Artificial Intelligence (AI) is now at the center of corporate investments in almost every sector, with generative AI and tools like ChatGPT and Gemini capturing much of the attention. However, while AI agents capture the imagination and attention of many, it’s equally true that other, more established fields of artificial intelligence are quietly but effectively finding applications in nearly every industrial sector. Among these, computer vision stands out—the technology that enables machines to extract information from images, giving computers eyes. Computer vision allows for the extraction, identification, classification, tracking, and interpretation of information in images, which can then be used by humans or other AI agents. It can be applied in a multitude of use cases, ranging from defective product recognition to tracking vehicles, people, or moving objects, with precision ranging from single-pixel analysis to full-image classification.
This is an extremely versatile technology, which according to a study1 conducted by Panasonic on a sample of 300 professionals working in digital transformation and the implementation of AI and Computer Vision, is employed in many business areas and applications. Repairs and maintenance (34%), production monitoring (33%), and quality checks (32%) are the areas where computer vision is most commonly adopted, followed by sales and marketing (31%), logistics and distribution (28%), back-office and administrative departments (25%), security (24%), and healthcare (22%).
The Benefits for Productivity
According to the study, computer vision proves to be the preferred technology for many companies to increase productivity. Computer vision has indeed reached a much higher level of maturity compared to other AI solutions. Precisely for this reason, most respondents expect an average productivity increase of 42% after three years from the implementation and integration of computer vision solutions in their company, with peaks of 52% for the manufacturing sector and 45% for the public and service sectors.
What really matters, however, is the ability to identify the areas where this technology can have the greatest impact on business processes and to skillfully integrate these tools within the company. To this end, as highlighted by another Microsoft study2, it is essential to start with the workforce: the goal is not to eliminate employees but to empower them by relieving them of repetitive and time-consuming activities. The companies that are gaining the most advantages from these technologies are those that have understood how to increase their staff’s productivity by enhancing them with AI. A successful example is the insurance sector. An insurance company like Generali3 handles over 90 million images of vehicles involved in claims each year. Without image analytics tools, employees spend 50% of their time analyzing these images to extract relevant information. This is a tedious, repetitive, and time-consuming process that Generali has managed to automate, significantly speeding it up. Here, computer vision is used to automatically detect and locate damages and data such as license plate numbers or vehicle identification numbers (VIN), estimate the extent of damages, or show the operator images of similar damages.
Challenges to Consider
Despite the enormous potential of this technology, development costs and the lack of skills represent the two main barriers to adoption reported by most companies surveyed. Being particularly recent technologies, finding a qualified team is often very difficult. Additionally, 37% of respondents report difficulties in finding support in development and maintenance outsourcing with third-party providers.
At Synapsi, we offer precisely this service. Every development and integration activity is always preceded by a preliminary study of business processes and the impact these technologies can have on the company. By involving all stakeholders, we ensure the development of solutions that bring measurable value and returns on investment. Budget and time constraints outline the path to follow, but it’s always important to have a clear destination. Only afterward do we move on to the phases of development, integration, and maintenance of computer vision solutions.
Lastly, data security and privacy are another major obstacle to the adoption of these technologies. To overcome this, it is always important to adopt solutions compliant with current regulations like GDPR and that ensure secure data storage and management. In this sense, it can be advantageous to choose hybrid solutions between cloud and edge computing or completely isolated (air-gapped) ones.
The Future of Computer Vision
Computer Vision is, as the study reveals, one of the most mature applications of artificial intelligence, and it is quietly but significantly revolutionizing the way companies in almost every sector work. However, this revolution is only at the beginning, and the trajectory is clear. More than a third of the companies surveyed (37%) have stated that they have already implemented and are benefiting from generative and agentic AI, with an additional 34% in the implementation phase of this technology and 17% still evaluating its adoption.
And this is where the potential of computer vision reaches its peak. Through its integration with AI agents, this technology enables a further step: it’s no longer just about extracting information from images but about automating decisions. By enhancing AI agents with visual perception of the physical world, it is indeed possible to automate complex tasks involving the extraction and interpretation of visual data. If you too want to understand how to integrate AI into your company, I invite you to schedule a meeting with our team to explore how we can help revolutionize your business processes.