If you want to sophisticate your demand forecasting with AI, you\u2019re in the right place. We\u2019re on a journey to discover why we should use AI and its key benefits, with tips from professionals throughout.<\/p>\n
Table of Contents<\/strong><\/p>\n <\/a> <\/p>\n To create this article, I interviewed 23 professionals and analyzed their responses to understand common use cases for AI\u2019s role in demand forecasting.<\/p>\n The three main reasons for using AI in demand forecasting are:<\/p>\n I\u2019m going to look into these three in a little more detail.<\/p>\n Other reasons for using AI in demand forecasting include:<\/p>\n For the AI pioneers in demand forecasting, enhanced analysis is one of the main benefits of using AI.<\/p>\n Enhanced analysis is mainly considered a benefit in conjunction with predictive analysis, customer behavior analysis, and competitor analysis.<\/p>\n AI in predictive analysis identifies demand fluctuations and market trends so businesses can proactively respond to changes, reducing risks like stockouts or overproduction.<\/p>\n When it comes to competitive insights, AI pioneers are using tools to analyze competitors, identify market shifts, and take action based on the findings.<\/p>\n In an article for TrueProject<\/a>, Tom Villani<\/a>, TrueProject\u2019s CEO, credits AI\u2019s enhanced analysis as offering:<\/p>\n Enhanced analysis should be taken seriously. According to an IBM report<\/a>, poor data quality costs businesses worldwide.<\/p>\n Image Source<\/a><\/em><\/p>\n According to the experts I connected with, AI helps businesses anticipate trends and predict future sales more accurately than humans. However, it can\u2019t be ignored that a common side note to improved accuracy with AI in demand forecasting is that your input has to be good.<\/p>\n Many experts recommend keeping the AI updated to get accurate results.<\/p>\n Simplilearn<\/a> compared AI with human intelligence to see where AI shines and where humans do. When it comes to improved accuracy, AI takes the trophy.<\/p>\n On \u201cperfection,\u201d Simplilearn raises the possibility of \u201chuman mistakes\u201d missing nuances, whereas AI\u2019s capabilities are credited with being \u201cupdated\u201d to \u201cdeliver accurate results.\u201d<\/p>\n I like the quote, \u201cWithout data, you\u2019re just another person with an opinion.\u201d<\/p>\n Data allows businesses to make decisions that have the best chance of succeeding. Data can challenge what we think will work. I\u2019ve certainly been in situations where the data has proven the opposite of my expectations.<\/p>\n Gathering data is time-consuming, and your data analysis can be incorrect or biased when relying on a human.<\/p>\n While I don\u2019t want to ignore that AI can also be biased<\/a>, AI can reduce reliance on guesswork and increase forecast accuracy, leading to better data. In addition, AI can analyze data faster and in much larger datasets than humans.<\/p>\n In his Forbes article, Leveraging AI For Data-Driven Decision-Making While Safeguarding Privacy And Security<\/a>, Neil Sahota<\/a>, an AI advisor, writes incredibly positively about the role of AI in decision-making.<\/p>\n Sahota writes, \u201cAI algorithms, fueled by machine learning and advanced analytics, can process colossal datasets at speeds unimaginable for humans. This capability enables organizations to extract valuable insights, identify patterns, and make decisions with unprecedented accuracy<\/strong>.\u201d<\/p>\n In HubSpot\u2019s Smarter Selling with AI research<\/a>, a quarter of salespeople believe AI helps employees make data-driven decisions.<\/p>\n <\/a> <\/p>\n From the experts I connected with, I got many use cases for AI. Experts generously shared how they’re using AI and what tools so that you can get started.<\/p>\n You simply couldn\u2019t research AI in demand forecasting without examining its value in managing stock and inventory. I had a lot<\/em> of responses about AI and its role in managing stock and inventory.<\/p>\n Here are some of the best.<\/p>\n Tomasz Borys<\/a>, senior VP of marketing and sales at Deep Sentinel<\/a>, is using different data streams to build a wider picture of what the market wants so he can manage stock.<\/p>\n Borys says, \u201cWe noticed that our AI system predicted a 30% increase in demand for our outdoor cameras in certain regions during specific months. Upon investigation, we found this correlated with seasonal increases in property crimes in those areas. This insight allowed us to adjust our inventory and marketing strategies accordingly, resulting in a 25% increase in sales during those periods.\u201d<\/p>\n To get this data, Borys is using tools like Salesforce\u2019s Einstein Analytics<\/a>, which Borys credits for its \u201cability to analyze historical sales data alongside external factors like seasonal trends, economic indicators, and even local crime rates (which is particularly relevant for our security products). This comprehensive analysis allows us to predict demand with much greater accuracy than traditional methods.