The current era of innovation and rapid development of smarter technology has been referred to as the “Fourth Industrial Revolution,” initially by Klaus Schwab of the World Economic Forum. As with the previous industrial revolutions, we have already seen significant disruption to the workforce and the way businesses and governments of various sizes conduct operations. Although the pace of change has been slower in some areas than others, the role of project manager will not be immune from the changes brought on by the integration of this newer, smarter technology. Here we will explore the anticipated changes to the project management role in general, as well as how those changes will likely impact the people assigned to that role for government projects in the near future.
Introducing intelligent automation
Intelligent automation is the latest term to describe the combination of various technological advances to increase the decision-making capability of machines to achieve business objectives. These advances often fall into the categories of artificial intelligence (AI), machine learning (ML), and robotic process automation (RPA). The business objectives achieved by their application are usually associated with increased efficiency by reducing manual labor steps. In an environment of deliberate process orchestration, these tools are aligned to gather and prepare data, make decisions based on that data, and take action as needed. This is a workflow that typically involves several interactions by multiple people from various departments, but that a computer system with the proper access to information and analytical capacity can execute in a matter of seconds. This allows an organization to increase its output, minimize time to value, increase accuracy and decrease overall costs.
Robotics and automation are no longer limited to the manufacturing industry. In his book, The Robots are Coming! (or ¡Salvase Quien Pueda! in the original Spanish), Andrés Oppenheimer explores how automation is already affecting several industries that had previously been the exclusive domain of highly educated professionals and how that disruption is likely to progress in the next few years. This includes the practice of law, accounting, education, and even medicine. In the case of medicine, for example, wearable technology that can measure specific biological data like heartrate, temperature, etc., with increasing accuracy already exists. That data could be transmitted to a computer using existing wireless technology that could then make a diagnosis in an instant based on the patient’s history as compared to thousands of other cases and the latest published research. This system would not replace doctors, but if an initial diagnosis can be made without the patient leaving their home and without the direct involvement of a human doctor, that would greatly reduce the amount of routine medical appointments that occasionally are not available for days or weeks. In turn, doctors can instead focus on providing treatment and fulfilling their primary role as a trusted medical consultant.
How could this change project management?
Looking at current tasks performed by the typical project manager, in theory, many of their responsibilities could be handled by an AI program with access to relevant information regarding the project personnel, current capabilities, known restrictions and monitoring tools.
Planning: Planning a project is a function of scheduling personnel with the needed expertise and resources to deliver a specified outcome within a defined timeframe. In a matrix organization, this is typically done by coordinating between various departments via meetings, scheduling tools and other means of communication. The project manager produces a project plan that includes a schedule with noted dependencies and a list of identified risks, opportunities and assumptions, if any were made. Depending on the scope and complexity of the project and the number of stakeholders involved, this process could take between a few days to a few months. Using predictive analysis and ML, an AI program could identify the best person for each task and determine the amount of time it would take each individual to complete the task based on a number of factors, including past performance and previously assigned tasks. This same type of analysis could be used to determine risks and opportunities based on known factors entered into the system, although this would still likely require some human input and review since even the smartest computer programs can’t comprehend the full context and impact of these factors. Even with this limitation, the overall coordination needed and time to produce a full project plan and project schedule would be significantly reduced.
Monitoring and Controlling: Once the project is in the execution phase, the project manager monitors progress against the schedule, budget and scope to determine the current health of the project, if any risks have turned into issues that will impact progress, and if any assumptions have been validated or invalidated. There are various applications available that collect information from individual contributors and present the overall progress on the project in a dashboard format or reports as needed. An additional AI component could take this information a few steps further and predict the project health leading up to key milestones on the schedule, if current trends continue. An analysis of time charged against the project on timesheets and other related expenses can also automatically generate reports and determine how the project is doing and expected to do against its budget. As communication is critical throughout this phase, the automated system could also send out notifications in real time to the relevant stakeholders, if the inputs it is expecting in accordance with the project plan are not detected as scheduled.
Closing: At the end of the project, the final reports and documentation are submitted to the stakeholders and the project team conducts a final review. An AI program could collect and analyze all inputs from throughout the project lifecycle to generate a final report that illustrates how well the project was able to achieve the desired outcomes. This report could include a detailed analysis of how each contributor did against the plan, unknown risks or issues that came up during execution, suggested process improvements to be applied to future projects, and any lessons learned by the system itself which would be validated with the project team and relevant stakeholders. A significant advantage to a consolidated AI program is that it could function as its own knowledge management ecosystem that can store and apply lessons learned and process improvements as it learns in real time.
How could this impact government projects?
Whether or not a government agency has a dedicated project management office is usually determined by budgetary restrictions. Smaller agencies that decide to make a significant system change often assign one of their staff to the role of project manager on a temporary basis or contract a third-party consultant as needed. In either case, using intelligent automation to calculate, analyze and report on the progress of the project would greatly reduce the number of manual tasks that all involved project managers would have to complete while increasing accurate, real-time visibility into the status of the project. Making the relevant information available to the public, as determined by the public agency’s governing body, would also increase public trust and buy-in on the project outcomes. Another benefit would be more time for agency staff to dedicate to their primary roles during the project lifecycle and allow vendor project managers to dedicate more time to vendor-to-agency communications and building human relationships based on the attainment of the common goals of the project.
The focus of the machines would be on the calculations and impact analysis, which they can do better than humans. The focus of people would be on what is typically referred to as the “soft skills” of human-to-human communication, assigning priorities to outcomes based on the alignment of overall objectives and making sure stakeholder concerns are considered when evaluating project changes. Machines are less capable of detecting and understanding the various nuances associated with project objectives and the impact on the various stakeholders, including the public. In this way, the projects of the future in an intelligent automation environment will be more symbiotic and allow the human project managers at all levels to focus on how the results of their projects affect the people in the communities they serve.