For AI to overtake 80 percent of project management functions, the industry will be dependent on software providers adding new AI functionality into existing program & project management (PPM) tools. These new features will proliferate in the coming years and will continuously evolve over time. Yet today, PPM vendor leaders are in an embryonic stage as it relates to AI-enabled PPM.
Taking a look at the Leaders in Gartner’s Magic Quadrant for Project and Portfolio Management software providers shows how few AI, Machine Learning (ML) or Robotic Process Automation (RPA) tools are currently available from the major software suppliers. At this time, none of these four vendors offer any fully developed AI features within their current product; however, all four have tools under development.
There are some challengers to this space that are developing new, narrow focused tools that integrate with organizations’ existing tool suites. One example of a tool that has already hit the marketplace for example is Stratejos. Stratejos integrates with Jira and Slack and will automatically make updates to Jira tickets from Slack conversations thus saving a project manager and the team time. Another category of tools provides collaboration capabilities which provide direct stakeholder involvement in project decision-making. This includes: Asana, Workfront, Monday, Memo, and Advanced Management Insight.
The current tools available, and the ones soon on the way, will largely be leveraging descriptive analytics. All these tools will rely on human data input and interaction to help tell us what has already occurred in a project or will be able to automate basic data management tasks. These tools act as great “smart” assistants for PMs and their teams but are still very dependent on human management and interaction.
As time progresses, the next step for software vendors will be to develop tools that can offer more predictive analytics support. By tracking project data over time, these tools will evolve to help project managers make difficult decisions around scheduling, estimates, risk mitigation, etc. They will be able to offer deep insights into team performance, estimate quality, etc. and help aid in modeling different project change management scenarios.
The final step in the evolution will be when these tools can offer prescriptive analytics support. The tools will have enough historical data to begin to operate with incomplete future data, with the AI adding its own project predictions through machine learning processes. Eventually the project management tools can automatically reschedule project tasks due to a project issue, which was listed by a developer in a chat window, based on a complete statistical analysis of the best possible schedule outcome.
How to Get Started Using AI for PPM
The use of AI to improve project management will be implemented through innovative project management tool features. Through AI, organizations can manage projects and programs more efficiently, making the PMO an even stronger value generator and enabler of organizational strategy. The benefits of these features are currently focused on reducing administrative tasks through automation, auditing project management artifacts against best practice standards, and leveraging enhanced collaboration capabilities to better manage risks. To unlock the promise of AI for PPM, the key is to start exploring these tools with the small steps that your organization can take today.
To achieve the greatest value from AI in PPM, having guidance on navigating the early stages of adoption is key. Regardless of whether your approach is Agile, Waterfall, or Hybrid, identify, prioritize, and implement your AI efforts based on opportunities for greatest efficiency. Based on specific business needs, you can enlist partners for support with building custom tools in existing processes or use custom off-the-shelf AI tools as well.
We’ve outlined a few key fundamental principles in getting started:
- Test features based on methodology. For Agile projects, test the features in the Stratejos tool which assist with estimates, budget, and sprint management in Jira. For Waterfall projects, test the project artifact auditing features of Steelray tool to compare your plans to best practices.
- Focus on collaboration capabilities. For both types of projects, examine the team collaboration capabilities of the Asana, Workfront, Monday, Memo, and Advanced Management Insight tools to determine the benefits from broader sharing of project activities and increased stakeholder participation in project decision making.
- Double down on data. To prepare for the future of AI for project management, cleanse your task and resource management data. Machine learning will provide the ability for tools to provide advice on estimating in the future, but this will only be as good as the data which is provided. A focus today on good knowledge management, capturing the right data, accurately will form a basis for predictive analytics that help estimate schedules and resources needs.
By applying the power of AI to industry proven best practices within PMO, organizations can achieve greater business value while developing PMO tools tailored to organizational culture, strategy, and needs.
Click here to read part one in the series.