Vertical AI Market Trends

Vertical AI Market Trends

The integration of vertical AI with other emerging technologies, such as IoT, 5G, and edge computing, enhances its functionality and application across industries. In sectors such as automotive, the combination of AI and IoT enables real-time monitoring of vehicle performance, while AI’s integration with 5G allows for faster data processing in industries including healthcare.
 

This synergy between vertical AI and other technologies creates smarter, more responsive solutions that can optimize performance and provide better insights. The increasing integration of these technologies is pushing the growth of the vertical AI market as businesses seek to harness the power of AI alongside other advanced tools.
 

Vertical AI providers are focused on launching innovative AI solutions for various industries. For instance, in August 2024, Caregility Corporation, an enterprise telehealth provider dedicated to connecting care for patients and clinicians released new fall risk detection capability in its iObserver solution. Hospital care teams use iObserver for continuous observation of patients at risk of self-harm or falls. The new AI capability, developed natively by Caregility, uses computer vision to analyze visual information, detect fall risks, and alert caregivers accordingly.
 

Additionally, with the explosion of big data in across industries, AI systems require vast amounts of information to make informed decisions. According to Statista, the total amount of data created, captured, copied, and consumed across the globe reached 149 zettabytes in 2024.  Vertical AI uses this data to improve processes, enhance customer experiences, and generate insights. In healthcare, AI analyzes patient records to find patterns and predict outcomes. In finance, AI reviews transaction data to detect fraud. The increase in big data and the power of cloud computing and edge devices drive AI adoption in vertical markets.
 

The initial cost of implementing vertical AI solutions can be very high for many organizations. Developing industry-specific AI models, getting the necessary infrastructure, and training staff to handle AI systems require significant investments. Additionally, for industries that lack the necessary data or infrastructure to support AI, the setup costs can be even higher. This financial barrier can make it hard for SMEs to adopt vertical AI, limiting its accessibility to larger, more resource-rich organizations.