What intelligent workload balancing means for RPA

This article explains the definition of intelligent workload balancing and what it means to operate robotic process automation (RPA).

How can intelligent workload balancing be achieved in this area?

The relatively new concept of intelligent workload balancing is important in running RPA as it determines whether tasks are more appropriate for human employees or their digital colleagues.

With this in mind, five industry experts identify specific ways this can be applied to this area.

Manage rules and transactions

First, intelligent workload balancing can be used to check whether bots can comply with the rules set by the company.

“The ability to automatically determine whether an activity requires human intervention or can be done by a bot is commonly referred to as ‘smart workload balancing,'” said Sathya Srinivasan, vice president of solution consulting (partner) at Appian. “The information comes from the business rules that determine who is the best candidate to do the job – human or bot. If a human, which department, group, skill level or management is best suited for this case, and if a Bot, what it takes to turn a bot on, how flexible can a bot be to different types of requirements.

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“To be really effective, a bot has to be able to work with a variety of parameters. For example, suppose a rule states that a bot will complete the job on returned goods less than $ 100 in value. However, during peak times with high returns, the rules can dynamically change the threshold to a higher number. The bot should still be able to take all the necessary steps for that approval amount without having to reconfigure each time. “

Gopal Ramasubramanian, Senior Director, Intelligent Automation & Technology at Cognizant added, “When 100,000 transactions need to be executed and instead of manually assigning transactions to different robots, the intelligent workload balancing feature of the RPA platform automatically distributes the 100,000 transactions across different robots and make sure transactions are completed as quickly as possible.

“If a service level agreement (SLA) is tied to the completion of these transactions and the robots cannot meet the SLA, intelligent workload balancing can also hire additional robots if necessary to distribute the workload and ensure that a specific task is performed is done on time. “

Process Intelligence Solutions

Neil Murphy, Global Vice President at ABBYY, explained how Process Intelligence solutions can be integrated to get a better view of what areas need to be optimized.

“In RPA, repeating a large number of processes can be challenging when the processes are interrupted or not fully understood – as it leads to frequent human intervention,” Murphy said. “So there is definitely a reason to apply intelligent workload balancing to RPA.

“This is why Process Intelligence solutions were created. This helps organizations better identify which processes can best be optimized for RPA and ensure they fully understand a process by identifying bottlenecks that can cause errors or increase lead times.

Process Intelligence also reveals the most common ways of executing processes, reveals incorrect process variations and uncovered other hidden inefficiencies in a company’s processes. A growing practice in companies that want true digital transformation rather than just automating manual processes is to combine process intelligence with RPA. This ensures the best results. “

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Bot licensing

Chris Porter, CEO of NexBotix, expands the use of intelligent workload balancing to optimize operations and talks about the importance of considering bot licensing.

“Traditional RPA vendors have mostly focused on bot licensing – selling licenses per bot,” Porter said. “Every bot is a fixed resource that can only process a certain amount of work. Once this is full you will need to buy another bot. Another problem with bot licensing is that bots are effectively scheduled manually. That’s a big hassle when someone is sitting there planning the work and managing your license pool. Every business should aim to minimize expenses and maximize automation.

“With intelligent workload balancing, the available resources are checked and then work is dynamically assigned to them. This effectively maximizes your resource usage by automatically assigning work to different bots or different servers depending on what type of work you are doing. It doesn’t have to be RPA – it can be machine learning or OCR and distribute these tasks automatically.

“When this becomes really important, you have an increase in the volume or seasonality of your workforce and automatically need to assign more bots to work or turn on more bots. For example, the insurance industry typically sees an increased workload in January when there is high renewal rates and an increase in processes.

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“With traditional RPA, you have to buy for the peak – you have to make sure that those licenses are available for the peak. For the rest of the year, those licenses sit idle – you’re still paying for them because the traditional licensing model means you need to have them available. “

Digital workers

A final example of intelligent workload management in action within RPA is digital workforce.

Peter Walker, CTO EMEA at Blue Prism said, “Unlike any other robot, digital workers work proactively by weaving AI functions together to work together effortlessly in ever-changing digital environments – without errors. Digital employees can optimally plan the workflow and workload execution in order to achieve the best results for the immediate and intelligent management of workloads, automatically scale other digital employees according to business conditions and analyze business processes with the help of automatic process mining.

“Digital employees can solve logic, business and system problems without intervention in order to guarantee the highest level of service with the help of automatic problem detection and to increase productivity in all process automation. To reduce customer care time and improve overall quality, digital employees can communicate and complete tasks with people, systems, and other digital employees.

“For example, chatbots can be used to work with digital employees who serve customers autonomously and, if necessary, forward actions to people.”

April 18, 2021