Top 4 Trends Driving Enterprise Hyperautomation in 2024

Hyperautomation has emerged as one of the hottest technology trends, with enterprises using it to rapidly automate business processes. But what are the key technologies shaping hyperautomation today?

In this comprehensive guide, we‘ll explore the top 4 hyperautomation trends that should be on every CIO and technology leader‘s radar in 2024 and beyond. Let‘s get started!

What is Hyperautomation?

Hyperautomation refers to the approach of automating as many business and IT processes as possible using the most suitable automation technologies. It involves combining tools like robotic process automation (RPA), artificial intelligence (AI), machine learning (ML), process mining, low-code platforms, and more.

The goal of hyperautomation is to maximize the benefits of process automation by taking an intelligent, end-to-end approach. This enables greater operational efficiency, improved customer experience, lower costs, and better allocation of human talent to strategic tasks.

According to Gartner, the market for hyperautomation technologies is estimated to reach nearly $600 billion by 2022 as more organizations explore hyperautomation to digitally transform.

Let‘s examine the top 4 technology trends that are enabling enterprise hyperautomation initiatives today:

1. Intelligent Process Automation With AI

While RPA provides the foundation for hyperautomation, RPA bots are limited to automating repetitive, rules-based tasks. To deliver end-to-end automation, RPA needs to be combined with AI and ML technologies capable of handling complex processes.

This combination of RPA and AI is referred to as Intelligent Process Automation (IPA) and it is rising as one of the fastest growing segments within hyperautomation today.

According to Deloitte, organizations augmenting RPA with at least 2 AI technologies see over 2X the benefits compared to RPA alone. Some of the key ways AI augments RPA include:

  • NLP for processing unstructured data – like emails, support tickets, conversations
  • Intelligent document processing – automated extraction of data from documents
  • Chatbots – for natural language interactions and query resolution
  • Process mining – discovering processes, analytics, monitoring

Here is a comparison between RPA and IPA:

RPAIntelligent Process Automation
Type of ProcessesRepetitive, rules-based processesComplex processes involving unstructured data
Bot IntelligenceFixed logic and rulesAI-enabled, self-learning
Implementation TimeWeeks to monthsMonths to quarters
Ease of ScalingModerate, needs bot deployment per processHigh, single bot can automate end-to-end processes

According to McKinsey, Intelligent Process Automation can result in 50-70% reduction in processing costs by 2025. Leading RPA vendors like Automation Anywhere, UiPath, Microsoft, and others provide AI capabilities to power intelligent hyperautomation.

2. Process Mining Becoming Critical

Process mining is an integral technology for successful hyperautomation programs. It provides complete visibility into processes by capturing real-time data of actual process execution and analyzing it to discover bottlenecks, deviations, opportunities for improvement.

According to Gartner, 70% of new RPA implementations will leverage process mining capabilities by 2025.

Forrester Research predicts the global process mining market to grow at a CAGR of ~50% from 2020 to 2023 as more organizations adopt process mining to enable hyperautomation.

Some key benefits provided by process mining include:

  • Accelerated automation – By mapping processes before automation, process mining reduces RPA implementation time by 50-80%
  • Increased automation ROI – Organizations see 40%+ more benefits from RPA when using process mining compared to not using it
  • Ongoing optimization – Continuously monitoring automated processes for improvement opportunities

Leading process mining vendors like Celonis, UiPath Task Mining, Minit, and others provide easy integration with RPA and other automation technologies.

3. Digital Twins For Enterprise-Wide Automation

Digital twins, virtual replicas of organizations‘ physical assets and processes, are playing an emerging role in enabling enterprise-wide hyperautomation programs.

According to ABI Research, over 50% of major organizations globally are expected to be implementing digital twins by 2026.

Digital twins support hyperautomation initiatives in two key ways:

  1. Providing holistic visibility – Digital twins mirror the organization‘s assets and processes in one virtual environment to provide complete data-driven insights for automation.
  2. Enabling automation at scale – Organizations can use digital twins to simulate the impact of automation on interconnected processes before actual implementation. This de-risks automation and helps scale it enterprise-wide.

For instance, a healthcare provider can implement a digital twin of its facilities depicting all processes. This can help identify and prioritize automation opportunities across different departments like patient registration, lab testing, billing etc.

According to Gartner, the digital twin market is forecast to reach $38 billion by 2027, indicating its growing adoption as part of hyperautomation initiatives.

4. Democratizing Automation Using Low-Code/No-Code

One of the biggest roadblocks to hyperautomation is the lack of skills needed to develop automation solutions. Low-code and no-code platforms help democratize RPA and AI by enabling citizen developers to build solutions.

Low-code and no-code platforms provide visual, drag-and-drop interfaces and pre-built components for developing software robots and AI applications without coding.

According to Gartner, by 2025 around 70% of new applications developed by enterprises will use low-code or no-code technologies, up from less than 25% in 2020.

Democratizing automation allows organizations to scale their automation initiatives much faster across different processes and business units. Employees are also more likely to adopt solutions built internally leveraging their domain knowledge.

Leading RPA platforms like Microsoft Power Automate, UiPath StudioX, Automation Anywhere allow low-code automation development. Dedicated no-code AI platforms like MonkeyLearn, Amazon Honeycode, and others are on the rise too.

Here are the key hyperautomation trends that enterprise leaders should have on their radar today:

  • Combining RPA with AI/ML for automating complex processes
  • Adopting process mining for transparency and driving automation strategy
  • Exploring digital twins to enable enterprise-wide automation
  • Leveraging low-code/no-code to democratize automation development

A successful hyperautomation strategy requires an integrated approach across people, processes, and technologies. As hyperautomation continues maturing in 2024, the ability to intelligently automate end-to-end processes at scale will be the key competitive differentiator for enterprises.

I hope this guide provided you valuable insights on the top hyperautomation trends shaping the market currently. Let me know if you have any other questions! I‘m always happy to discuss more.

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