As you’re looking at your plan for the coming year: does it lay out the specific internal and external drivers that could impact your targets? Does it accommodate for uncertainty? Is there enough agility to easily revisit and tweak?

If you’re answering “no” more often, it’s likely time to abandon sluggish, top-down, cycle-based planning in favor of continuous, forward-looking scenario planning. And with the rapid progression of AI, businesses can embrace driver-based scenario planning in a way they couldn’t before.

Scenario planning is table stakes for businesses

Historical trends and assumptions can’t capture the complexity, volatility and opportunities presented by our new reality. But AI and machine learning technology allow businesses to parse out key performance drivers from their data and use them to imagine new futures and plan for change. And AI does it efficiently.

It’s no surprise that among senior executives, forward-looking FP&A is the most popular use case for AI technology. According to Paro’s 2023 Future of Finance study, 67 percent of businesses that have adopted AI report using it for predictive analytics —more than any other use case, including process automation, customer service or hiring and recruiting.

And while scenario planning is nothing new, AI optimizes this process in three important ways.

How AI can upgrade your scenario planning

AI is helping businesses model scenarios with speed and specificity, while uncovering potential blind spots that humans alone often miss.

1. AI gives you more levers to pull.

Historically, scenario planning has been based on the more obvious relationships that professionals can manually discern between data points. But even the most talented team simply can’t cover the breadth of complexity in a large set of data.

AI can process vast amounts of data, finding subtle patterns and relationships between performance drivers. And it can do so using a variety of sources, from social media activity or weather data to sales and inventory data.

It then uses those patterns to predict opportunities—e.g., correlations between subtle distribution network changes and customer experience drivers—or issue warnings—e.g., connections between changes in planned maintenance schedules and equipment downtime that affect output.

Identifying these drivers can give you one more lever to pull in your scenario analysis. And professionals can further probe their AI to better understand what drivers have the most impact on the business.  

Click for full article