Journey of Business Process Transformation(1) — Hyperautomation
Enterprises always want to transform their processes into more efficient, agile, and cost-saving ones. Yet, there is no best process, only a better one. Before, we evolved from manual paperwork and digitalization to automation.
What is the next step? Definitely, Hyperautomation.
“Hyperautomation involves the orchestrated use of multiple technologies, tools or platforms, including artificial intelligence (AI), machine learning, event-driven software architecture, robotic process automation (RPA), business process management (BPM) and intelligent business process management suites (iBPMS), integration platform as a service (iPaaS), low-code/no-code tools, packaged software, and other types of decision, process and task automation tools.” quoted from Gartner Glossary
Hyperautomation has been chosen by Gartner as one of the Top 10 Strategic Technology Trends for 2020, 2021, and 2022 consecutively. In 2023, it is still an emerging trend.
To sum up, there are two main key points of Hyperautomation differentiating from automation.
- AI Algorithms
- End-to-end platforms
Below we use Cloud Pak for Business Automation, an end-to-end portfolio to traverse a whole hyperautomation journey.
The steps are flexible instead of fixed. Depending on what obstacles companies face, the direct solutions are different and execution steps are varied. For example, some companies’ priorities are to remove process silos and need an integration and collaboration platform between teams to unify developing environments. Business Automation Workflow would be the first choice. In some application scenarios in which manual work is way involved and can be automated straightforwardly by running scripts, RPA demonstrates its benefits of operation efficiency immediately.
However, what if we haven’t recognized the impediments of processes, jumping into automation of tasks without second thoughts will cause more problems in the future.
For example, this article summarized five main reasons for failed automation projects.
- Deploying automation in the dark
2. Not testing before implementation
3. Automating tasks rather than entire processes
4. Failing to iterate
5. Lack of skills to scale automation
I would say the transparency of processes is the first precondition and identifying the bottlenecks of processes instead of tasks comes next.
Leveraging methodologies of processes to systematically analyze processes and prioritize and applying Process Mining to build consensus between teams via process transparency is the best first step from scratch.
After pain points are recognized, we start to design automation tasks and test and implement them. The last step is iteration by monitoring our automation tasks and going back to the first step which is applying APQC methodologies and Process Mining to form the optimization iteration loop for building capabilities of scale-out to other processes.
In the next chapters, I will introduce APQC methodologies and Process Mining in detail.
