In the past, the term “digital worker” described a human employee with digital skills, but more recently, the market has defined it as a category of software robots, which are trained to perform specific tasks or processes in partnership with their human colleagues.
More specifically, Forrester offers the following definition for digital worker automation: It is “a combination of [intelligent automation] IA building blocks, such as conversational intelligence and [robotic process automation] RPA, that work alongside employees. They understand human intent, respond to questions, and take action on the human's behalf, leaving humans with control, authority, and an enhanced experience.”
IBM Automation observes digital workers similarly, defining them as software-based labor that can independently execute meaningful parts of complex, end-to-end processes using a range of skills. They leverage artificial intelligence capabilities, like machine learning, computer vision, and natural language processing, to execute a sequence of tasks within a given workflow. For example, a digital accounts payable worker may be able to autonomously perform parts of three traditional job roles — customer service representative, billing agent and cash applicator or dispute resolver — to complete an Order to Cash (OTC) process. Since digital workers increases the bandwidth of their employees, they have largely been adopted through digital transformation efforts, allowing companies to reallocate their workforce to more strategic tasks.
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In order to initiate your digital workforce, you should consider the human element. Enterprise design thinking can help you determine how you want humans to interact with digital workers to enable the intelligent workflow. From there, you’ll want to go through the following steps:
- Assess the need: The team should identify a process for a digital worker to own. During this stage, you may want to start more simply, such as incorporating intelligent data capture or basic business rules, to enable better decision-making, adding complexity gradually.
- Document the process: Detailed documentation of the process should exist to train new digital workers correctly.
- Train digital workers: Once a documented process has been identified to transition over to a digital worker, they are trained to handle the tasks within the selected workflow. Bots are also instructed to identify and flag exceptions for their human counterparts, routing more complex use cases to them and freeing them from mundane monitoring activities.
- Assess performance: During this step, teams can assess the performance of a given digital worker, ensuring it has generated an appropriate return on investment (ROI) for the business. By utilizing process mining and process maps, teams can validate training efforts, but they can also use this opportunity to identify bottlenecks, further optimizing the process.
The IBM Automation Services team has put these steps into practice, supporting multiple clients in our business process outsourcing practice. Below is an example of a digital worker that they created.
Meet Ocash
Ocash is a digital cash application specialist: the latest recruit for the finance and accounting function. It’s often helpful to consider and position your digital workers in the roles that they would fill within your enterprise’s operations.
To create Ocash, the IBM services team began with the workflow outcome in mind. They sourced data from ERP systems to deconstruct the OTC process and identify its component parts. From there, the team focused on one of the more manual aspects of the process. They automated the tasks where they could and augmented those that still required human workers. Ocash was designed to execute tasks that were best suited for automation, calling upon its human colleague(s) only when needed. They also took this opportunity to optimize the process. For example, automating the verification of invoice accuracy with a customer at the start of the process can significantly eliminate the number of payment disputes later on.
While digital workers can extend beyond digital tasks, they have primarily been utilized for support work across a variety of business functions. Some examples include:
- Supply Chain: Retailers, such as Amazon, are leveraging robots to assist them in checking stock quantities and pricing.
- Human resources: Bots can answer benefits questions in real-time, collect employee data and route complex tasks to subject matter experts, evolving the employee experience within companies.
- Sales and product support: Bots can answer basic product questions to help customer success and sales teams manage prospective and existing clients. They can also route requests by level of urgency, so high-priority tickets are addressed in a timely manner, improving the overall customer experience.
While digital workers provide process efficiency gains, there can be challenges in implementing a digital workforce. Some of these barriers to success include:
- Not enough tasks to automate, making it difficult to justify the cost of a digital worker.
- Technology may struggle with some unstructured data sources.
- Gathering information for documentation purposes can be costly and resource intensive.
- Scaling can be difficult if digital workers are not designed to be configurable and adaptable.
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The IBM Institute for Business Value, in collaboration with Oxford Economics, surveyed 1,500 executives from around the world as part of a comprehensive study on the impacts of intelligent automation initiatives today and in the near future.