Image courtesy of Grok 2.
Here's an alternative approach to risk quantification.
Identify your unknowns. Scenario plan for them. Quantify and then Control.
The Unknowns you ask? Well, there are at least two types.
Known Unknowns
Unknown Unknowns
Let's review and discuss...
Known Unknowns (KUs):
Definition: Events we can predict based on past experience, but their occurrence probability remains uncertain.
Challenge: Uncertainty in probability and impact, resource allocation dilemmas, and the need for continuous scenario planning amidst dynamic environments and potential cognitive biases.
Strategy: Quantification Method: Use scenario planning ("What if this happens, then we do this") to estimate the impact. Adjust effort hours and skill requirements based on these scenarios. This method allows for a more precise calculation of risk, moving from a percentage-based guess to a scenario-driven estimate.
Example: The impact of inflation or the evolution of AI on project outcomes.
Unknown Unknowns (UUs):
Definition: Events or scenarios that are not only unpredictable based on current knowledge and experience but also not considered within the realm of possibility at the time of planning. These are the unforeseen events where we are unaware of both the event itself and its potential impact until they actually occur.
Challenge: By definition, inherent unpredictability, which demands an adaptive and resilient approach in planning and execution, as they represent risks or events that are not foreseen and thus not prepared for.
Strategy: Early Warning System (EWS): Implement systems to detect emerging issues, not just in your project but across similar projects globally. This proactive approach turns UUs into KUs, allowing for timely response and quantification of potential impacts.
Example: The IT labor market disruption due to geopolitical events like the Russia-Ukraine conflict could be anticipated through an EWS, allowing for preemptive adjustments in project planning. Leveraging AI: With the advent of General AI (GenAI), we can now tap into vast, diverse datasets to identify patterns or potential risks that were previously unknown. Different AI models can offer varied insights, enhancing our ability to foresee and quantify UUs.
Conclusion: By shifting from a static percentage-based risk pool to a dynamic, scenario-based approach, project management can better adapt to both known and unknown risks. This involves:
Scenario Planning: For Known Unknowns, detailed what-if scenarios provide a structured way to assess and mitigate risks.
Proactive Monitoring: For Unknown Unknowns, an Early Warning System, supplemented by AI-driven analytics, allows for the transformation of unforeseen events into manageable risks.
This approach not only enhances project resilience but also aligns risk management more closely with actual project dynamics, ensuring that both the project's success and the service provider's profitability are safeguarded through informed, data-driven strategies.
As always, no AI was harmed in the writing of this note.
#projectestimation #riskpool #ISGOne #PEaaS #PEACS