The Truth About AI Forecasting that Companies Won’t Tell You

Sales forecasting is one of the most critical tools when it comes to business planning and determining the long-term growth of a business. 

Since it is such a crucial component, oftentimes managers spend a large amount of time trying to develop a sales forecast.

Typically, companies conduct weekly forecasts to make sure they are on track with their sales goal which means, a large portion of their time during the week is spent developing a plan of action.

There are many ways companies can forecast revenue from intuition, to in depth historical analyses, or through various pipeline management forecasting techniques.

What all these forecasting methods lack however, is the ability to account for human error.

Even with a proper CRM software, the data that is used for forecasts can be inflated, making it inaccurate.

This is why companies often turn to some form of Artificial Intelligence to reduce inaccuracies in sales forecasting.

The Reason Behind AI Forecasting

Sales forecasting techniques such as length of sales cycle forecasting, rollup forecasting, scenario writing forecasting, and opportunity stage forecasting all rely somewhat on human judgement.

The problem with human judgement is that it is often influenced by biases. Therefore, it is not 100% accurate. It is an intelligent forecasting tool because over time with more data input, this forecasting tool becomes well adapted with a company’s sales.

With AI forecasting, these errors caused by human bias are virtually eliminated because AI relies strictly on hard data, making it the most accurate forecasting method. It offers predictive insights into the forecasted sales figures.

AI Used in Sales Pipeline

Typically, AI is used by sales leaders to accurately determine weighted sales forecasts. Despite using algorithms to determine forecasting methods, the data produced by traditional sales forecasting techniques can be unreliable.

AI can use complex algorithms to conduct predictive forecasting of rep and lead interactions to determine its own weighted sales forecast.

This number can then be compared to the number given by reps to determine what the actual probability is for a particular deal to close. It can determine aggregate sales volumes for a sales organization

AI Used in Marketing

Some prospects in companies come from the marketing department. The marketing department uses their own set of complex algorithms to determine whether or not a potential lead is worth pursuing.

The sales department uses indicators such as a lead score to determine a list of prospects that is then given to a sales team.

What is Lead Scoring?

The term lead scoring is used to describe decisions that are based on consumer buying signals. Typically, a marketing team analyzes certain interactions that customers have with the company’s product, website, and social media.

Factors such as the amount of time a potential customer spends on a website or the amount of clicks they have on the website are all summed up to generate a lead score.

The purpose of lead scoring is to rank each lead based on the perceived value of the lead that is represented in an organization.

Often lead scoring is based on buying signals that the reps receive from the customers. Since these signals are purely subjective, they can be based on an impulse or gut feeling.

When emotions begin to sway the lead score, this form of human error, can result in data inaccuracies.

To mitigate this error, companies start implementing various software that can reduce or even eliminate biased lead scores.

AI and Lead Scoring

AI technology is becoming an integral part of a company’s sales forecasts and offers meaningful insights into the sales pipeline. Through AI, businesses are able to improve their lead score by more accurately determining the statuses of sales opportunities which in return can lead to more accurate sales forecasts.

AI forecasting uses data derived from factors that a traditional CRM software can’t account for. It can use real-time data in conjunction with historical data to identify sales leads and conduct predictive analytics.

The algorithms that most AI software use are extremely intricate and can take a multitude of different factors into account such as reps’ individual opportunities and sales numbers.

This technology takes factors like demographics, psychographics, and technographics to tweak the lead score and then spits out the strongest possible lead.

The detailed analyses conducted by AI technology is the reason why this forecasting method is often deemed as being accurate.

Fault in Human Error

In the beginning, this article mentioned how human error caused internally within a company by sales reps can lead to inaccurate sales forecasts.

Human error actually works both ways. No matter how sophisticated the technology, AI can’t account for all human emotions.

This is why even though it is a sophisticated forecasting method, AI sales forecasting needs to be taken with a grain of salt.

AI can offer powerful insight into a lead. However, human relationship intelligence also plays a factor.

Humans are emotional beings and often make emotionally driven decisions. This is a factor that AI can’t account for. This is why the best way to use AI for sales forecasting is to use it in conjunction with other sales forecasting methods.

Accounting for Customer Expectation

Companies that rely only on AI for forecasting can experience a decrease in revenue. An expectation of the modern customer is to have a more personalized approach.

In order for sales reps to close deals, they have to be able to connect with customers by creating personalized interactions and experiences.

This is crucial because CRM software companies often believe that if they use AI technology in their software, it will become a superior product.

This isn’t always the case because despite having a great product, if the product doesn’t meet customer expectations or if the sales rep can’t build a personal connection with the client, then the money invested in implementing AI technology into a CRM software will have been useless.

Pros and Cons of Using AI

The benefit of using AI is that everything reported by AI is based on emotionless facts. The data reported by AI is raw and generally clean. In addition, AI can leverage data better than software programmed by humans and therefore can create personalized experiences.

Another benefit of using AI is that AI has an eidetic memory so to speak. This means that it does not forget and the more information and training that is put into the technology, the smarter the software will get.

Even though AI is a smart piece of technology, it does come with its’ own set of hindrances. For one, a lot of time and patience is required in order for AI to become “smart”.

A lot of companies that use AI for forecasting do not give it enough time to “learn” and that’s why they end up not considering it to be a reliable forecasting method.

Employee Privacy

Using AI for sales forecasting can eliminate human error, but it does come with a cost.

In order to have a smart AI, companies often have to tap into reps’ personal data through email, phone conversation and text messages.

This invasion of privacy allows the AI software to analyze underlying tone of conversations in order to determine a “lead score” or the likelihood that a particular deal will close.

This data is then used as a benchmark for companies to determine the accuracy of the forecasts conducted by a sales rep.

Ethical Concerns

AI forecasting does raise ethical concerns and can be perceived as a company’s inability to trust forecasts conducted by reps which can lead to animosity amongst employees.

In addition, many sales reps use personal phones to conduct calls. With AI technology, all of a rep’s personal data would be available to the company.

Personal data should be private, which is why AI forecasting can be an issue for some companies. One way to resolve this issue is by giving sales reps company phones, laptops and emails.

That way the company that is using AI forecasting can only have access to company data. This solution is often expensive because companies have to pay out of pocket to provide this tech for employees.

Who Should Use AI Sales Forecasting?

There is not a specific type of company that needs to use AI forecasting because it is a forecasting method that is easily adaptable.

In order for this forecasting method to be accurate, however, companies need to give it time to adapt and learn from the data given.

Is AI Forecasting Right for Me?

Unless you and or your company are paranoid about a robot uprising, this forecasting method is a great way to obtain accurate sales data. It is reliable to an extent and offers insight that traditional CRM software is incapable of. If companies want further more detailed analysis of their sales forecasts, they can always use other forecasting methods such as multivariable and regression analysis forecasting. One thing to note however that multivariable, regression analysis, and AI are all expensive techniques that require years of data so they aren’t forecasting methods for smaller companies, or fairly new startups.


AI forecasting requires a lot of patience and time which is why it is recommended to use AI forecasting in conjunction with other sales forecasting methods. 

The predictive intelligence of this forecasting method offers real insight into the data-driven process of sales predictability.

This forecasting method is a great way to get accurate data but again technology cannot account for human emotion. This is why companies should not be solely dependent on this forecasting method.

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