Using AI To Forecast Your Revenue

The way CEOs manage revenue forecasting is rapidly changing – to accurately predict future revenue, organizations require efficient processes and tools to generate and disseminate real-time data that reflect rapidly changing circumstances.

Too many companies still rely on manual forecasting because they think AI requires better-quality data than they have available. Nowadays, that’s a costly mistake.

Not All Excel Forecasts Are Created Equal

CEO’s know what a “good” forecasting process looks like: but how it is usually executed on can be drastically different.

Excel spreadsheets are repeatedly shared back and forth and finally end up with finance, where they have to be manually sifted through, validated, and merged. This process takes up an enormous amount of time for a CFO, usually requiring multiple rounds of revisions, and is an error-prone process with an accuracy rate far lower than you’d like. By the time it reaches the CEO or board meeting, that data is likely outdated and tainted by human biases and errors.

According to McKinsey, who surveyed 130 CFOs, assessing their satisfaction with their forecast accuracy, some 40 percent said their forecasts are not particularly accurate and that the process takes far too much time.

Adopting AI Into Your Forecasting Process

What You Can Expect

Reduce Deal Time To Close By 10%

96% Revenue Prediction Accuracy Vs 72% When Done Manually ​

Increase Revenue By 4% Within The First Quarter

Find The Truth In Your Pipeline

Ask Your Team The Right Questions

Know Months In Advance That You Will Miss Your Targets

Data Silos Are Leading To Revenue Leakage

Data is building up in various systems across your organization – often automatically, without anyone noticing. This is not always financial data, but increasingly it’s operational team data that can offer powerful insights into your business performance and reveal growth opportunities for optimization.

Accessing these insights from your data silos requires intelligent, powerful tooling that runs in real-time, is consistent and is centrally available to key decision-makers. Excel is simply not fit for purpose in this scenario. If you are to win in the long-term and wish to be ‘completely’ satisfied with your forecasting processes, senior leaders must have a clear vision of how they will use and adopt new technologies. 

CEOs and CFOs are well-positioned to provide the vision to lead this technological change and adoption of advanced forecasting processes.

They have the expertise to assess the real value to be gained from such tooling and ironically, they are typically the ones with the least sophisticated tooling. From our experience, they are still relying on outdated, error-strewn spreadsheets extracted from various systems that can be tainted by human biases.

If you are waiting on better quality data before adopting AI, it’s worth noting that you are currently making important strategic decisions on this data today.

AI and Machine Learning have the capacity to consume, cleanse and churn huge volumes of data, in real-time and identify new growth opportunities, otherwise not visible with manual spreadsheet analysis and human processing power. 

The power of AI forecasting enables CEOs & CFOs to make quick strategic maneuvers because the tooling exposes opportunities and risks before they happen, rather than just reacting to them after the fact.