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What Are the Disadvantages of Manual Revenue Forecasting, and How Can Automation Help?

May 5, 2023

In today’s world, the only certainty is that there will be uncertainty. The Covid-19 pandemic was a situation that few would have predicted, but it's not the only challenging financial event any organization will ever experience, realistically speaking.

The key problem is that finance teams are struggling to accurately predict what's coming. Especially if they are dealing with legacy systems, manual processes, and dispersed data. They need help forecasting comprehensively, accurately, and at speed to tighten costs and manage cash in response to market changes.

Financial forecasting, if done traditionally, is labor intensive. Revenue calculations typically require teams to manually review classic business data, run statistical calculations, and create an outlook for different regions and markets. The whole process consumes considerable time and resources each quarter.

These pain points can be dealt with, by automating processes and adopting next-gen technology such as artificial intelligence (AI) & machine learning (ML).

Why is this a serious problem?

Forecasting is an error-prone process

Manual processes increase the likelihood of simple accounting mistakes, such as transposing digits, misplacing a decimal point, double-counting or failing to record an activity in a ledger. An IDC report, states that businesses lose up to 30% of their revenue each year due to inefficient and error-prone manual processes. Adding to this, forecasts simply on the basis of historic data fail to account for frequently hidden shifts in your customers’ businesses and demands, let alone once-in-a-lifetime events.

Inaccurate predictions can lead to bad business decisions

Almost any business decision a corporation makes that will affect the future of the company relies on the ability to accurately forecast future cash flow, while knowing the company’s current cash position. These decisions could include hiring new employees or developing new dividend policies. 70% of C-level executives claim to have made a significant business decision based on inaccurate financials.


Sancode technologies can help corporations optimize their forecasting capabilities by incorporating AI, data mining, statistics and machine learning to create predictive models. Predictive analytics models can go through the information available, analyze patterns and make predictions based on these patterns. Revenue Forecasting efforts can be bolstered with AI seamlessly scanning through volumes of data and providing the corporation with insights, observations and suggestions in real-time.

This allows them to see patterns or trends that can otherwise be difficult for humans to detect. AI helps with both short-term and long-term planning by providing insights into what might happen in the future based on past actions, behaviors, and outcomes.

Machine learning technology on the other hand can allow for real-time updates to existing forecasts, so corporations always have the most up-to-date data at your fingertips.