\u201d<\/p>\n Another company using AI to manage inventory is All Filters<\/a>. Shu Saito<\/a>, the CEO and founder, recommends Prophet by Facebook<\/a>. Saito uses this AI to predict seasonal demand.<\/p>\n Saito says, \u201cProphet allows me to model sales data with built-in flexibility, accounting for irregular trends like sudden surges or dips. This helps me optimize inventory levels and adjust marketing strategies ahead of key selling periods, ensuring I can meet customer demand without overstocking.\u201d<\/p>\n Finally, but by no means least, HubSpot CRM is used by Jason English<\/a>, entrepreneur and CEO of CG Tech<\/a>.<\/p>\n English says, \u201cI have discovered that AI has significantly impacted the way we predict sales demand. We utilize platforms such as HubSpot and Microsoft Dynamics 365 to analyze past data, market trends, and customer behavior trends. The accuracy of AI assists in improving our ability to predict demand, resulting in smoother inventory management and resource allocation.\u201d<\/p>\n What I like about this: <\/strong>HubSpot\u2019s CRM makes forecasting simple. Instead of importing and analyzing data in spreadsheets, sales teams or trend forecasting teams can get a seamless picture directly from the CRM. You can instantly view sales revenue by month or quarter to make data-driven divisions based on sales trends.<\/p>\n Request a demo for HubSpot\u2019s sales forecasting software today.<\/a><\/em><\/strong><\/p>\n I\u2019ve already mentioned the value of accurate data and how AI in demand forecasting helps teams make better decisions. Julie Ginn<\/a>, vice president of global revenue marketing at Aprimo<\/a>, can illustrate this point with an example of how Aprimo uses AI in demand forecasting.<\/p>\n Ginn shares how AI and machine learning generate sales forecasts and customer insights, \u201cWe use tools like Amazon Forecast and Microsoft Azure to analyze three to five years of a customer’s historical sales data to identify trends and patterns. For a major CPG company, Forecast predicted a 10% uptick in seasonal product demand. We adjusted marketing spend and saw sales jump 18%.<\/p>\n \u201cFor customers, AI-driven forecasts have cut excess inventory and boosted sales by 15% to 20% annually. Integrating predictive insights into business processes and using them to make timely decisions is key. Companies leveraging predictive analytics will gain a competitive edge.\u201d<\/p>\n When asked for a tip about having sizable, high-quality data, Ginn shared that they refresh models quarterly with new data. \u201cWhile AI is accurate, human judgment remains critical, especially for events impacting demand. AI improves human insights.\u201d<\/p>\n What I like about this<\/strong>: I love how Aprimo has used AI, demand forecasting, and marketing to understand what customers want. Increasing marketing spending would\u2019ve put the right product in front of the right audience, resulting in increased revenue.<\/p>\n As mentioned above, competitor analysis was one of the top use cases for AI in demand forecasting.<\/p>\n Jessica Bane<\/a>, director of business operations at GoPromotional<\/a>, provides an example of how GoPromotional does it.<\/p>\n Bane recommends pairing competitive intelligence with internal sales data to create a powerful forecasting tool.<\/p>\n \u201cInternal sales data provides the historical context, while competitive insights offer an external perspective,\u201d she explains. \u201cMerging these can refine predictions, offering a clearer view of where market demand might head. Integrating these data streams ensures forecasts aren’t just educated guesses but are grounded in comprehensive, multifaceted analyses. This integrated approach allows sales teams to remain agile and responsive to market changes.\u201d<\/p>\n When asked where to start, Bane recommends conducting regular competitor audits and tracking the following:<\/p>\n Bane says, \u201cCombining these findings with sales performance data<\/a> can paint a detailed picture of future demand trends. This kind of strategic review not only sharpens forecasts but also prepares the team to pivot quickly, optimizing both sales strategies and resource allocation.\u201d<\/p>\n To conduct competitor audits with AI to aid demand forecasting, Bane recommends Crayon<\/a> and Klue<\/a>.<\/p>\n She says, \u201c[These tools] are transforming how sales teams view the competitive landscape. These platforms gather valuable insights about competitors, like pricing and new product launches, and highlight market trends that could affect demand. Knowing what competitors are up to helps us anticipate shifts in the market, allowing us to adjust strategies proactively. It’s akin to having a window into future market dynamics, which is vital for staying ahead.\u201d<\/p>\n What I like about this:<\/strong> Bane is taking a holistic approach to demand forecasting combining competitive research with owned sales performances. It could be tempting to rely only on sales data but I like how the competitor audit would bring another layer of data.