machine learning to analyze data, identify patterns, and make informed decisions. This allows hyper automation tools to adapt to changing technology and handle complex situations effectively. Unlike traditional automation, hyper automation platforms often provide a unified interface for managing and coordinating various automation tools, creating a more seamless and efficient workflow. A great example of tools like this is Activepieces.
Hyper automation tools can also handle tasks that require judgment, reasoning, and problem-solving. They can analyze data, identify patterns, and make informed decisions, even in complex or uncertain environments. This makes them a valuable tool for automating tasks that we previously thought were too complex for machines.
Decision making
Automation
Traditional automation handles specific tasks by executing each step in a specific sequence -no deviation. It never strays from the script or instruction it was provided with. However, this is great if you are assuming an ideal environment where there are no conflicts to be resolved or no nuances to consider.
But how possible is that?
Hyperautomation
Back to the company that I was speaking about at the beginning of this conversation
As the business grew, so did the complexity of the orders. We started encountering issues that traditional automation couldn’t handle. For instance, if there was a conflict in the order details or a delay in payment, the system would halt, waiting for human intervention.
One time there was a sudden spike in orders during a promotional campaign. Traditional automation would have struggled to keep up, leading to delays and errors. But with a hyper-automation set up, the system analyzed the incoming data, predicted the surge, and adjusted inventory levels in real-time.
In simpler terms, while automation follows a set of rules, hyper-automation is more like a seasoned expert who can think on their feet, adapt to new situations, and make informed decisions. Hyper automation is a shift from rule-based to intelligence-driven systems truly transforms the way we approach complex problems, making our processes more efficient and resilient. This allows hyper automation to handle more complex tasks, adapt to changing circumstances, and even make predictions based on data.
Integration
Automation often involves integrating with other systems, but the level of integration can be limited. It might be able to connect to a few specific systems, but it might struggle with more complex integrations or data exchanges.
Hyper Automation, on the other hand, focuses on seamless integration. It aims to connect various systems, tools, and data sources to create interconnected workflows. This means that different parts of the business can communicate and collaborate more effectively. For example, a hyper-automated supply chain might integrate with inventory management systems, transportation providers, and customer relationship management systems to optimize the entire process. In essence, automation is about connecting a few pieces, while hyper-automation is about building a well-connected network. This is crucial for achieving the full potential of automation and driving efficiency.
Scalability
Automation can be scalable to some extent. You can often increase the volume of work it handles by adding more resources or adjusting the system's configuration. However, as the workload grows, you might need to manually adjust the system or reprogram it to handle the increased volume. Think of it like a car - you can increase its load capacity, but there are limits.
Hyper automation, on the other hand, is designed for scalability. It can handle increased workloads without significant manual intervention. It uses AI and ML to automatically adjust and optimize its performance. This means that as your business grows, your automation system can grow with it, handling more work and adapting to new challenges.
In simpler terms, automation is like a car that needs regular tuning, while hyper automation is like a self-driving car that can adjust its speed and route based on traffic conditions. This makes hyper automation a more flexible and scalable solution for businesses that are constantly evolving.
Continuous Improvement
Automation often involves periodic updates and refinements. You might need to make changes to the system from time to time to address new requirements or fix issues. Think of it like maintaining a car - you need to do regular check-ups and repairs.
Hyper automation, on the other hand, emphasizes continuous improvement. It uses data-driven insights and AI to identify opportunities for optimization. The system can learn from its experiences, adapt to changing conditions, and continuously improve its performance. It's like a self-improving car that gets better over time. This does not totally eliminate the need for human checks but it is not as dependent on human intervention as traditional automation
Examples of Automation vs Hyper Automation In Enterprise Departments
Is hyper automation just hype or reality?
Let us explore the differences between automation and hyper automation in terms of use cases.
Finance and Accounting
Traditional Automation in finance and accounting often focuses on automating individual tasks, such as extracting data from invoices and matching them to purchase orders. While this can improve efficiency, it doesn't address the complexities and potential issues that arise in real-world financial transactions.
Hyper Automation, on the other hand, leverages AI and ML to create a more sophisticated and intelligent system. AI-powered systems accurately extract crucial information from invoices, including vendor details, invoice numbers, dates, and line items. Machine learning system can analyze invoices for red flags like unusual pricing, duplicate invoices, or inconsistencies in data. These invoices ar now compared to historical data to identify patterns that deviate from normal behavior, signaling potential fraudulent activities.
Going beyond simple data processing, hyper-automation can also assist in negotiations in the following way.
- AI can analyze market data to determine fair prices for goods and services.
- The system can suggest negotiation strategies based on historical data, supplier relationships, and current market conditions.
- In certain cases, the system can even automate simple negotiations, such as requesting discounts or negotiating payment terms.
Hyper automation in finance and accounting not only streamlines processes but also enhances risk management and empowers businesses with data-driven insights.
Human Resources
Traditional Automation in HR often focuses on automating routine tasks, such as sending welcome emails or setting up access to systems. While this can improve efficiency, it doesn't address the complexities and potential inefficiencies in the hiring and onboarding process.
Hyper automation, on the other hand, leverages AI and ML to create a more intelligent and efficient HR system. Here's how it works:
AI-powered systems can analyze resumes and job applications, identifying the most relevant candidates based on specific criteria. ML algorithms can score candidates based on their skills, experience, and fit with the company culture.
The system can automatically schedule interviews based on candidate availability and interviewer preferences. AI can also analyze candidate data to identify their preferred learning style and tailor the onboarding process accordingly.
Customer Service
Automation in customer service often involves using chatbots to handle simple, repetitive inquiries. While this can free up human agents for more complex tasks, it has limitations when dealing with complex queries or requiring access to a wide range of information.
Hyperautomation, on the other hand, leverages AI and ML to create more sophisticated and intelligent chatbots. Here's how it works: AI-powered chatbots can understand and respond to natural language queries, making interactions more human-like. Hyperautomated chatbots can access and process information from multiple sources, such as knowledge bases, CRM systems, and product catalogs. By analyzing customer data and preferences, chatbots can provide personalized recommendations and solutions.
IT Operations
Traditional Automation in IT operations often involves automating routine tasks, such as software updates. While this can improve efficiency, it doesn't address the complexities and challenges of modern IT environments.
Hyper Automation on the other hand, leverages AI and ML to create more intelligent and proactive IT operations. Here's how it works:
AI-powered monitoring tools can analyze vast amounts of data from IT infrastructure to identify anomalies and potential issues. ML algorithms can predict failures based on historical data and patterns, enabling proactive maintenance and correction.
Hyper automation systems can also solve certain IT issues automatically. For example it can trigger restarting servers or applying security patches, without human intervention. Hyper automation combines intelligent monitoring, predictive analytics, and automated correction to develop self-healing IT systems that can recover from failures independently.