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Rich Wagner is chief product officer and founder of Prevedere, a predictive analytics company that helps enterprises createaccurate forecast models by incorporating the global economic leading indicators. Rich brings over 20 years of technology, innovation, and leadership experience from Big 4 consulting and Fortune 500 companies.

Strategic growth in the face of economic turbulence is difficult at best. Even three years since the start of the pandemic and the accompanying economic concern, business leaders continue to function under layers of uncertainty. Looking at 2023, the first half of the year did not economically perform as anyone expected, and lingering doubts ofconsumer resilience are casting a shadow around the second half.

As we come face to face with an economic turning point, strategic executives are turning to AI modeling and machine learning technology to bring them back to proactive planning and away from reactive navigation. However, a recent survey by tech services company Accenture found that only 16% of CFOs are harnessing the power of real-time economic data in their business planning.

Just as executives are turning to generative AI to help solve costly productivity issues, executives must also turn to AI modeling and machine learning for business planning. Custom insights that combine their business historic performance data with real-time economic data allow them to create optimistic and pessimistic financial guardrails for the future.

With these insights at their fingertips, executives are able to navigate turbulence with grace, recovering quicker and seizing opportunity with more success.

The importance of external economic data

Traditionally, demand forecasting relies only on the historic data of a company in order to predictively plan for future performance. However, without also incorporating external economic data, this approach leaves a lot of room for error.

Take, for example, years where business performance is more of an anomaly than a sign of business growth or contraction. Executives and FP&A teams relying primarily on historical performance are basing future growth off of bad data. This is exactly how we should look at business performance between 2020 and 2022.

Those years were riddled with supply chain disruptions and inflated consumer spending fueled by government stimulus during the pandemic. Consumers were spending more than they were making and changing their behaviors every few months. Without considering those economic oddities, companies that use traditional demand-forecasting methods could go on to project higher growth in 2021 than was possible. The ongoing economic stimulus that continued in 2021 only made this more prominent in 2022.

However, executives who utilized AI data modeling and machine learning to include external economic data in their planning process were able to more accurately predict business performance throughout the pandemic. For example, retailers who used this technology accurately predicted the return to normalcy along the supply chain and avoided the inventory issues many big retailers like Target and Walmart experienced.

The competitive edge to AI modeling and machine learning

The benefit of using predictive analytics for financial planning goes well beyond navigating economic uncertainty. Because the technology is able to analyze and predict based on more than just financial performance (think trends in sales, promotions, and audience), it allows leaders to confidently step outside their comfort zones and approach business challenges with a competitive edge.

Instead of stepping into the unknown with a blindfold, leaders can use predictive planning to create optimistic and pessimistic forecasts, giving them guardrails in which to make their decisions. So if a business is looking to experiment with its audience or take a new creative approach to earning more market share, then leaders have data-driven insights to support those decisions.

Stepping into some certainty

The rise of generative AI has made the financial impact of productivity very clear. In fact, a recent McKinsey report found that generative AI could increase productivity enough for a 10 to 15% overall cost savings on R&D. The conclusion is clear: If a computer can do the mundane tasks quickly and free up your teams time for more important work, then its worth the investment.

The same thought can be applied to AI data modeling and financial planning.

While many businesses continue to reactively navigate the current uncertain market, those who have decided to embrace AI data modeling in their financial planning process are able to look beyond recovery and plan their next strategic business move. Whether its increasing sales with their already loyal customers or branching out to capture a new market, predictive planning allows leaders to move forward with confidence.

Read more:

Unlocking the potential of AI data modeling within CPG - Supermarket News