<\/p>\n Joanneke Schuurman<\/a>, sales executive at Custom-Lanyards.net<\/a>, also finds HubSpot CRM an essential tool for sales demand forecasting.<\/p>\n Schuurman says, \u201cOne way I use AI for sales-demand forecasting is by integrating tools like HubSpot alongside a platform like Clari<\/a>. These tools help track real-time data trends, historical sales patterns, and customer behaviors.\u201d<\/p>\n For example, they implemented AI-driven forecasts when launching a new lanyard product line and saw a 15% improvement in predicting peak demand, allowing them to optimize production scheduling.<\/p>\n \u201cBy analyzing patterns from past orders and customer preferences, AI helps us adjust marketing efforts and stock levels,\u201d she concludes.<\/p>\n If you\u2019re also using HubSpot\u2019s CRM and want to <\/strong>get more sophisticated with your forecasting<\/a><\/strong>, you can access data seamlessly within the CRM itself \u2014 no more exports into spreadsheets!<\/strong><\/p>\n Check out HubSpot\u2019s sales forecasting software.<\/a><\/em><\/strong><\/p>\n In my opinion, this is an interesting use case.<\/p>\n Daniel Meursing<\/a>, founder of Premier Staff<\/a>, uses sales-demand forecasting to establish security staffing needs. Knowing the demand for security staff helped Premier Staff where to invest in recruitment and training.<\/p>\n Meursing said, \u201cWe use Anaplan\u2019s AI-driven platform<\/a> for sales-demand forecasting. The tool analyzes historical data, market trends, and external factors to predict future demand for our staffing services. For example, Anaplan\u2019s AI helped us accurately forecast a 25% increase in demand for security staff at tech events, allowing us to proactively recruit and train personnel.\u201d<\/p>\n <\/a> <\/p>\n Before we close this research piece, I wanted to share some invaluable tips experts provided.<\/p>\n The tips for updating AI came in various formats: keep the data clean, update regularly, train the AI, etc. I received this tip so many times that it\u2019s taking the top spot in this shortlist of tips.<\/p>\n Outside of it being very true \u2014 you do need to keep AI updated and data clean and fresh in order to get the best out of it \u2014 I wonder how many might fail to integrate AI into their demand forecasting because their data input isn\u2019t quite there yet.<\/p>\n Tomasz Borys, mentioned above, updates AI models monthly and credits this with improving forecast accuracy by 15%.<\/p>\n Victor Santoro<\/a>, founder & CEO of Profit Leap<\/a>, uses AI tools Amazon Forecast<\/a> and Tableau<\/a> for predictive analysis.<\/p>\n Santoro says, \u201cStart with a basic tool like Google Sheets AI or Amazon Forecast. Connect them to your sales data and ask questions about patterns, risks, and opportunities. The more you use them, the smarter they\u2018ll get, tuning into the nuances of your business. If demand seems volatile, don\u2019t be afraid to make adjustments based on the forecasts.\u201d<\/p>\n For those intrigued by Santoro\u2019s use of these tools, he says, \u201cAmazon Forecast studies our past sales to anticipate seasonal fluctuations and demand spikes for our consulting services. By understanding these patterns, my team can optimize marketing spend, resource allocation, and new business development.<\/p>\n \u201cTableau helps us visualize complex sales data, identifying trends that would otherwise remain hidden. A few months ago, Tableau revealed an unexpected drop in sales from one of our major client segments. We were able to diagnose the issue and implement changes to reverse the trend, recovering over $200,000 in projected revenue.\u201d<\/p>\n Elia Guidorzi<\/a>, marketing executive at Techni Waterjet<\/a>, uses AI for predictive analysis. Guidorzi\u2019s main tip is to \u201censure your AI tool integrates with your CRM for real-time data, which enhances the accuracy of sales forecasts and allows your team to make more informed decisions.\u201d<\/p>\n![\"Download](\"https:\/\/no-cache.hubspot.com\/cta\/default\/53\/6f674af4-3116-43b0-8a54-4a64f926afb6.png\")
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Why Use AI for Demand Forecasting?<\/h2>\n
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Enhanced Analysis<\/h3>\n
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<\/p>\nImproved Accuracy<\/h3>\n
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<\/p>\nData-Driven Decision Making<\/h3>\n
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<\/p>\nHow AI Can Be Used for Demand Forecasting<\/h2>\n
To Manage Stock and Inventory<\/h3>\n
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<\/p>\nTo Make Marketing Decisions That Bolster Revenue<\/h3>\n
To Conduct Competitor Audits<\/h3>\n
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To Analyze Past Orders<\/h3>\n
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<\/p>\nTo Offer Recruitment and Training<\/h3>\n
Tips for AI Demand Forecasting<\/h2>\n
Tip 1: \u201cTrain\u201d and update your AI.<\/h3>\n
Tip 2: Start with basic tools.<\/h3>\n
Tip 3: Leverage AI for customer behavior.<\/h3>\n
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