AI is actively penetrating multiple industries and workflows, improving their efficiency. In this article, we’ll take a look at how AI resource management software changes the traditional resource management approaches, and go over potential challenges of its implementation.
How AI Enhances Resource Management Software
Traditional resource management tools do enough to help organizations balance resource loads and optimize productivity. AI resource management software greatly improves the quality of insights gathered through these tools and simplifies their use. Here are the four main avenues through which AI can do this.
Predictive analytics
One of the areas where AI excels is analyzing historical data, finding common patterns, extrapolating them, and using them to create projections about future performance. Artificial intelligence and machine learning are more efficient in forecasting than simpler algorithms because they can account for more factors and weight them automatically based on historical performance data.
In resource management tools, AI can greatly improve predicting:
- Operational risks based on project scheduling and resource capacity.
- Future resource load based on planned project and resource availability.
- Project delivery based on current capacity and performance.
- Actual project margins based on budget usage.
- Portfolio performance based on different project composition scenarios.
AI can bring greater accuracy and reduce the need for advanced knowledge in creating prediction models.
Skill-based resource allocation
Matching projects with the resources with an adequate skill level can be a major challenge. Higher management often lacks visibility into the resource pool, especially if the organization has a large headcount. Middle management might not have the possibility to request talent that’s best suited for their project, either because they’re not aware of them due to siloed department structures, or because there’s a conflict for resources between teams.
Basic resource management tools can solve that problem to a degree by providing a list of resources and their skills. AI resource management software brings a strategic dimension to skill-based resource allocation. It can:
- Adjust skill level based on historical performance data.
- Find hidden talent within the organization.
- Match resources to projects automatically based on their skill level and capacity.
These features can both save time and improve decision-making quality.
Automated resource allocation
One of the hardest things to do in resource management is make decisions on which projects to give resources to and which ones to postpone. With traditional resource allocation tools, the typical workflow for making those types of decisions is:
- Analyze project resource requirements.
- Analyze project business value based on a variety of metrics.
- Simulate dozens of portfolio scenarios to find the optimal one.
Assessment of project resource requirements and determining business value still largely depends on resource managers or higher management because the criteria used for that differ greatly from one organization to another. What AI helps immensely with is the decision-making process based on those two inputs.
Some AI resource management tools are able to analyze the portfolio based on an organization’s resource constraints and project priority, and return a portfolio scenario that provides the maximum business value per work hour. This feature can potentially save dozens of hours of running scenario analytics manually and prevent making suboptimal decisions.
Insight prompts
AI can be effectively used to discover insights in areas that resource managers are interested in, allocate resources based on skill levels, and automate the core functions of resource management. Some AI-based tools go even further than that.
Instead of waiting for the manager to run a specific analytical task, AI-driven resource management software can analyze all the data in the background, and highlight areas that might require improvement.
A tool that has this capability can:
- Highlight projects that are at risk based on budget utilization rate.
- Send custom alerts to the main dashboard.
- Highlight potential productivity gaps in relevant dashboards.
- Suggest potential solutions.
These features increase the speed with which resource managers learn about improvement potential in their line of work and decrease the need for manual analytics.
AI-based virtual assistants
Most of the benefits AI brings into resource management software are based around superior analytics capabilities. AI also excels at generating human-like helpful text. Some AI resource management software can combine the two strong sides of this technology to create a new way of interacting with the tool.
If a tool has an AI-driven virtual assistant, the user doesn’t have to navigate through the dashboard to run an analysis or learn a specific detail about the portfolio. They can simply ask the AI instead, and it will provide an answer based on the data available to the tool and its analytical capabilities.
Some AI systems like that also have agentic capabilities. These allow the user to perform actions with the help of AI, for instance, to tell it to update a task sheet with new information, to rearrange projects, or to notify stakeholders of the changes.
How Resource Management Tools Lift Challenges of AI Implementation
Most industry leaders believe that AI adoption is necessary, but in fact, not very many actually invest in the technology. That’s due to several challenges inherent to AI implementation on the level of the organization.
If that was one of your concerns about adopting AI, you need to know that there’s a vast difference between building a custom AI solution for your company and adopting an AI-based resource management tool. Let’s go through several core challenges and take a look at how using an AI already integrated with a resource management tool lifts them.
- Cost of implementation. Building a custom AI solution is expensive. Resource management tools like Epicflow deal with the AI infrastructure on their end and typically don’t upcharge for using AI capabilities.
- Lack of tech skills. Custom AI implementation has a high tech skill ceiling. Adopting an AI-based tool doesn’t require any tech talent. Most software providers will help integrate their tool into your existing tech stack.
- Security concerns. AI does have some security risks that should be addressed when implementing it. Software providers that sell AI-based tools deal with them on their end, and your company doesn’t have to invest in that process.
Overall, adopting AI built into an already existing tool is generally devoid of typical drawbacks associated with AI implementation while providing just as much value to the organization that adopts it.
Conclusion
AI resource management software comes with little to no downsides and can greatly improve your project portfolio management workflows in:
- Resource utilization forecasting.
- Skill-based resource allocation.
- Value-based resource allocation.
- Proactive analytics.
Agentic AI assistants also make the whole job of a resource manager smoother and faster. Considering the only challenge you’ll face when adopting a tool like that is its learning curve, all organizations dealing with resource management should look into upgrading to an AI-based tool.
