The growing adoption of AI in project management software is transforming the skill set needed for PMs in the UK. Project manager coach Peter Taylor offers his insights on how companies can prepare the project managers to effectively work with AI.
In this article
- 1. Review your tech stack to pinpoint underused AI features and determine the need for advanced tools
- 2. Design tailored training sessions to enhance critical hard and soft skills
- 3. Develop strategies to secure data and prevent misuse of AI tools
- Empowering your project managers with AI expertise is essential
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 the United Kingdom (UK)* found that 45% PMs in the UK are already using AI-enabled project management tools and 42% of them expect their company 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 leader Peter Taylor. [1]
Taylor has years of experience leading project management offices (PMOs), and regularly coaches organisations in the UK and elsewhere on how to improve their processes.
1. Review your tech stack to pinpoint underused AI features and determine the need for advanced 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.
In Taylor’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 underutilised 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 optimised 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 optimised workflows for organising tasks and activities, so project managers can handle multiple projects with varying deadlines and tasks.
2. Design tailored training sessions to enhance critical 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.”
The fact that most project managers feel the use of emotional intelligence has increased over the last two years already points in that direction. More than half of the surveyed project managers in the UK (52%) 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 (45%), communicating needs or expectations (36%) and identifying emotions (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 personalised 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. Develop strategies to secure data and prevent misuse of AI tools
“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.”
The Capterra survey findings confirm the conclusion of Taylor, as 38% of project managers in the UK say their company has experienced data quality issues and 20% had unwanted bias in the outputs. It might be one of the reasons almost half (49%) of project managers are sceptical about the use of AI.
The question is how companies can create right policies, guardrails and training to minimise 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. [2] 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 [3] 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.
Analysing 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.
Empowering your project managers with AI expertise is essential
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. 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.
Sources:
- Peter Taylor, LinkedIn
- Gartner, When Not to Use Generative AI
- Gartner, 7 Data Quality Focus Areas to Ensure Effective Analytics and AI
Survey methodology:
Capterra’s 2024 Impactful Project Management Tools Survey was conducted online in May 2024 among 2,500 respondents worldwide and 200 in the United Kingdom.
The goal of the study was to understand the leadership and emotional intelligence skills needed for PMs to successfully lead teams and projects leveraging/incorporating AI. Respondents were screened to be project management professionals at organisations of all sizes. Their organisation must currently use project management software.