In the past few years, project management has been in the throes of a revolution. Iteration cycles are shorter, project teams are more dispersed, and the pressure to deliver results is ever greater. At the same time, emerging technologies are creating new opportunities for project management organizations (PMOs) to manage this changing work, according to a recent study by ProjectManagement.com.
The automation and data visualization provided by state-of-the-art project management systems give managers the tools they need to make informed decisions. But what if many of those decisions could be made by the software itself?
That’s the promise of machine learning—a promise that will change the face of project teams, management, and projects themselves sooner than you may think.
Today’s project management systems typically rely on manual input, such as timesheets and status updates, to inform a manager’s decisions around task assignment and resource allocation. More sophisticated systems, such as Oracle Project Portfolio Management Cloud, can pull additional data from other parts of the enterprise, such as finance, and present it with advanced visualizations.
A few years from now, however, AI and machine learning may be making these decisions independently and objectively, without human input. Software robots (commonly referred to as “bots”) could build project plans, assign tasks, and allocate resources. These bots would be able to learn from any number of inputs within and outside of the organization to refine these decisions, resulting in ever-greater efficiency and speed.
That does not mean we’ll do without project managers altogether. Instead, with bots making these tactical decisions, human project managers can take on a more strategic focus, keeping the organization’s project portfolio aligned to its strategic priorities. The project manager would also act as a coach to the team, monitoring broad trends, identifying unforeseen opportunities, and bringing innovative thinking to bear on the problems at hand.
Project teams certainly aren’t what they used to be. In place of rigid, hierarchical structures, nimble self-organized teams are creating a dynamic project delivery environment. Team members may be spread across the country or the globe, collaborating via social tools and focusing on individual technical specialties rather than more general expertise.
Bots are a natural fit for the increasingly pervasive work-at-will model, whether it be a pool of outside contractors, internal ad-hoc teams, or a combination of both. The system would be analogous to a ride-share service, in which potential team members make themselves available when (and where) they wish, and the system assigns tasks based on availability, expertise, and other criteria. The AI system would then ensure that these disparate elements come together as a cohesive whole, on time and on budget. When roadblocks arise, the management bot, much like a GPS system, could find alternate routes or report the issue to a human operator for resolution.
As bots become more sophisticated, the nature of projects themselves is likely to evolve alongside them. In fact, the foundational elements of this future work model are already in place. The influence of Agile has driven a more iterative approach; organizations are shortening planning windows and increasing the cadence of deliveries to meet demand.
The result is a level of complexity and speed that might be difficult for a human to track—but for which a bot is perfectly suited. Intelligent automation could reduce the lag between identifying a need and implementing the solution. At the same time, it would maximize beneﬁts and value. Large-scale, potentially disruptive packaged projects would give way to smaller, incremental deliveries that realize the promise of continuous improvement.
Machine learning requires high-quality data—lots of it. This is why project management bots will rely on connected ERP cloud systems to exchange information with every functional area of the enterprise. Finance, in particular, could benefit immensely from this next-generation automation. Bots would keep projects within budget and ROI parameters, while also delivering information about cost and revenue impact to the finance function in real time.
In addition to the structured data provided by ERP system, bots could draw on big data from the Internet of Things (IoT), data as a service, and other unstructured sources to learn new ways of optimizing project cost and projected ROI. On-premises infrastructure and software simply don’t provide enough readily-available data to train the machines. The future of AI and machine learning is in the cloud.
Where all of this will take us is still an open question. To ensure that your organization is future-ready, choose solutions that already offer predictive analytics and intelligent automation. Oracle, for example, has embedded machine learning into its finance, human resources, supply chain, and customer experience cloud applications.