Jan 26, 2025
Companies are increasingly embracing the idea of helping nontechnical staff members — those who have deep business-area expertise — learn to directly automate processes that give them headaches and eat up their time. For instance, human resources employees are uniquely qualified to identify the mundane and repetitive parts of their jobs, such as candidate-tracking tasks, and then, with some training, build automation that will relieve them of chores such as duplicative data entry and data cleaning.
While the development of such applications by so-called citizens within organizations requires careful planning and governance to be effective, low-code and no-code technologies have become commonplace and made such ventures possible.1 Specifically, robotic process automation (RPA) and a broader intelligent automation (IA) suite that allows for the redesign and automation of workflows are now straightforward enough that functional experts can design, develop, and deploy IT applications and analytical models themselves. No longer do all projects require mediation by IT employees, who might not fully understand end users’ pain points. These tools of citizen-led automation are allowing less technical people to build complex systems that improve their work experience, and they are already generating considerable value for many businesses.
In this article, we draw on interviews with six companies — AT&T, Dentsu, Johnson & Johnson (J&J), PwC, Voya Financial, and Wesco — to describe their efforts to join the citizen automation movement. We also detail how other organizations can best develop these capabilities and the benefits and challenges of doing so.
At its core, encouraging non-IT professionals to participate in designing their own work tools is not new. Enterprises have long tapped into teams across their businesses for process improvement ideas. Six Sigma belt wearers, for instance, have been trained in improving small processes. What’s new is that today’s citizens can actually sketch out and then run the future state they were once only able to describe to IT development teams.
Harnessing citizenry is partly necessary because there are simply not enough people with the professional IT skills needed to accomplish the torrent of digital initiatives on companies’ agendas. Even conservative estimates project a dramatic shortage of tech workers by 2030.2 Tasks such as moving information between transactional systems, updating spreadsheets, and even composing standard-format emails are ripe for automation, but that often doesn’t happen because people with the skills to do that work aren’t available.
At accounting consultancy PwC, the citizen automation effort arose out of an initiative to train employees who would be known as “digital accelerators” to help grow the business without adding a proportional number of staff members.3 Data, automation, and AI were identified as the three pillars of the initiative. Employees who volunteered to learn new skills and technologies, and were selected for the program were asked to focus on one of the three pillars. Participants were given time off from their regular client service jobs to pursue a variety of upskilling options. Employees who focused on automation were trained in the tools (including RPA, data prep and blending, and simple machine learning models) and in Six Sigma process improvement methods and were asked to identify processes that would benefit from automation. One audit-focused digital accelerator created an automation to scope needed tasks on a client audit by automatically extracting and aggregating data from many different spreadsheets. It saved 40 hours from the audit engagement and was adopted as a standard tool for auditors to employ with other clients.
Of course, a primary driver of the rise in citizen-led automation is the corresponding dramatic rise in the relative simplicity of some automation programming.
The citizen automation movement is enabled by the rapid evolution and democratization of automation tools. Compared with other forms of artificial intelligence, RPA and IA tend to be easier to implement and less expensive. RPA is being widely adopted to access data from multiple systems and to automate structured, information-intensive tasks, such as routing incoming customer emails or updating order status in a transaction system. When combined with IA tools such as machine learning and character recognition, they can also make data-driven decisions and extract important information from documents such as handwritten customer forms or key provisions in a contract.
There are several technology options. First are standard RPA tools from vendors such as UiPath, Blue Prism, and Automation Anywhere. These can be complex to learn and use — not because they require coding but because they may need to be integrated with transaction systems. With proper training, many nontech employees can build simple automations with them. Second is technology that is specifically developed for citizens and thus involves little or no coding. Some mainstream RPA software comes in simpler versions intended for citizen automation. Microsoft, for instance, has made easy-to-use RPA capabilities available as part of its Office productivity suite.
Most of the organizations we’ve spoken with in our research offer citizen automation training programs that are between 40 and 80 hours long, and many supplement their programs with instruction provided by the leading automation tool vendors. Training programs can also include hackathons in which trainees apply their skills to quickly build RPA applications.
Because RPA systems typically link to and extract data from existing transactional systems, citizen automators often need to have an awareness of corporate IT architectures in order to safely access and use data. However, if citizen-developed RPA applications are certified by IT professionals and address any issues around integration with other systems, this knowledge might not be necessary. Some organizations we’ve studied have established automation centers of excellence (COEs) that handle all such integrations and compliance, allowing citizens to focus on applying automation to the processes they understand without requiring them to become familiar with the complexities of the underlying architectures.4
The recent appearance of generative AI on the enterprise scene is already beginning to make RPA design and implementation easier. Since OpenAI’s ChatGPT was announced in late 2022, for example, several RPA vendors have announced interfaces between their RPA systems and the language capabilities ChatGPT offers. Before long, it should be very easy for a user to specify the desired attributes of the automation system in virtually any natural language and have a working prototype of the system automatically produced. The generative AI system should also be able to automatically create an easily understood description of the workflow and decision rules, if prompted to do so.
