Three key ways AI-powered software is improving project management

52% of the 200 project managers we surveyed say increased productivity and efficiency is a top benefit of artificial intelligence for them. We cover how they’re using it, from predictive analytics to scheduling optimisation.

Project managers (PMs) are seeing significant boosts to productivity and efficiency with artificial intelligence (AI), but it's easy to underestimate the complexity and run into problems. Our data shows that leveraging AI for scheduling optimisation, task automation, and predictive analytics shows the most promise for early return on investment (ROI).

In Capterra’s Most Impactful Project Management Tools survey*, we surveyed 2,500 project managers (PMs) globally who are currently using AI, including 200 Canadian respondents. We’ll examine the adoption, use cases, and benefits of AI-driven project management tools among the Canadian-based project managers.

AI investments driven by positive ROI and PM confidence

The majority of project managers report success in their current AI investments, with 87% reporting a positive ROI delivered in the last 12 months. This high level of success is likely encouraging increased confidence in the technology, as these project managers expect their businesses to increase AI investments by an average of 28% by 2025, slightly lower than the global average of 36%.

As AI adoption continues, so will the benefits. Today, 52% of PMs report feeling that increased productivity and efficiency is a top benefit of AI in project management. These boosts can come from automation and task delegation as AI can be “assigned” tasks typically performed by the PM.

Comfort in delegating important tasks to AI is high, with 74% of PMs feeling confident to do so. This delegation not only improves productivity and efficiency, but also allows PMs to focus on more strategic aspects of project management, such as decision-making and stakeholder management. This blend of confidence, comfort, and awareness is driving momentum for further investment in AI, highlighting its role as an important tool in modern project management.

However, skepticism persists…

The majority (88%) of surveyed Canadian PMs feel confident in their ability to lead AI-implementation projects. But with just over half expressing skepticism toward AI, there’s indication that a considerable portion might underestimate the complexities and limitations associated with AI. Particularly the black-box nature of the inner workings. 

Unless your business has created a custom AI-driven tool from scratch, the reality is you don’t know the limitations of the tool you’re using. The current, popular vendors in the AI space are not providing the design details for their models. From the source and type of training data used to the ethical guardrails in place, the current tools are a black box and should be managed as high risk with strict cybersecurity protocols and policies in place.

The missteps of AI-powered tools such as generative AI and chatbots hit the news regularly, from giving legally disastrous [1] information to potentially harmful advice. [2] PMs need training to increase their understanding of AI's rate of hallucinations, propensity for biases, and data dependencies in order to help protect the business from costly mistakes. This education is crucial to help them effectively oversee AI implementation projects, make informed decisions, and manage the inherent risks. 

Top use cases of AI in PM: Predictive analytics, scheduling optimisation, and task automation

Current AI capabilities are pretty effective at providing PMs with deeper insights with more accurate forecasts, enhanced scheduling capabilities, and automating repetitive tasks. So it makes sense we see these as the top current use cases of AI by Canadian project managers. Together, these AI-driven improvements can lead to more efficient project management and increased project success rates, ultimately helping drive business growth and competitiveness.

Let’s take a look at examples of what these top three use cases can look like. 

1. Predictive analytics

AI-driven predictive analytics can help you design an effective project plan and identify potential risks before they happen. These tools can forecast potential timeline and budget overruns by analysing historical data and finding trends and patterns. This foresight helps you plan more accurately, allocate resources better, and ultimately, execute projects with fewer disruptions.

• For example, construction project managers can use AI to predict the likelihood of price increases in key materials based on market trends and historical data. The system could receive information on a rate increase on materials, such as steel, and notify the PM, allowing them to plan for contingencies and adjust budgets accordingly.

2. Scheduling optimisation

AI can analyse multiple variables such as team availability, task dependencies, and project deadlines to create the most efficient project timelines. This optimisation is especially effective for larger businesses with a complex network of internal and external resources and constraints. An AI-powered scheduling tool not only ensures that all resources are used effectively, but can also help minimise downtime and avoid scheduling conflicts.

• For example, a project management office (PMO) can leverage AI to identify the best people and timeline for a new project based on the historical performance of team members, upcoming work schedules, and even competing priorities. A manual calculation like this would take multiple managers hours or even days to complete. And the odds of them missing important information needed to make the best schedule is greater than if the PMO uses an AI-driven scheduling tool to create the first draft.

3. Task automation

AI can handle repetitive tasks such as project status updates, task notifications, and report generation, which typically take the project team hours, even days. Automation allows the team to focus more on critical, high-value activities, thereby accelerating project timelines and improving overall output.

• For example, a marketing agency’s PM tool might automatically update the project status and task assignments based on data received by integrating the workflow management software and customer communication platforms. When a client reviews a deliverable and indicates their approval, the AI-enhanced PM tool will know to move the project forward.

A closer look at using AI for project management

It’s not just popular generative AI tools, such as ChatGPT, that are helping PMs. In fact, genAI and large language models (LLMs) are just one area of artificial intelligence. Let’s take a closer look at the ways project managers can use these tools and the various other types of AI in project management. 

Predict and mitigate scheduling risks: 42% of PMs report feeling that AI is already improving planning and scheduling capabilities and we expect this to grow considerably. Project managers can use AI tools to simulate various what-if scenarios—also known as risk modeling—by changing project variables such as due dates, budget, and resource allocations to see the impact they could have. 

• An AI-enhanced scheduling feature might evaluate the impact of a potential delay in a critical supplier's delivery by adjusting the project schedule accordingly and predicting downstream effects on overall project completion. 

Generative AI can be used to ideate various risks your project might see, help you prepare to discuss the risks with key stakeholders, and provide guidance on mitigation strategies.

Unstructured data analysis: Leverage natural language processing (NLP) to analyse large volumes of unstructured data, such as emails and meeting notes, for task creation, identifying potential risks, and generating more accurate status reports. In fact, 33% of PMs think generating status reports will be greatly impacted by AI in the next 12 months and NLP capabilities could be a key way to get there.

• NLP-powered software can identify tasks discussed in a meeting and then go on to create and assign that task for you.
• These tools can also scan through project-related communications to identify warning signs of potential risks, such as frequent mentions of "delays" or "resource shortages." 

Source multiple data points for analytics: 46% of PMs think predictive analytics is a top area AI will have the greatest impact on in the next 12 months. Especially when integrated with other systems, AI-enhanced analytics tools can use data from various sources, such as budget reports and team performance metrics, to generate more accurate forecasts and reports. 

• For example, AI could combine data from your project management software, enterprise resource planning (ERP), and market research tools to forecast potential budget overruns due to rising material costs. 
• Project timelines and the scheduling of tasks can be improved when AI has access to multiple data sources, such as your email system, time tracking, and/or risk management tools.

These use cases of AI can help you also reap the benefits reported by your peers. It could save you hours, perhaps days, of gathering information and analysing all the different possible outcomes on your own.

Rapid improvements in AI expected by project managers

As AI technologies continue to advance, PMs report expecting significant advancements in task automation, predictive analytics, and generating status reports in the next 12 months. If you’re planning to start or increase investment in AI for your project management, we suggest focusing on these three areas for now. These AI-enhanced features will help PMs to focus their time on strategic decision-making and creative problem-solving, anticipate and mitigate risks with greater precision.

AI can save you time and identify ways to optimise project resources, timelines, and outcomes. By preparing for these advancements and actively integrating AI into existing workflows, businesses can empower project managers to deliver projects more successfully.