Industrial AI Software Market Size & Share Analysis - Growth Trends & Forecasts (2024 - 2029)

Industrial AI Software Market Analysis

The industrial AI software market is valued at USD 84.34 billion in the current year. It is expected to register a CAGR of 35.97% during the forecast period to become USD 391.97 billion by the next five years.

• Increased focus on capturing value from industrial data. This drives the need for multi-dimensional optimization, meaning AI-enabled decision-making and operational agility are becoming more critical to executives. To thrive in today's volatile market, companies must simultaneously optimize their assets and processes across business objectives such as margins, economics, sustainability, etc.
• According to the European Commission, the European AI Strategy seeks to establish the European Union (EU) as a premier AI hub and to guarantee that artificial intelligence (AI) is trustworthy and centered on people. Such a purpose is translated through specific laws and deeds into the European approach to excellence and trust. An essential part of excellence in AI is making the most of available resources and coordinating investments. The Commission intends to invest EUR1 billion annually in AI through the Digital Europe and Horizon Europe programs. Over the digital decade, it will mobilize other investments from the private sector and the Member States to reach an annual investment volume of EUR 20 billion.
• Although AI is still one of the critical technology areas, organizations require an efficient way to scale their AI practices and use AI in business to accelerate ROI in AI investment. As organizations face more significant pressure to optimize their workflows, more companies will ask BI teams to manage and develop AI/ML models. The two critical factors that will drive this boost of a new BI-based AI developer class: First, enabling BI teams with tools such as automation platforms is more scalable and more sustainable than hiring dedicated data scientists; second, because BI teams are significantly closer to the business use-cases compared to data scientists, the lifecycle from the working model's requirement will be accelerated.
• With the advancement in AI technology, it is becoming more important than ever for the government to innovate its traditional methods to achieve better citizen engagement, interoperability, and accountability. Such trends are driving the demand for AI Governance from enterprises and organizations worldwide. For instance, Google has highlighted five areas where the government, in collaboration with AI practitioners and wider civil society, can play a crucial role in clarifying expectations about AI's application on a context-specific basis. These include explainability standards, safety considerations, approaches to appraising fairness, general liability frameworks, and requirements for human-AI collaboration.
• On the contrary, While most enterprise IT procurement is limited to simply choosing suitable software or hardware and eventually deploying it to serve its purpose, the fundamental trouble with AI is that there's an ongoing requirement for initial training and working with data and calibrating it to deliver the result. There are known problems limiting normalizing data in electronic records. These include natural language processing (NLP), proprietary datasets threatening open innovation, frequent bias in the medical literature due to journal bias and even outright fraud, and the growing complexity of health data such that past data is not specific sufficiently to be useful for current predictions.
• The issues caused by the COVID-19 pandemic suggested the weaknesses in production and redistribution with changes needed to make the supply chain more resilient. An increased investment toward AI in any in-transit element across the retail supply chain was presented. The industries in the various regions then depicted the biggest problems facing the supply chains as employee illness and shortages of raw supplies. The growing industrial focus on automation in the post-pandemic scenario and the move towards digitalization will result in higher demand for analytics and AI solutions in the market, which is expected to be more prevalent among IoT users in industries.

Industrial AI Software Market Trends

Retail and Consumer Packaged Goods is Expected to Hold Significant Share of the Market

Industrial AI Software Industry Segmentation

AI software is a computer program adept of high-complexity tasks, like learning, decision-making, and solving problems. AI software uses machine learning to simulate human intelligence, which can allow the software to complete tasks of increasing nuance and sensitivity. Companies use AI software to help their businesses run more efficiently and find new improvement areas. This can result in the better allocation of human resources, allowing individuals to focus on more strategic and fulfilling tasks.

The industrial AI software market is segmented by type (cloud-based and on-premise), by end-user industries (automotive & transportation, healthcare and life science, aerospace and defense, energy and utilities, retail and consumer packaged goods, and other end-user industries), and by geography (North America, Europe, Asia-Pacific, Latin America and and Middle East and Africa).

The market sizes and forecasts are provided in terms of value in USD for all the above segments.