As we head into the new year, many business and technology leaders are thinking about which technologies can help them accelerate innovation and digital business. We talked a lot about automation, data, and cloud in 2021, and many organizations are well down that path. However, it is important to consider how well those new technologies were implemented and what bottlenecks may be encountered.
Data is key to virtually all business processes, and the same applies to automation, which completely relies on data. Automated processes throw many “exceptions” or errors, which humans need to manage, and most of the time the reason for exceptions is bad data.
If bad data is supplied to the system, either the automation will throw an exception or it will flow incorrect data, in which case business managers may start blaming the automation. However, if you look at it closely, it’s the data that created the issue, not the automation. So, for every automated business process, you should supply clean data to achieve a higher success rate and maximum efficiency.
Automation follows the simple principle of “garbage in, garbage out,” so one must be cautious while providing input data.
Following are three key points businesses should focus on for higher efficiency and optimization with their automation initiatives.
1. Automation and data go hand in hand to deliver a stable bot with a minimal exception rate.
When forming a team, automation should not be considered a separate entity. The team should have clear visibility of data architecture, cloud, and other applications so the automation architecture can be formed accordingly.
We have experienced this situation in the past, where less visibility of application architecture and data architecture, and less coordination among teams, result in a higher percentage of business rule exceptions that need to be managed by humans. That creates a burden on the business to continue investing time in a process that is already automated. This leaves a negative impression within the business, and automation teams start to lose credibility.
So, it is vital for automation teams to coordinate with other teams in the department for quality delivery of the automated process and focus on delivering the stable bot with minimal business exceptions.
2. Innovation needs an implementation plan.
Every organization strives for innovation, and we have seen many form separate innovation cells. Yet, very few of the innovative ideas turn into a live or implemented process. Why? Many innovative ideas come through the pipeline and sound promising for solving a business problem, but they often lack an implementation plan and thus remain just an idea, even though it’s possible to implement it.
The most common reason for such stalled efforts is lack of coordination among teams and the teams are so busy with their day-to-day work that implementation comes last on their priority list. For example, maybe it doesn’t add value in their KPIs. As a result, many automation ideas are discarded and marked as red due to no implementation plan being attached to them.
To raise the success rate in bringing new ideas to fruition, teams should focus more on implementing these innovative ideas rather than keeping them in a backlog. Innovation and implementation should be added to the KPIs for each department to give them a boost.
In addition, CIOs should set these initiatives up in their priority lists so teams can organize brainstorming sessions to prepare the implementation plan and convert the ideas into a project. This can boost the number of successful automation projects within the organization and contribute to delivering value.
3. Focus on fully automated business processes.
Automation is a broad term, and there are a mix of technologies and technical buzz words that are often used when automating business processes. Some organizations use python for automation, some use external tools like UIPath, and some use low-code platforms based on an organization’s budget and specific requirements.
But in the end, all of them are used to automate business processes irrespective of the underlying technologies and the language associated with them. If we look at existing automation use cases that have been implemented, you will see that only certain modules of the business process are automated—humans still need to perform other modules manually.
However, a combination of technologies like AI/ML along with RPA—which we call as hyperautomation—can help in achieving fully automated processes.
Hyperautomation is not a technology by itself, but a combination of different capabilities such as AI/Ml that can help in delivering a fully automation solution.
Achieving End-to-End Automation Solutions
In conclusion, organizations that are on the path to automation should focus on implementing fully automated processes and form teams with diverse skillsets to achieve fully automated solutions. Too often we have seen that over-reliance on vendor tools may limit automation, as some tools don’t provide the full flexibility that’s required.
Alternatively, when diverse teams are formed, organizations can often achieve fully automated solutions. And the real benefits of automation are realized not with modular solutions, but when you deliver end-to-end automated solutions for the process.