How AI is turbocharging project risk management

If the last five years have taught us anything, it’s that projects are risky places to be. Unfortunately, that doesn’t look like changing anytime soon, with recent elections, stubborn interest rates and cybercrime continuing to have an impact on the world around us.

To understand and manage risks, historically project professionals have had to blend complex number crunching with pragmatism, taking calculated measures to keep projects moving forward while tip-toeing around the trapdoor of disaster.

In this series of articles, we’ve been exploring the rise of AI and its impact on the project profession – both now and in the future. For this instalment, we’ll focus on risk management and how leveraging AI can help project professionals better identify, analyse and respond to the risks in their project environment.

Let’s get into it.

1. Risk identification: looking back to look forward

In the early stages of most projects, teams get together to identify the risks ahead of them. This initial risk identification exercise often relies on the knowledge and experience of subject-matter experts, leaning on lessons learned.

In previous articles (here and here), we’ve explored how GenAI tools such as ChatGPT, Claude and Google Gemini can be used to spark new ideas that the team may not have considered. This is useful for risk identification too. All you need is a well-crafted prompt, such as the below, and GenAI tools will suggest some high-level risks to get you started:

“Imagine you are a project manager leading a change to upgrade your organisation’s accounting software. Identify 10 risks that could impact the project during its delivery.”

Some of the most popular project management tools are starting to embed AI into their software to actively study the makeup of your project and predict problems before they occur. Asana’s ‘Teammate’ AI is a prime example, analysing project schedules, task structures and the team’s workload to highlight areas of the project that look a little shaky.

AI is already helping project professionals identify risks they may have missed, so learn how to use these tools to speed up risk identification and plug any gaps you and the team might have missed.

2. Risk analysis: the power of one million data points

Analysing risks has always been a numbers game, with project professionals working to quantify the impact, probability and proximity of a risk, and its impact on the project.

Enterprise-level risk management tools have been using AI-powered engines for a while, with techniques such as Monte Carlo boosted by smart algorithms that help complete hundreds of calculations in seconds.

Platforms such as Taskade can also help you speed up your risk analysis administration by creating instant AI-powered templates.

But where will AI really change the world of risk analysis? Cognitive analytics is an AI trend to keep an eye on. It takes AI’s natural quantitative capabilities and adds human-like elements. Primarily, this technology consumes and understands unstructured data such as images, emails, text documents and social posts, creating millions of additional data points to feed your analysis.

On top of this, it will also start to ‘think’ like a human, learning how to apply context and rationale to provide a ‘real-world’ view of a risk, its likelihood and the potential impact on your project.

Right now, AI isn’t transforming the world of risk analysis much beyond what’s already in use. But get ahead of the game and learn about cognitive analysis and the changes it will make to risk analysis in the future.

3. Risk response and monitoring: supercomputing a path forward

Identifying and analysing risks isn’t enough. Project professionals must take actions to mitigate risks, keeping an eye on the impact of their responses to plan their next move. This has always been an admin-heavy process, with detailed reporting needed to keep track of actions and outcomes.

Software tools such as Nodes & Links are taking Quantitative Schedule Risk Analysis (QSRA) to the next level, using AI to turbocharge response simulations, helping project professionals increase their chances of choosing actions that best prevent risks turning into issues.

On top of this, AI-based reporting takes the time and admin out of risk monitoring, with predictive dashboards and smart KPIs doing all the heavy lifting, simply notifying project teams and sponsoring stakeholders when key metrics are out of tolerance.

As with all AI, these tools are only as strong as the data they’re given. Project teams must be disciplined with their admin, ensuring these tools are fed with the data they need to automate risk monitoring.

For years, risk response planning and monitoring has been a laborious task. But AI engines are now turbocharging response analysis, providing stakeholders with confidence in the path forwards and giving project professionals more time to do the things that matter.