Enterprises are constantly looking for ways to develop greater responsiveness towards disruption and volatility. In focus are supply chain challenges, economic uncertainty, and rapidly changing consumer needs. The responsiveness and agility of an organization to external forces are being seen as key differentiators between companies that survive and those that thrive.
Automation technology has emerged as a key enabler of responsiveness. Strategically implemented automation allows enterprises to reimagine business processes, increase productivity, improve efficiency, drive cost savings, and elevate business resilience.
Given its rising importance, enterprise automation is evolving by the minute. Technologies like RPA are maturing at a breakneck speed and have moved up from basic rule-based automation. Automation is now integrating technologies such as AI and ML to drive impressive business outcomes in a tough marketplace.
In this blog, we will examine the top 7 trends transforming the world of enterprise automation.
Hyperautomation: The Rise of the “Automation Superpower”
Hyperautomation is no longer a futuristic concept. The Hyperautomation market size is estimated at USD 12.95 billion in 2024 and is expected to reach USD 31.95 billion by 2029. Gartner predicts that 65% of large organizations worldwide will have deployed some form of Hyperautomation this year.
Hyperautomation combines various automation technologies like RPA, Artificial Intelligence (AI), Machine Learning (ML), and Business Process Management (BPM) to create a comprehensive and intelligent automation ecosystem. These technologies work together to automate not just repetitive tasks but also complex decision-making processes.
Enterprises can accelerate operations while cutting down errors with Hyperautomation. This technology also spots safety lapses and averts catastrophic mishaps by enabling enterprises to modernize their security protocols and procedures.
Hyperautomation has several use cases across industries and verticals. From banking and finance to healthcare, automotive to manufacturing, as digital technologies expand so do Hyperautomation opportunities to securely automate key business processes and application.
Workload Automation and Intelligent job scheduling
Workload automation and intelligent job scheduling are other enterprise automation trends that are rising rapidly. The workload automation market is expected to reach $5 billion by 2030, growing at a CAGR of 7.5% during the forecast period 2023-2030.
An increased emphasis on compliance and growing cloud adoption fueling the need for workload automation. Enterprises are looking at this technology to manage and optimize workloads across hybrid and multi-cloud environments. They are looking at service providers who can offer tailored solutions that integrate seamlessly with existing systems, address specific and unique client needs, and offer ongoing support, and risk mitigation while addressing the complex and evolving needs of enterprises in their automation journey.
Moving beyond structured data
Enterprise automation solutions and RPA have been primarily focused on automating tasks involving structured data. This trend is now shifting and moving towards taking on unstructured data as well. Integrating AI, ML, OCR, or NLP into the RPA framework enables enterprises to extract, classify, and analyze relevant information from the unstructured data, and feed it into RPA workflows. access
Enterprises are looking to use data from emails, documents, and social media content to handle tasks like data extraction, sentiment analysis, and automated responses.
The Power of Process Mining
As automation becomes a key driver of enterprise success and agility, process mining becomes a pivotal new area of adding value. Reports estimate that the process mining market will reach a value of $12.1 billion by 2028.
As more regulated industries such as healthcare, banking and finance, pharma, etc. look at automation adoption the capabilities of process mining tools become crucial to drive automation success. Banks, for example, are leveraging process mining to analyze loan application processes.
Advanced process mining tools and the use of data analysis techniques to extract insights on how tasks are actually performed are now critical to drive powerful automation opportunities.
Robust process mining skills are becoming essential for enterprises to adopt a data-driven approach and identify bottlenecks, redundancies, and opportunities for automation, ensuring they automate the right processes effectively.
The Human-in-the-Loop Automation
Automation is set to change the workplace and make it more efficient and effective. Automation, however, does not spell the end of human jobs. Rather, it is moving towards becoming one of the greatest enablers of human capabilities.
The human-in-the-loop automation approach incorporates human decision-making when needed into automated systems. This automation approach allows human workers to interact with automated systems, change the direction of the automated process, and enable algorithms to improve themselves through this feedback.
With this automation, bots can seek human direction and continue the process with the new information. This is especially useful for edge case handling. Customer service, invoice processing, DevOps, etc. are areas where human-in-the-loop automation shows significant value and improves automation efforts using enhanced and intelligent review and verification actions.
With bots handling repetitive tasks, humans can focus on higher-order activities like strategic decision-making, customer interaction, and exception handling.
RPA Evolution – Beyond Scripting, Towards Intelligence
RPA, a core pillar supporting enterprise automation is also evolving rapidly. This technology that automated rule-based tasks through scripting is now ready to add AI and ML capabilities into the RPA framework.
RPA not only mimics simple tasks but can also handle more complex tasks, learn from experience, and make basic decisions based on pre-defined rules when powered by AI and ML. With Intelligent RPA or IPA, bots can handle exceptions and more complex tasks.
Intelligent automation provides many use cases across industries. Improved productivity, elevated customer experience, workforce augmentation, process consistency and accuracy, and regulatory compliance are a few of the reasons driving this adoption.
Deloitte’s recent annual global survey states, “73 percent of respondents said their organizations have embarked on a path to intelligent automation: a significant 58 percent jump from the number reported in 2019.”
Trends show that while RPA excels at replicating repetitive tasks, it can struggle with decision-making. This is where IPA comes in. IPA solutions harness the power of AI and ML to automate tasks that require judgment and help enterprises navigate today’s dynamic and disruptive market.
To sum up
While intelligent automation brings immense benefits, businesses need to navigate the challenges of implementing and managing complex automation solutions in the face of lacking skills and expertise in RPA, AI, and process design.
Effective change management strategies, well-defined and easy-to-use UI and UX, and robust security controls also become important considerations as they influence employee buy-in and successful adoption.
Integrating automation with systems containing sensitive data also raises security and privacy concerns. Domain and technology expertise along with implementation experience become pivotal in designing impactful automation systems and processes that deliver greater efficiency and business value. Let us show you how.
