3 pro insights to prepare Canadian companies for AI in project management

The increasing use of AI in project management software is changing the role and the required skills of project managers. Project management pros Peter Taylor and Hussain Bandukwala share their insights on how companies in Canada should prepare their PMs to work with AI.

It’s time to upgrade your use of artificial intelligence (AI) to manage projects if you want to keep pace with your peers. A new Capterra study of 2,500 project managers worldwide and 200 in Canada* found that 39% PMs in Canada are already using AI-enabled project management tools and one in three (33%) expect to increase those AI investments by at least 30% by the end of the year. 

If you’re on board with the idea of more AI in your company’s PM processes but concerned about the execution, start with the steps below from thought leaders Peter Taylor andHussain Bandukwala. [2]

Taylor has years of experience leading project management offices (PMOs), and regularly coaches organizations on how to improve their processes, and Bandukwala helps organizations set up and optimize their PMOs, and coaches PMO leaders. 

1. Evaluate your tech stack to identify underused AI features or the need for new tools

“AI has the potential to become a true digital partner of the project manager, freeing up their time, so they can spend more of it managing the project team,” says Taylor. 

Bandukwala adds: “AI is taking over a lot of the administrative tasks, so PMs should start focusing on things AI can’t do, such as building relationships, setting expectations, and analyzing the outcomes of projects.” 

In Taylor’s and Bandukwala’s view, businesses should prepare themselves and their project managers to fully take advantage of the possibilities of AI-enabled technology and software. 

They’ll have to take an inventory of their current project management software and find out how their project managers are currently using the tools. Companies can audit their software stack in few simple steps:

• Create a comprehensive inventory of all project management tools and their AI capabilities. Assess each tool’s purpose, usage frequency, and user satisfaction. 
• Gather feedback from the project managers. This will help you understand which features and tools are essential and which may be underutilized or redundant.
• Review the security and compliance of all tools. This way you ensure they meet your company’s standards. 
• Assess the level of training and support for each tool. This will help you identify training gaps and arrange additional training sessions or resources.

Based on the audit companies and the feedback of the project managers companies can make informed decisions about how to best use AI-driven software, and what new investments they may need to make.

Part of that decision depends on the personal wishes and needs of the companies and their project managers, but if the audit was done well it will show that there are some areas where AI has added value. 

“AI works best if it’s used as a kind of co-pilot or digital assistant,” explains Taylor. These are some examples of features you can find in AI-enabled project management software:

Leverage AI to create an optimized project plan, including tasks, subtasks, and resource allocation.The tool matches the project needs with the right team members automatically. This is based on team members’ skills and performance in past projects.

An AI enabled digital whiteboard for planning and team collaboration.It can be used as a central hub , where to capture tasks with sticky notes and share ideas, and feedback. The tool identifies key themes and next steps.

An AI workflow generator suggests optimized workflows for organizing tasks and activities, so project managers can handle multiple projects with varying deadlines and tasks.

2. Develop training programs focussed on essential hard and soft skills

“To support their project managers in using AI, companies first need to understand where there is a knowledge gap,” explains Taylor. “The second step is to support them with training. It doesn’t need to be a 2-day course. There is a movement towards so-called ‘micro-learning,’ where you offer knowledge in small bits of a couple of minutes at the moment that the user needs it.”

Part of the training will have to be dedicated to developing the right soft skills, explains Bandukwala: ”Companies need to focus on training their PMs on the people’s side of their job because that’s the part where they need to thrive.”

The fact that most project managers feel the use of emotional intelligence has increased over the last two years already points in that direction. Almost half of the surveyed project managers (49%) think it has a significant impact on the team’s ability to achieve its goals.

In the Capterra survey, project managers are asked what soft skills they struggle with. The most commonly mentioned are resolving conflicts (37%), influencing others (30%), and self regulation (30%).

Luckily, there are some effective methods business leaders can use to train their project managers:

Role-playing and simulation exercises thatallow project managers to experience real-world situations and experiment with different approaches to handling conflicts, negotiating and fostering positive relationships.

Interactive workshops that include activities such as group discussions, case studies and interactive exercises to teach strategies for active listening, understanding different personality types, managing difficult conversations and building a collaborative team environment. 

