‘According to the 2022 PwC survey, 70% of executives prioritise diversity in risk teams.’ This is one stat provided in the Project Management Institute’s (PMI) Building Resilience through Strategic Risk Management report published earlier this year.
The report highlights some key risk trends to watch for as businesses navigate through uncertainty. It also talks about businesses planning to invest in AI tools with emphasis on data analytics and detecting and monitoring threats, and automating processes. This however comes with a huge responsibility to ensure these systems and tools are providing quality data.
Here are the trends highlighted in the report, as well as my personal thoughts on what should be done to empower professionals to support their businesses confidently.
A brief reminder of risk management and the different approaches businesses can take:
Risk for a business is an event of uncertainty that when/if occurred, it will have an impact on a project. This is how a risk is defined in the PMBOK Guide seventh edition. The impact on the project can be negative – threat – or positive – opportunity. And there are 5 strategies to address each of those two risk categories.
PMI shares in the report 7 key trends for 2023: inflation, macroeconomic volatility, geopolitical conflict, cyber risk, health risk, climate change, and social inequality. All of these cause uncertainty to any business and project and need to be approach with focus and care from leaders. With the different domains of these risks, it is no surprise that keeping an ongoing approach to monitoring and managing risk could be a daunting exercise. Leaders, according to a survey run by PwC, are planning to investing more money in AI to help automate processes, and analyse data.
The intention of this is brilliant. I personally would flag one key concern all leaders need to be aware of and account for when embarking on this journey.
Is the system being developed, or the AI framework used, closely monitored and is it being assured?
A big risk for businesses is to start using such automation, and data analysis model without a thorough testing and close monitoring of the domains being build. In Quality Management, we encourage fact based decision making processes. When businesses build artificial intelligence models, they need to be confident that the model uses analytical approach that is fit for the purpose of the business; that the model is using data that has been quality checked that it is fit for purpose; and finally that the output presented by such model is being closely examined by humans who understand context and circumstantial situations.
And one approach to ensure that the above concern is addressed is provided by PMI in a four pro tips to building a strong risk culture:
- Use data
- Measure the impact
- Talk it out
- Plan for the worst
My favourite step in building such culture is to talk it out. Businesses with an open culture on discussing risk and its impact tend to be better equipped at handling uncertainties. And this very ethos must be used when building those AI models with the experts. Talk it out, bring in the relevant stakeholders and ensure that the systems are assessed for validity and suitability to the context of the business. If you are a project manager with a risk management speciality, you may wish to read about the Future of Assurance as published by the Chartered Quality Institute. Here is a brief about it in my latest How to Quality series.
You can download the full report ‘Building Resilience through Strategic Risk Management’ via PMI’s website here: https://www.pmi.org/learning/thought-leadership/resilience-and-risk-management
How do you and your leadership peers approach and deal with uncertainty?
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