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by Jordan Wilton on September 12, 2024
Weaving risk management into the fabric of each project roadmap helps managers and teams systematically prepare for any circumstances that could affect profitability, delivery dates, and customer experience.
The main purpose of any risk management system is to identify, track, and mitigate risks, both on the negative and positive sides. The big question is, can AI help? And if so, to what extent?
Here’s exactly how you can use AI to handle various aspects of your risk management strategy by helping you save valuable time with automation and self-learning capabilities.
How to use AI for project risk management
Create a risk management plan
An effective risk management plan will pinpoint risks and possible disruptions during the planning stage before work on the project even begins. Some AI tools can go ahead and create a solid risk management plan for you; all you have to do is plug in the relevant data.
Depending on which tool you use, this may or may not be as comprehensive as the project requires. For example, asking Gemini to make you a risk management plan won’t yield the kind of specifications that a project management software AI tool would, as the latter is specifically designed with project risks in mind.
In fact, more and more project management tools are now incorporating copilot features that can help you plan out your risk management strategy with the appropriate documentation before the project officially kicks off.
Predictive analytics
In reality, AI has been in the project management software wheelhouse for many years; it just wasn’t necessarily described as such.
Most PSA solutions– aka automated project management software for service businesses– offer a predictive analytics tool that qualifies as AI in that it’s a self-learning model.
Predictive analysis features will analyze your current and historical project data, learn patterns, and then spit these out into nifty reports that can help predict when a project might run into trouble.
Like most AI tools, the more data it has to pull from, the higher rate of success in identifying likely project risks and when they most likely could occur.
Risk tracking
AI can help monitor time spent, budget, utilization rates, etc, but this isn’t a job that AI necessarily has to do. Basic project tracking with automated alerts does not require a self-learning model and can be leveraged with basic project management tools.
However, where AI can come in most handy when it comes to tracking risks over the course of your project is in the accessibility of information. Instead of having to run a report, you can simply ask an AI chatbot to tell you where you stand with risks, and program it to tell you only what you want to know, aka the metrics that matter most to you day to day, week to week.
Scenario simulation
Get better at risk management and operations at large with AI-generated risk simulation. No need for insane calculations or endless formulas in an Excel spreadsheet. AI scenario simulations can test for risks at scale while eliminating human bias.
These systems will also let you see plausible events and their compounding effects. In the past, testing scenarios for operations in particular was arduous and expensive, but with AI, this can now be done swiftly and affordably to develop a comprehensive risk management protocol.
Data-driven recommendations to mitigate risk
Think about when you ask a question to ChatGPT. It learns from data it already has to generate an answer for you that’s intelligible, that makes sense of all the data.
This is one of the best things about AI for risk management: it’s not just about analyzing historical project data; it’s about presenting that information in a way you can understand and even taking that a step further to suggest steps you can take to mitigate risk based on that data. And it does so without human bias, meaning recommendations are always data-driven. These recommendations are invaluable for project managers who can evaluate the AI recommendations and act accordingly.
Adaptable strategy
A risk management schema in the context of projects shouldn’t be rigid; on the contrary, a highly adaptable framework is what can help you best identify negative and even positive risks, i.e. opportunities that may arise to increase efficiency, profit, quality, and customer experience.
AI can help create an adaptable and proactive approach to project risks, preventing PMs from getting bogged down by the nitty gritty of constant tracking or focusing too much on the ‘negative’. These tools can quickly account for a shift in strategy with appropriate algorithms that will adjust based on the data and any human input given to the system by a project manager.
What are the drawbacks of using AI for project risk management?
Although AI provides excellent value for project risk assessment by way of analyzing vast stores of data and spitting out simulations and recommendations, it only goes so far. The role of a project manager, regardless of the industry, requires strong soft skills, including high levels of adaptability and intuition. Responding favorably to the unexpected still requires personal strengths on the part of the PM.
AI as of now cannot replicate these soft skills and cannot possibly detect and mitigate all risks that have any sort of subjectivity, anything that can’t be identified with data alone.
Dynamic decision-making from a human mind with experience to leverage is still required in the face of project crises and unforeseen events.
Historical project data is not enough to cover the basis of project risks, as market conditions can shift, sometimes very rapidly, depending on the industry. Unless these variables are specifically accounted for in the AI’s data set, PMs still have to factor these in themselves.
Use AI to streamline the risk management process and save time
Including an AI system for risk management in your tool belt certainly should help streamline your processes in regard to the aforementioned functions.
Best of all, like most self-learning models, AI can potentially save you a lot of time, starting with creating a risk management plan for you and sticking with you throughout the project lifecycle to track risks and constantly analyze relevant data.