Mentoring and coaching programs that provide project managers with ongoing support and personalized guidance by pairing them with seasoned mentors in the company who can help to address specific challenges, set goals for improvement and track progress. 

The increasing use of AI also requires entirely new hard skills, such as data analyses, data management and how to get the most out of the AI-enabled project management tools. 

Learning management software (LMS) can be a great help for companies that want to create, manage, deliver, and track training content and activities. It allows companies to build tailor made learning paths for the specific knowledge and skills project managers need, whether it’s understanding how AI algorithms work or how to troubleshoot issues. 

Through the software companies can offer access to webinars, video lectures and articles from industry experts, and it often comes with discussion boards and forums where project managers can share insights, discuss challenges and collaborate on specific issues. PMs can access and engage with educational content or training course materials at their own pace and schedule.

3. Create safeguards to protect against data misuse in AI applications

“In all companies that I’ve worked with, the quality of the data that was generated by AI was an issue,” says Peter Taylor. “There is a famous saying: when you put in garbage, garbage will also come out. With AI it will go in and come out faster, so you’ll need to have a knowledge management process in place to ensure the data that comes out is reliable.” 

Part of that process should be specific tool training. “They should train PMs on how to spot bad data and give courses around biases and data security,” says Bandukwala.

The Capterra survey findings confirm the conclusion of Taylor and Bandukwala, as 37% of project managers in Canada say their company has experienced data quality issues, and 24% had unwanted bias in the outputs. It might be one of the reasons more than half (55%) of project managers are skeptical about the use of AI. 

Canada's Personal Information Protection and Electronic Documents Act (PIPEDA) is designed to prevent misuse of data, as it sets the ground rules for how companies collect, use, and disclose personal information.

“With that in mind, companies need to have data quality standards in place, and create policies for when and how data should be validated,” explains Bandukwala.

The question is how companies can create right policies, guardrails and training to minimize the risk of project managers taking actions based on the wrong data. 

The first step is to understand the advantages and the limitations of AI-enabled tools. For example, according to Gartner, GenAI is highly useful for content generation and knowledge discovery, but hardly useful for prediction/forecasting and planning. [3] Companies should select the tools based on the goals they want to achieve.

The second step is to create the right data management process. Gartner recommends business leader to implement a number of best practices [4] to ensure constant data quality, such as: 

• Creating the right mindset, which includes acknowledging that change management is needed to maintain an acceptable level of data quality.
• Embed responsibility for data quality across multiple roles, and don’t just delegate it to the IT-department.
• Leverage technology to integrate data sources, and to measure and report data quality provided by operational sources.
• Consistently map the impact of data quality on business outcomes, performance, and financial impacts.

Larger companies that want to operationalize data risk management can leverage risk management software, which can be integrated into project management software. There are several ways AI can be used for risk management, such as:

Monitoring the quality of the data that is being fed into the AI tools. By setting predefined data quality metrics, the software can automatically generate alerts when quality falls below acceptable levels. The software can maintain detailed audit trails of all data-related activities, including data collection, processing and usage. 

Running what-if scenarios, by changing project variables such as due dates, budget, and resource allocations to see the impact they could have. With the help of Generative AI project managers can ideate various risks they might encounter in a project, and discuss those with their key stakeholders.

Analyzing large volumes of unstructured data, such as emails, meeting notes, and progress reports, with the help of natural language processing (NLP). NLP tools enable computers to understand and communicate with human language. You can use them to scan through project-related communications to identify warning signs of potential risks.

Equipping your project managers with AI skills is key

The capabilities of AI-tools are constantly evolving, as is the skills set that is required to work with those tools. Companies will have to invest in the knowledge and skill set of their project manager, to make sure they can keep up with new technological developments. 

Peter Taylor recommends companies offer “micro learning sessions” to develop their soft and hard skills, and, in addition, Bandukwala emphasizes the need of simulating specific situations. 

Where the focus with soft skills should be on developing their EQ, the focus with the hard skills should be on making them data-savvy, to recognize poor data quality, deal with data quantity and make more informed, data-driven decisions.

Your next step is to work with your company’s leaders to align on which of the insights of Peter Taylor you’ll apply and when. Set yourself up for success by identifying in advance where knowledge gaps of your project managers are, and which actions you can take to address them.