Managers and leaders worldwide struggle to decide between projects A, B, or both. Traditional estimation techniques fail because humans can't predict the future. This talk proposes a simulation-based approach inspired by investment strategies, industrial management, and poker playing. By leveraging AI, forecasting, and computing power, simulations offer a reliable and adaptable portfolio planning strategy. Rather than relying on human estimation, simulations streamline decision-making and provide reassurance.

Target Audience: Portfolio Managers, Product Leaders, CPO, CEO, CTO, Product Managers, Product Owners
Prerequisites: Beginner level probabilistic forecasting, familiarity with portfolio level decisions
Level: Basic

Extended Abstract:
Right now, around the world, managers and leaders are scratching their heads to try and answer the question “Should we take project A, B, or both?”. The techniques they are using, are woefully inadequate to answer their question because they rely on a skill humans don’t possess: predicting the future!
Estimation as a portfolio and risk management strategy relies on our ability to predict the future. But we don’t have that skill! What can we use instead then?
In this talk, we explore how we can learn from the world of investment (risk management), industrial management (process control), and poker playing (thinking in bets) to create a powerful simulation strategy that will streamline and reassure your portfolio planning team. Unlike humans, simulation can take as many ideas as you can throw at it, and can come up with the most likely winning scenarios quickly, repeatably, and is infinitely adaptable to future surprises.
Why rely on estimation when we can rely on AI, Forecasting, and the near-infinite computing power we have in even the most humble of spreadsheet programs?
Key Learnings

• Basics of simulating portfolio decisions
• Comparing simulation vs estimation for portfolio level decisions
• Examples of simulation use in complex scenario assessment, with N >> 1 options for decision
• How to effectively support decision making with simulation.