AI-Based Forecasting Market Size, Share, Opportunities, And Trends By Technology (Bayesian Network, Evolutionary Algorithms, Deep Learning), By End Users (Manufacturing, Healthcare, Retail, Agriculture, Others), And By Geography - Forecasts From 2023 To 2028

AI-based forecasting refers to the employment of AI technology software and machine learning algorithms to predict the future values of different business aspects and sectors based on past data. An AI-based forecasting application automates data connection and preparation processes and identifies different business metrics on which to base the forecast to create a customized AI forecasting solution for different enterprises and departments. The requirement of minimum input by the user and the ability to consider a vast number of factors and metrics is creating a high demand for AI-based forecasting software across different healthcare, retail, and other manufacturing sectors. Evolutionary algorithms, deep learning, and Bayesian network are the most commonly used technologies in the AI-based forecasting market. The implementation of AI-based forecasting assists companies in achieving a competitive advantage and lowering the risk of manufacturing errors. Due to this, an increasing number of enterprises are incorporating AI-powered forecasting methods in their business operations. For instance, the application of an AI-based forecasting process in Reynolds Aluminium enabled the company to lower its inventory cost by 1 million pounds and decrease errors in its forecasting by approximately 2%. Therefore, due to the constant evolution in AI technology and the increasing adoption of AI-powered forecasting methods across several industries, it can be anticipated that the AI-based forecasting market will significantly grow over the forecast period.

Market Drivers

• Increase in the amount of data generated by companies

The digitalization of the business operations of companies in different business fields is resulting in massive growth in the data generated by companies and their customers resulting in the need for big data analytics solutions using AI technology in enterprises. For instance, a survey revealed that a medium portion of around 40% of the data generated by an enterprise is being effectively utilized. However, the optimum utilization of the data generated by companies by using them in AI-based forecasting and data analytics model could help companies to accurately predict demand, forecast growth, and manage supply chains and inventories. For instance, the integration of an AI-based forecasting model in the business operations of Danone Group enabled the company to enhance its demand forecasting and lower revenue loss by around 30%. Hence, AI-based forecasting software is being extensively adopted by companies to improve their business operations.

The high levels of cost associated with AI-based forecasting tools could limit the growth of the optical character recognition market.

The additional cost expenses of installing and implementing AI-powered forecasting solutions could discourage small and medium-sized business enterprises from purchasing such solutions. The effective working of AI-based forecasting requires employees with expertise in the working of such software. Therefore, a company newly incorporating such software solutions might have to incur additional expenses to conduct workshops and training sessions. In addition, the company should spend to maintain and updates its AI-based forecasting service to prevent it from becoming obsolete. Hence, such costs could prevent the application of AI-based forecasting software across certain companies.

Key Developments

• In March 2023, Zionex, Inc., a company specializing in the production of technological business solutions introduced its latest SaaS software product based on AI, PlanNEL Beta, which contains different forecasting and planning features including AI demand planning, AI-sponsored baseline forecasting, and other AI-based inventory management and prediction tools.
• In October 2022, Everything But Water, a US-based retail firm announced its decision to integrate the AI-based forecasting, planning, and other solutions offered by Atuit.ai, a Zebra Technologies unit, into its business operations to enhance the quality of its business operations.
• In June 2022, Quantive, a company providing technological execution solutions to businesses acquired a UK-based AI-tech company, Cliff.ai by Greendeck Technologies, Ltd. The acquisition with the aim of improving the forecasting, data analysis, and other data-driven decision features on its business strategy application, Quantive Results.

The incorporation of AI-based forecasting software in weather prediction could help the farmers and agricultural sector to improve the yield and warn about any possible natural disasters.

AI-based weather forecasting software uses satellite and other aerial image sources to examine weather patterns and conditions. Due to the higher accuracy of AI-based forecasting models, the farmers could prevent the loss of their harvest in case of a disaster. For instance, a collaboration between the World Bank and the Government of India enabled Indian farmers to generate 37% more revenue by using the AI-sponsored weather prediction tool by Cropin Technologies. The disturbances in weather and the changes in rainfall patterns accounted for by global warming increase the demand for AI-based weather forecasting to prevent the loss of crops cultivated by farmers around the world. For instance, the Ministry of Agriculture and Farmers’ welfare in India announced that approximately 5.04 mha of agricultural area in India was affected due to natural calamities in 2021. Therefore, the increasing amount of crop and harvest damage across the world due to erratic weather changes generates a high level of demand for AI-based forecasting products.

Asia Pacific holds a prominent share of the AI-based forecasting market and is expected to grow in the forecast period.

AI-based forecasting in the Asia Pacific region is experiencing significant growth due to the escalating investments in the AI field and the prominence of the retail and agricultural sectors in this region. The increase in the retail sector of the economies in the Asia Pacific region is due to the rise in e-commerce activities and the digitalization of business activities. Consequentially, a large number of companies operating in the retail sector are using AI-based forecasting tools to centralize their working departments to accurately manage inventory storage and purchase orders. For instance, the HnM fashion retail stores in Asia use AI-driven demand forecasting tools to make production and other business decisions. Therefore, the increasing market size of the retail sector in the Asia Pacific region encourages the growth of the AI-based forecasting market in this region.

AI-Based Forecasting Market Scope:

Key Market Segments:

• By Technology
  • Bayesian Network
  • Evolutionary Algorithms
  • Deep Learning
• By End-Users
  • Manufacturing
  • Healthcare
  • Retail
  • Agriculture
  • Others
• By Geography
  • North America
    • USA
    • Canada
    • Mexico
  • South America
    • Brazil
    • Argentina
    • Others
  • Europe
    • United Kingdom
    • Germany
    • France
    • Italy
    • Spain
    • Others
  • Middle East and Africa
    • Saudi Arabia
    • UAE
    • Others
  • Asia Pacific
    • China
    • Japan
    • India
    • South Korea
    • Australia
    • Singapore
    • Indonesia
    • Others