Although hypersystemization is not that popular in enterprises, it has shifted from processing only automation to an interconnected intelligent ecosystem powered by AI, machine learning (ML) and robotic process automation (RPA). Does it inspire businesses to implement these solutions? most likely.
According to Gartner, nearly one-third of businesses will automate more than half of their business by 2026 – a leap from 10% in 2023. But while hypersystemization is expected to revolutionize the industry and the number of people embracing it will grow, unfortunately many organizations are still working hard to scale up their effective size. Less than 20% of companies master the overautomation of their processes.
So, in this article, let’s explore why overautomated first, the main challenges of its implementation, and how businesses can do things that can prevent while avoiding common pitfalls.
From basic automation to intelligent systems
It is clear from itself that hypersystems take automation to the next level by combining AI, ML, RPA and other technologies. It allows enterprises to automate complex tasks, analyze large amounts of data and make decisions in real time. Thus, while traditional automation focuses on a single task, hypersystemization creates systems that are constantly learning and improving.
As mentioned earlier, there aren’t so many businesses that have put it together, probably because they don’t really understand the necessity – they need to be overautomated to stay competitive in the world on digital. how? In fact, the list is long: it reduces cost, improves efficiency, minimizes mistakes in repetitive tasks, simplifies operations, helps comply with regulations and enhances customer experience.
But, as we have seen from Gartner’s forecasts, nearly a third of businesses will automate more than half of their operations by 2026, a shift that shows that companies need not only automate tasks — systems that they need to analyze, learn and adjust in real time.
For example, enterprises are using intelligent automation (IA) to improve decision making. This involves integrating generative AI (Genai) with an automation platform through which companies can reduce manual labor and increase efficiency. Airbus SE and Equinix, Inc. Companies such as this have successfully implemented AI-based financial overdoses that can significantly reduce workload and speed up processes.
As the amount of data grows, real-time decision-making becomes essential, and hypersystemization plays a key role in business success.
The challenge of executing super-system
Although the idea of full automation sounds appealing, its actual adoption level is still low. Aside from the inability to define the goals of overautomation, lack of resources and resistance to change can be a huge bottleneck. In addition to this, the complexity of integrating new technologies with existing systems and the need to invest heavily in training staff poses significant challenges. Given these hurdles, most companies still rely heavily on manual processes and outdated operational workflows.
Unfortunately, the obstacles are not over. Another important reason few organizations can effectively implement automation is due to poor data culture. Without structured data policies and recordable processes, it is difficult for enterprises to map their workflows accurately, which leads to inefficiency and automation alone cannot be solved. The lack of a strong data governance program can also lead to data quality issues, making it difficult to ensure that automated systems operate in order to drive meaningful changes.
There is also the fact that its teams usually run separately from other business infrastructures, and the gap between perspectives makes automation difficult to execute. Climbing this gap requires a strong impetus, whether they are external consultants or internal team members who believe in automation and have the personal benefit that makes it possible. For example, an employee can be paid (or bonus, at least bonus) associated with measurable results, in which case drive automation is directly associated with higher efficiency and economic compensation.
A clear deadline and success metrics are also critical, as there is no defined timeline, automation can stagnate and fail to achieve meaningful results. Even if the initial implementation is successful, the automation needs to be continuously maintained. Software updates are often very frequent and you have to keep up with them to ensure that the AI model you are using remains in the correct integration with the system.
In this regard, I recommend you minimize the number of software vendors on which your company’s products depend. The more platforms there are, the more difficult it is to oversee all these interconnected products. Overautomation works better in companies with direct operations and clear protocols, and their automation systems can be updated and maintained.
The Supernatural Future: Startups Lead the Way
Super system is most effective for companies with clean slate. Built businesses, while often stranded by traditional systems, have the advantage of large budgets and can hire large teams, which allows them to meet challenges in ways that smaller companies cannot match due to limited funds. That’s why I believe startups that are building everything from scratch will increasingly push overautomation to reduce operating costs.
However, for both camps, it is important to pay attention to customer responses. If automation can have a negative impact on the customer experience (whether due to poor implementation or lack of demand, it’s something worth considering. Currently, customers doubt AI chatbots, answers to automation, and many other things modern customer service can offer. As a result, forcing automation doesn’t require risks to cause more harm than good.
Finally, I suggest that companies should view overautomation as a cross-department initiative involving all departments to ensure optimal conditions with actual business needs. In smaller startups, experiments have more latitudes, but for larger firms, this means building structured oversight to prevent expensive mistakes.
It is important to remember that hypersystems are not only related to technology, but also to create adaptive business process approaches, and successful approaches will have important advantages for competitors. Overautomation is inevitable, but without the right strategy, it can generate more problems than it solves.
