The first rule of any technology used in a business is that automation applied to an efficient operation will magnify the efficiency. The second is that automation applied to an inefficient operation will magnify the inefficiency.
– Bill Gates
Automation of processes has been in the news for almost three decades. A recent report by technavio projects that the business process outsourcing market is set to grow by USD 40.16 billion from 2020 to 2027, with a 4% CAGR in growth momentum.
Process automation has been a long-standing goal across industries and has been in continuous demand since its inception. Automation promises to deliver greater efficiency, decrease manual dependencies and errors, and significantly improve the scalability of processes.
While automation is being adopted across industries, automation projects often fail to deliver the intended results, leading to an increase in inefficiencies or meagre returns. Why? The answer lies in the approach towards adopting automation and the factors that directly impact the success of the projects being overlooked. Let’s look at these critical factors that can make or break an automation project.
Why Automation Projects Fail to Deliver?
When we look at why many automation projects fail, we see different factors working simultaneously. The most prominent mistakes made by businesses opting for automation or tech-led transformation are:
Underestimating the manual work involved in the process
When a process is specified as manual, it is often tagged as inefficient and costly. However, there might be several reasons why it continues to be manually operated. It might be due to technological constraints, the overall process, how it is organized, and the need for flexibility with short turnaround times. When automation is used for such processes, these reasons are not mapped, and automating without considering all factors of the process will lead to disconnects with ops, delivery, and clients.
Missing or unclear objectives of automation
Organizations often think of automation as a magic wand that converts processes and reduces costs. However, in many cases, process complexity, criticality, and maturity play critical roles in delaying the overall development and deployment. It is very important to have clear objectives and end goals drawn prior to the automation projects. This will act as a north star when changes and deviations from the initial scope occur.
Data everywhere, but leading nowhere
Inefficient processes don’t look at available data to drive process efficiency. It is important to identify key data points that are critical to and/or generated by processes and map them to data sets to make insightful business decisions. This helps automation projects focus on delivering input to output while directing the processes toward effective reporting and continuous improvement at scale.
Not considering underlying risks
When a process is not well documented, along with its associated risks, a failure can have a cascading effect, which may result in a ticking bomb. Hence, it is crucial to make sure that process steps are not just mapped from the actions that need to be automated; understanding the impact on the business and the risks connected with it are also critical when developing solutions.
Balance of cost and value delivered
Every solution built for a business problem has to justify the need for a solution. Even the best solutions often cause dissatisfaction among the users and/or customers since the problems are minimized but not eliminated. To avoid such issues, the business cases have to be analyzed from the perspective of cost savings and revenue impact, as well as the long-term value they can bring to the organization and its customers. There should be enough room for the elimination of a business process and a higher focus on customer satisfaction than just focusing on monetary values assigned to them.
Quality in testing
Quality assurance can result in costly errors and setbacks during automation implementation. Skipping rigorous testing can lead to unforeseen issues that disrupt operations and damage customer trust. Organizations should invest in comprehensive testing processes to avoid such pitfalls and ensure that automation solutions are thoroughly validated before deployment.
Changes to processes and people
After the automation and transformation exercises reach a certain benchmark, the business may need a different skill set than the ones acquired before the solution implementation. These have to be mapped and re-trained accordingly. Also, the change management processes may require upstream and downstream changes to accommodate the automation. The changes have to be planned in the beginning and executed in parallel while implementing the solution to make the project successful.
SLK’s Recommendations
SLK Software recommends different methods to uplift the processes and shift their focus to leading indicators, like customer satisfaction, from lagging indicators, like SLAs etc. Each process has its own fabric, which must be studied before automating them.
Our Digital Operations team comes with relevant business expertise and experience in implementing transformation solutions. We make required changes to the processes prior to implementing the transformational approach. This approach works on the following fundamentals:
- Standardize: Define process objectives and document each step of the process. Implement process improvements driven by Lean, Six Sigma, Poka-Yoke, and Kaizen. Identify key data points critical to the process performance and collect insights from these processes.
- Digitize: Processes using manual media like images, paper, and calls are analyzed and digitized appropriately. The intermediate steps of the processes are subjected to process improvement using simpler automation wherever possible. The domain expertise will be enhanced for all users so they can contribute to the process improvement. The critical process data is collected from various systems to derive meaningful insights. Reporting performance is system-driven and will be monitored in real-time. Non-value-adding aspects in the process are reviewed and eliminated accordingly. The processes in this stage can be easily mapped using SLK’s Task Capture tool, which helps in determining areas within processes that can be automated using RPA or IPA. The OCR/ICR enabled with NLP helps in automating image-extensive processes.
- Transform: Once the process is standardized and automated, the customer realizes the cost savings. The domain expertise team will look further into enhancing the process with the center of focus on outcomes that satisfy client needs. The process involves reviewing and transforming end-to-end processes using data, elevating people with technical and domain knowledge, and making system changes to manage compliance, predictiveness, and resilience.
SLK’s Digital Operations team will review the process and work with you before automating the processes and suggesting the best approaches. We will partner with our customers for all our transformational approaches to ensure our customers get the best of the solutions.
Conclusion
Process automation may look like a lucrative opportunity to derive business value, and it is!
However, the complexities around implementing it should be taken into account before, during, and after the project to extract the maximum benefit from it. Some of these are important enough to result in the failure of your automation project and increased inefficiencies thereafter.
Automation has the potential to transform how you operate and give you competitive advantages. Businesses looking to implement automation should simplify and optimize their processes to a certain degree before implementing it. And most importantly, automation is just the beginning of this transformation. Continuous analysis and improvement is the real key to transformation!
