While hyperautomation hasn’t achieved widespread popularity among enterprises yet, it is swiftly transitioning from simple process automation to a sophisticated, interconnected ecosystem driven by AI, machine learning (ML), and robotic process automation (RPA). So, does this trend encourage businesses to adopt these advanced solutions? Most likely.
As per Gartner’s analysis, nearly one-third of enterprises are predicted to automate over half of their operations by 2026, a significant growth from just 10% in 2023. Despite the transformative promise of hyperautomation and the growing number of companies adopting it, many organizations still face challenges in scaling it effectively. Less than 20% of companies have fully mastered the hyperautomation of their operations.
This article will delve into the factors driving the evolution of hyperautomation, the key challenges it poses, and strategies for future-proofing business operations while avoiding common pitfalls.
Transitioning from Basic Automation to Intelligent Systems
Hyperautomation takes automation to an advanced level by integrating AI, ML, RPA, and other technologies. It enables businesses to automate complex tasks, process large volumes of data, and make real-time decisions. Unlike traditional automation, which targets individual tasks, hyperautomation builds systems that continuously learn and improve.
Despite its potential, many businesses have yet to embrace hyperautomation, possibly due to a lack of understanding of its strategic importance in remaining competitive in a digital-first world. Hyperautomation can reduce costs, enhance efficiency, minimize human errors in repetitive tasks, streamline operations, ensure regulatory compliance, and improve customer experiences.
Gartner’s forecast suggests that by 2026, nearly a third of businesses will have automated more than half of their operations, indicating a demand for systems that can analyze, learn, and adapt in real time.
For illustration, companies like Airbus SE and Equinix, Inc. have successfully implemented hyperautomation powered by AI for financial processes, significantly reducing workloads and accelerating operations. As data volumes increase and real-time decision-making becomes crucial, hyperautomation is vital for business success.
Challenges in Implementing Hyperautomation
Despite the allure of full-scale automation, actual adoption remains low. Challenges include undefined goals for hyperautomation, resource constraints, and resistance to change. Integration complexity with existing systems and the need for significant investments in training personnel further complicate matters, leading to a continued reliance on manual processes and outdated workflows.
Moreover, poor data culture hinders successful automation. Without structured data policies and well-documented workflows, businesses struggle with inefficiencies that automation alone cannot resolve. A lack of robust data governance can also lead to data quality issues, jeopardizing the accuracy and reliability of automated systems.
The disconnect between IT teams and the broader business infrastructure presents another challenge, often resulting in differing viewpoints that impede automation efforts. Bridging this gap requires strong advocates, either external consultants or internal team members committed to automation’s success. Aligning employee incentives, such as linking salaries or bonuses to automation outcomes, can drive efficiency and financial rewards.
Setting clear deadlines and success metrics is crucial to prevent automation initiatives from stagnating. Even with successful initial implementation, ongoing maintenance is necessary, including keeping pace with frequent software updates to ensure AI models remain well-integrated.
To streamline this process, minimize reliance on multiple software vendors, as fewer platforms simplify maintaining oversight and ensure smoother integration. Hyperautomation works best in companies with straightforward operations and clear protocols for updating and maintaining automated systems.
The Future of Hyperautomation: Startups Leading the Charge
Hyperautomation is especially advantageous for startups building from the ground up. Established enterprises, despite being hindered by legacy systems, possess large budgets and can hire extensive teams to address such challenges, unlike smaller companies with limited resources. Therefore, startups are likely to drive hyperautomation as a strategy to reduce operational costs.
Nevertheless, both established companies and startups must consider customer reactions. Negative impacts on customer experience, whether from poor implementation or lack of demand, must be addressed. Presently, customers often view AI chatbots and automated responses with skepticism, cautioning against unnecessary automation that could backfire.
Ultimately, companies should approach hyperautomation as a cross-departmental initiative, involving all divisions to align with actual business needs. While startups can experiment more freely, larger enterprises must establish structured oversight to avoid costly errors.
Remember, hyperautomation is not solely a technological endeavor — it’s about cultivating an adaptable approach to business processes. Those who succeed in harnessing it effectively will gain a competitive edge. While inevitable, without the right strategy, hyperautomation can create more challenges than it addresses.
