January 2, 2024
Managing multiple projects simultaneously is a challenging responsibility. As a rule, multi-project environments have a shared pool of resources, which requires careful planning and management to ensure that they will be utilised efficiently and the projects will be delivered on time and within budget.
Artificial intelligence can transform multi-project resource management: it provides project and resource managers with data-driven insights, automates and streamlines various processes, and facilitates informed decision-making. Read the article to learn how AI-powered resource management software can optimise multi-project management and ensure the successful governance of both projects and resources.
A multi-project environment means that an organisation runs several projects simultaneously, and these projects have the same pool of human and material resources. Of course, juggling projects and their resources successfully is a demanding task, so let’s review the main challenges you may face.
Multiple projects usually have shared resources, e.g., employees with certain skills or materials/tools. This may lead to bottlenecks hampering the project's progress: the work cannot be completed, as there’s a lack of required resources. Also, resource constraints can lead to overwork, as project managers can assign more tasks to available people. Finally, resource constraints may limit a company’s ability to execute more projects.
Projects in a multi-project environment are interconnected. Most often, these dependencies refer to resources, both human and material (including shared infrastructure). Changes or delays in one project can impact the other initiatives. For example, if a team member fails to complete their task on time, it can hamper their teammates’ work. These delays will snowball and can cause a major halt to the work of the whole multi-project environment.
In a multi-project environment, you may face difficulties determining the right priorities between projects and tasks. First, there’s too much data to analyse. Second, priorities may change, and you cannot predict how these changes will affect the workflow and other projects in the environment. Third, it can be difficult for team members to manage tasks and determine which of them have the highest priority and should be completed first.
Working in a multi-project environment increases the risks of resource conflicts. This situation arises when multiple project managers vie for the same resources, potentially leading to decreased efficiency and delays. Additionally, in a multi-project environment, projects may have overlapping deadlines, necessitating careful prioritisation and allocation of resources.
Most difficulties are associated with managing resources; when done right, resource management is the key to successful multi-project management. Let’s now consider how AI can assist with managing resources in a multi-project landscape.
AI-powered resource management tools are becoming indispensable helpers for work in multi-project environments with shared resources. As noted earlier, they can process vast amounts of data, identify patterns, and make actionable recommendations. Here’s how AI enhances multi-project resource management.
AI can analyse historical data along with project schedules and resource availability to predict future resource requirements. This capability is especially critical for demand forecasting—predicting resource needs helps reduce bottlenecks and delays in the project flow. Also, you can use AI-powered scenario analysis to test different project changes and their impact on the other projects and the resource pool. This capability will help you make more effective decisions.
AI-driven software can monitor resource utilisation in real time. You can use these insights to optimise resource allocation depending on real-time project needs or priorities. In addition, AI helps you monitor resource performance and detect inefficiencies (e.g., underutilised or over utilised resources). Finally, you can use What-if Analysis to test different ways of responding to these issues: it will help you take the most reasonable actions.
AI resource management software can proactively identify resource conflicts before they turn into significant problems for the workflow. For example, AI can analyse schedules, dependencies, and workloads and detect areas of possible issues. Moreover, AI can suggest ways to address these conflicts, e.g., adjusting schedules, reallocating resources, moving the due dates, etc.
AI-driven RM tools leverage advanced analytics to support project managers in making data-driven decisions. For instance, it can help with task prioritisation and resource allocation by analysing project urgency, impact, and dependencies with other projects. Testing scenarios is also helpful for optimising decision-making and streamlining work on multiple projects.
Artificial intelligence comes in handy for identifying and mitigating risks when managing multiple projects and their resources. Therefore, you can use AI-powered RM solutions to predict potential delays, resource shortages, or bottlenecks. Using these predictions, you can prepare more accurate contingency plans and avoid the negative consequences of risk-bearing events.
“AI cannot predict the illness of a key employee or any external event, but it can provide you with enough flexibility by trying multiple scenarios beforehand to be ready to switch to another plan if needed.”
Jan Willem Tromp, a multi-project resource management expert and AI researcher.
An example of AI-powered resource management software is Epicflow, which was designed specifically for multi-project environments. Its functionality perfectly fits the needs of complex multi-project resource management. Here are its most powerful capabilities:
Complex resource management in a multi-project environment demands innovative solutions, and AI is emerging as a game-changer. It provides project/resource managers with predictive insights, enhances resource allocation, and optimises decision-making. As a result, AI facilitates greater efficiency of business organisations, reduces resource and schedule conflicts, and helps deliver projects on time and within budget. The role of AI in multi-project resource management is expected to grow, which in turn requires managers and team members to embrace digital skills. This comprehensive approach will make it possible to drive improved performance and gain a competitive advantage for organisations across industries.
The most common challenges you'll face involve managing a shared resource pool. These include resource constraints leading to bottlenecks, complex dependencies where a delay in one project affects others, difficulties in prioritising tasks across projects, and an increased risk of resource conflicts between teams.
AI-powered software assists by analysing past project data to predict future resource requirements accurately. This forecasting capability helps you plan ahead, avoid shortages, and ensure resources are available when needed. It also allows you to run 'what-if' scenarios to see how changes might impact your resource pool.
Yes, it can proactively identify them. AI resource management software analyses project schedules, team member workloads, and task dependencies to flag potential conflicts before they become serious problems. It can then suggest solutions, like adjusting schedules or reallocating resources to maintain a smooth workflow.
AI tools provide data-driven insights rather than relying on guesswork. They help you prioritise tasks based on urgency and impact, optimise resource allocation based on real-time data, and test different scenarios. This support helps you make more informed and effective decisions for your entire Project En portfolio.
The article mentions Epicflow as an example of AI-powered software designed for multi-project environments. Its features include automatic task prioritisation, AI-based resource allocation, workload prediction, and a 'What-if' analysis function to aid decision-making.
