For leading enterprises, the next chapter in operational excellence is bringing artificial intelligence to the physical world.
An important trend is the growing adoption and availability of vision AI (also called computer vision) by enterprises using the technology to transform processes in manufacturing, logistics, services, retail, and other industries.
In the latest report "Trends in Vision AI 2025," we delve into the key insights shaping this field and explore how enterprises are successfully deploying vision AI to solve real-world challenges. We've examined thousands of enterprise deployments to provide a data-driven perspective on the state of vision AI and its transformative potential.
Vision AI Trends Key Highlights
Below are a few of the findings we explore in the report.
Two concurrent revolutions in vision AI
While large, general-purpose vision models are making headlines with their impressive capabilities, a parallel revolution is underway with the rise of purpose-built vision AI models. These specialized, efficient models are proving highly effective for tackling specific business challenges, running in the cloud and on edge devices, and are driving a shift in the practical application of AI in key industries.
Training enterprise AI is less data intensive than you’d expect
While it may sound daunting, the process of training custom AI has become significantly less data intensive. Our analysis revealed that 43% of enterprise models were trained with datasets containing fewer than 1,000 images. With just a few hundred images, businesses can develop custom AI models capable of solving specialized challenges, greatly reducing development time and cost.
Models with varying levels of accuracy are driving value
While automated accuracy tests are useful when comparing different versions of AI models, it’s more important to evaluate the effectiveness of models based on the real world use case. The report shows most enterprise AI models achieve 80% accuracy, but lower accuracy scores are acceptable in many situations, especially when combining multiple models or tackling previously unsolvable business challenges.
Vision AI models get better over time – with minimal human effort
Compared to traditional machine vision solutions, one of the key advantages of modern vision AI is the ability to retrain models and improve performance over time. As demonstrated in the report, by leveraging images annotated with the initial AI model, organizations are actively retraining their models and significantly improving accuracy. This process requires minimal effort, since they can review predictions from their custom model instead of annotating images manually.
For example, the report highlights one organization that retrained their AI model 18 times during the sample period, expanding the training dataset by over 900 images and improved the accuracy 40 percentage points.
AI-assisted development reduces time to deployment
The AI development process has become incredibly efficient. Our data shows that 51% of new models were deployed within the same week they were trained. This rapid deployment is largely thanks to dedicated tools and platforms that simplify the process, allowing businesses to quickly build, test, and deploy custom AI systems and start seeing value immediately.
Multi-stage processes and integration unlock new possibilities
When solving complex business challenges, enterprises are increasingly using multi-stage AI processes, combining different models and leveraging the unique strengths of each, to deploy creative solutions. Additionally, the report also covers how integrating AI with other systems, such as databases, alert systems, and GPS, can unlock even greater efficiency, automation, and competitive advantages.
Real-World Applications: Case Studies in Vision AI
The report features three case studies that showcase the ease of developing purpose-built AI, deploying to edge devices, and integrating with existing systems:
- Predictive Maintenance in Mineral Production: A global supplier of minerals and materials uses visual AI to monitor equipment condition and predict maintenance needs, reducing downtime and costs.
- Packaging Integrity in Food Processing: A national food supplier leverages edge AI to detect packaging defects in real-time, ensuring product quality and minimizing waste.
- Automated Yard Management in Logistics: Leading freight and logistics companies are using visual AI combined with GPS data to optimize yard operations, improve container tracking, and enhance overall efficiency.
These case studies provide valuable insights into how businesses are leveraging AI to solve real-world challenges and achieve tangible results.
Read the full report
Click this link to download the full report: “Trends in Vision AI 2025: Data Driven Insights in How Leading Enterprises are Deploying AI.”
If you have questions or want to share your own insights from your own experience with AI, please email stories@roboflow.com. We’d love to hear from you and perhaps include your story in a future piece.
Cite this Post
Use the following entry to cite this post in your research:
Patrick Deschere. (Mar 7, 2025). Report: Trends in Vision AI 2025. Roboflow Blog: https://blog.roboflow.com/vision-ai-trends/
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