According to a survey from Deloitte, nearly eighty percent of CFOs say their companies will "embed more automation/digital transformation into operations."
But while automation is a great way to increase efficiency and reduce costs, as well as to eliminate issues caused by human error, there are some common automation problems that businesses tend to encounter when they begin to automate their workflows.
Here are five top automation challenges your business is likely to face, and some best practices that will help you to overcome them.
If you rush into automating a business process without really understanding how it runs, or ensuring it’s running optimally, you won’t get the results you expect. Automation could actually amplify inefficiencies in the process. But even if it doesn’t, you’re unlikely to get the financial or productivity gains you need.
What’s more, processes rarely run in isolation. They usually interconnect with other processes, running across different different departments or functions. By automating a process without understanding the wider implications, you risk unwelcome consequences elsewhere in the business.
Two key technologies can help you overcome this automation challenge:
Process mining extracts data from your business systems to gain an accurate visualization of business processes in all their variations. This is an ideal foundation for planning business automations with confidence.
Object-centric process mining moves from a two-dimensional view of processes to a three-dimensional and dynamic view of your entire business, meaning you can see how processes intersect and impact different functions.
So far, most businesses are automating simple, repetitive tasks within a single process. Basic automation tools like Robotic Process Automation (RPA) can be highly effective at undertaking these specific tasks, increasing efficiency and cutting costs at a local or departmental level.
Tasks commonly completed by RPA include:
Logging into applications
Filling in forms
Matching POs to invoices
Compiling routine reports
But this type of automation is very difficult to scale across multiple processes, business functions, or markets, because RPA bots can’t think beyond the boundaries of their specific task or adapt to system changes. To scale automation, bots need an additional layer of intelligence to tell them what to do, how to do it, and when to do it.
AI-powered intelligence, from technologies such as process mining, can provide a level of situational awareness that enables automation to be applied more widely and effectively across your business. By taking data, processing it, and determining what needs to be done, intelligent automation can drive real business value, at scale.
Automation doesn’t necessarily mean eliminating human intervention altogether. The extent to which people need to remain in the loop will vary by use case, but the ability to flag or escalate a problem to a real person will almost always be required.
The truth is bots and humans can work together and complement one another. Take a customer service chatbot for example. It may need to escalate a query to a human customer service representative, but it can still support that person with the relevant data and insights they need to resolve the customer’s problem.
In fact, when we consider intelligent automation, bots can learn from human experience. Initially an AI-powered bot may ask a real person to make a decision when it detects a certain situation or issue. The bot can use machine learning to learn from the person’s responses and, over time, can take over more of the decision making process, taking action based on what the person would have done in a similar situation.
While cutting labor costs and increasing efficiency is often an automation objective, the aim shouldn’t necessarily be to remove humans from the process altogether.
Enterprise automation tools can be difficult and time consuming for businesses to configure and use. Users often have to learn a complex new interface, and the range of features and options available can be confusing, leading to setup errors.
What’s more, the average automation tool is difficult to integrate with existing systems. A lack of legacy system compatibility and data silos mean automations aren’t necessarily using the most accurate and up-to-date data, or enabling the best outcomes.
With the right tools, however, you can create and quickly deploy effective automations. With the Celonis platform, for instance, you can make the most of over 10,000 pre-built automations across more than 1,000 applications for common use cases. Our simple, low-code interface is easy for anyone to use, allowing you to automate processes in seconds, and API-led automation makes it easy to scale across your business.
One of the biggest mistakes businesses make when automating processes is setting up automations and leaving them to run without sufficient testing, transparency, or traceability. To avoid this situation, there are three steps you should take when automating processes:
Automation testing: thorough testing of any automation will help you to make sure you are achieving the results you expect, and not unintentionally automating sub-optimal processes or inefficiencies.
Automation auditing: Continually auditing automated processes in real time is essential to make sure automations continue to run as intended. Automation discovery and impact analysis will allow you to debug processes and handle any errors.
Automation tracking: By tracking the impact of your automation efforts you can understand the value they are generating. You should be able to instantly pull results to share with anyone who needs to see them.
Process automation is a great way to increase efficiency and productivity, but it needs continual monitoring to ensure it is operating as expected.