Knowledge work requires the ability to learn, analyze and apply intelligence – traits that only people used to have. Now, advancements in automation, cognition and Artificial Intelligence (AI) allow organizations to augment their workforce with virtual knowledge workers (VKWs). Performing complex tasks that previously required a person, these automated programs have been deployed across several industries with great success.
Digitizing a business process requires a continuum of interventions:
- LEAN: Making the process more efficient by removing errors and redundancy
- RAPID: Automating the process by removing manual steps/intervention
- SMART: Applying analytics to make the process more efficient
- LEARN: Incorporating machine learning to make the process adapt
- VIRTUAL: Transforming to a digital, adaptive process that can run without human intervention
Over the years, technology has made knowledge workers more productive. Robotic Process Automation (RPA), as example, can now eliminate many of the repetitive tasks that consumed the time of a knowledge worker, allowing them to focus on more important responsibilities while making a business process more RAPID. However advancements in machine learning, cognitive computing and AI allows companies to further augment business operations with VKWs, software programs that can learn, adapt and make decisions.
The Emergence of Virtual Knoweldge Workers
The role and scope of the virtual knowledge worker is constantly evolving. At the moment, VKWs in the workplace augments rather than replaces human capabilities. This provides people with more time to focus on crucial valuecreating activities. Because they follow a defined set of automatic responses, virtual knowledge workers make fewer mistakes and are faster than humans. These digital assistants mimic human conversations by assessing the context of what a user is saying and responding appropriately.
This can create a superior customer experience by allowing for seamless interaction between VKWs and customers across multiple channels including phone, messaging, mobile and web.
One area where VKWs can take some of the burden off of human employees is using natural language to converse with the customer. VKWs can answer simple help desk questions via chat, allowing the support user to interact and deal with more complex tasks.
Altering and improving VKWs often requires no coding; the bots are trained to mimic human actions while interacting with existing application. Over time, processes become virtualized simply by training the bots.
Real-life Applications of Virtual Knowledge Workers
Virtual knowledge workers are more prevalent than you would expect. There are several interesting use cases across different industry segments
An unlikely area where there are several examples is the legal field.
A VKW named ROSS assists various teams in a US firm with legal research. Another VKW called DoNotPay has successfully helped void 160,000 parking tickets across London and New York in less than two years –for free. The bot asks a set of questions to calculate whether a ticket can actually be appealed. After determining that an appeal is viable, the bot walks the user through the detailed, specific steps to contest the ticket
These VKW lawyers can even work out precise compensation for delayed flights, or assist vulnerable groups like those who are HIV positive and refugees in foreign countries by presenting tips to navigate complicated legal systems. AI lawyers are expected to soon start drafting their own documents, building arguments and comparing and contrasting past cases. However, arguing cases in the courtroom is still the domain of human lawyers, no matter how well they’re assisted by machines.
The vast amount of data and variables in healthcare make it an extremely attractive area for virtual knowledge workers. One high-profile example is the Google Deepmind Health Project, a VKW used to mine medical record data in order to provide better and faster health services. VKWs from other companies are assisting in clinical decisions in areas such as radiology and cardiology by analyzing images to quickly detect problems that humans may miss. Another application offers consultations based on a user’s personal medical history. A person can report the symptoms of their illness to the app, which checks them against a database of diseases using speech recognition. After examining the patient’s history and circumstances, the app suggests appropriate courses of action. It also reminds patients to take their medication and follows up to find out how they’re feeling.
Some firms have begun using VKWs to retrieve and examine invoices containing digital data regarding treatments, providers and hospitals. By analyzing this information, the VKW can tell if healthcare providers repeatedly mistreat certain conditions. They can also direct patients to providers with a better success rate for their condition.
A VKW has been designed to help small and medium-sized enterprises digitize and automate accounting and financial processes. Users submit their receipts to the bot, which turns them into a machinereadable format, encrypts them and allocates them to an account. The platform gradually self–learns while tracking invoices, sales, costs, and their liquidity.
Legally, the term “financial advisor” refers to any entity offering advice about securities.
Robo-advisors are a class of VKW financial adviser that provides financial advice or portfolio management online without human intervention.
Most robo-advisors are restricted to allocating investments among asset classes using the same algorithms as their human counterparts, freeing up these employees to focus on financial planning and cash-flow management.
Robo-advisors directly managed about $19 billion as of December 2014, according to a study done from Corporate Insight, while another study predicted this to grow to $255 billion in total assets by 2020.
VKWs can be deployed to research, aggregate, refine and present information to underwriters, thereby allowing them to focus on their core underwriting activities.
Forward-thinking insurers are deploying VKWs already. These include companies using VKWs to develop a range of underwriting solutions to achieve accurate pricing. Others rely on VKWs to rate, bind and issue policies over the phone. Another virtual insurance agent can generate a car insurance quote if a customer simply texts a photo of their license plate.
Recommendation for Getting Started
Virtual knowledge workers represent a holistic approach to the digital transformation of a process. This includes mapping the customer touch points and moments of truth with a design thinking approach, architecting a flexible data infrastructure that delivers context, and incorporating cognitive automation and machine learning into the business process.
However, once on this path, companies will need to build the right framework for a successful VKW program. These basic requirements include:
Companies should first identify where a virtual knowledge worker would generate the greatest return and then pilot and test solutions to understand capabilities and limitations. This includes assessing the types of work the bot will perform, and then building the business case for integrating VKWs into the process.
Program management and governance are crucial as well. Organizations may need to redesign tasks, jobs, management practices, and performance goals when they begin implementing VKWs.
Carefully charting a plan for implementing VKWs can transform a company’s operations. VKWs can provide personalized interactions with customers in the front office while automating complex tasks to free up human talent for high-value work in the back office. Organizations that combine advanced capabilities in RPA, AI and a solid VKW framework will be well positioned to take advantage of these virtual workers.