What intelligent workload balancing means for RPA

The relatively new concept of intelligent workload balancing is an important one to consider when operating RPA, because it determines whether tasks are more suitable for human employees or their digital colleagues.

With this in mind, five industry experts identify particular ways in which this can be applied to this space.

Managing rules and transactions

Firstly, intelligent workload balancing can be used to check that bots can adhere to rules set up by the company.

“The ability to automatically decide if an activity requires human intervention or if it can be performed by a bot is usually called ‘intelligent workload balancing’,” said Sathya Srinivasan, vice-president, solutions consulting (Partners) at Appian. “The intelligence comes from the business rules that determine who is the best candidate to complete the work – human or bot. If human, which department, group, experience level or management is best to handle this case, and if bot, what does it take to bring a bot in, how flexible can a bot cater to different types of requests.

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“To be truly effective, a bot must be able to work across a wide set of parameters. Let’s say, for example, a rule involves a bot to complete work for goods returned that are less than $100 in value, but during peak times when returns are high, the rules may dynamically change the threshold to a higher number. The bot should still be able to perform all the necessary steps for that amount of approval without having to be reconfigured every time.”

Gopal Ramasubramanian, senior director, intelligent automation & technology at Cognizant, added: “If there are 100,000 transactions that need to be performed and instead of manually assigning transactions to different robots, the intelligent workload balancing feature of the RPA platform will automatically distribute the 100,000 transactions across different robots and ensure transactions are completed as soon as possible.

“If a service level agreement (SLA) is tied to the completion of these transactions and the robots will not be able to meet the SLA, intelligent workload balancing can also commission additional robots on demand to distribute the workload and ensure any given task is completed on time.”

Process intelligence solutions

Neil Murphy, global vice-president at ABBYY, explained how process intelligence solutions can be incorporated in order to get a better of perspective of which areas need to be optimised.

“In RPA, repeating a high volume number of processes can be challenging if the processes are broken or not fully understood – as this leads to frequent human intervention,” said Murphy. “As such, there is definitely a case to apply intelligent workload balancing to RPA.

“This is why we have seen process intelligence solutions emerging. This helps businesses better identify which processes are best to optimise for RPA, and ensures they fully understand a process by detecting bottlenecks that might be causing errors or increasing lead times.

“Process intelligence will also reveal the most frequent paths of executing processes, expose broken process variations, and uncover other hidden inefficiencies within an organisation’s processes. A growing practice within organisations wanting true digital transformation – rather than just automating manual processes – is to combine process intelligence with RPA. This secures the best results.”

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Bot licensing

Chris Porter, CEO of NexBotix, expands on the use of intelligent workload balancing to optimise operations, talking about the importance of taking bot licensing into account.

“Traditional RPA vendors predominantly focused on bot licencing – selling licences on a per bot basis,” said Porter. “Each bot is a fixed resource that can only process a certain amount of work, and once that is full, you need to go and buy another bot. Another problem with bot licencing is that bots are effectively scheduled manually; that’s a big overhead to have somebody sat there, scheduling work and managing your licencing pool, and any organisation should aim to minimise spend and maximise automation.

“Intelligent workload balancing is taking a look at the resources you have available, and then allocating work to them in a dynamic way. Effectively what this does is it maximises your resource utilisation by automatically allocating work to different bots, or different servers depending on what kind of work it is that you’re doing. It doesn’t have to be RPA – it could be machine learning or OCR, and automatically distributing those tasks.

“When this becomes really important is where you have an increase in volumes or seasonality in your workforce and you need to automatically allocate work to more bots or switch on more bots. For example, the insurance sector typically sees increased workloads in January when there are high levels of renewals and a spike in processes.

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“With traditional RPA you have to buy for the peak – you need to make sure you have those licences available for the peak. The rest of the year those licences are sat around idle – you’re still paying for them because the traditional licence model means you have to have those available.”

Digital workers

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

Peter Walker, CTO EMEA at Blue Prism, explained: “Unlike any other robot, digital workers proactively work by interweaving AI capabilities to inter-operate effortlessly across ever-changing digital environments – without fail. Digital workers can optimally plan workflow and workload execution to deliver the best outcomes to instantly and intelligently manage workloads, auto-scale other digital workers as needed by business conditions and use automatic process mining to analyse business processes.

“Digital workers can solve logic, business and system problems without intervention to use automatic problem detection to ensure the highest levels of service to increase productivity throughout all process automations. To reduce time to service customers and improve overall quality, digital workers can communicate and complete tasks with people, systems and other digital workers.

“For example, chatbots can be deployed to work with digital workers autonomously servicing customers, and when needed, escalating actions to people.”