Imre Szenttornyay, CEO and Founder of Cielo helps franchise owners grow and scale their operations profitably using AI.
At one time or another, every business owner has wished they could have spotted an issue before it happened: Two kids colliding on a trampoline, a shoplifter taking flight or employees socializing more than supervising.
One technology solution is intended to provide this proverbial 360-degree, real-time view: computer vision.
Computer vision enables machines to interpret visual data instantly, mimicking human perception with greater accuracy and speed. This capability enables businesses to automate processes, identify patterns and make data-driven decisions, which can reduce operational risk, increase safety and boost profitability.
As CEO of Cielo, which offers computer vision technology to franchises, I’ve seen how many companies have used the technology to protect their property, their customers and their employees. In this article, I’ll explain the role that computer vision can play, how it could continue transforming operations in the future and what it takes to implement it correctly.
Is computer vision really changing the business landscape?
Put simply: Yes. Advancements in AI and increased demand across the manufacturing, retail, automotive and other industries are pushing the boundaries on what’s possible with computer vision.
As a result, Gartner projected that computer vision will “generate global revenue of $386 billion by 2031, up from $126 billion in 2022.” The integration of computer vision provides benefits for many industries, including these key advantages:
1. Enhanced Accuracy In Monitoring: Real-time monitoring powered by computer vision ensures compliance with safety protocols by reducing human error. I’ve seen fitness centers use computer vision to track where staff are following hygiene standards and to ensure that guests use equipment properly and safely. Similarly, many quick-service restaurants have deployed the technology to monitor kitchen processes and whether they meet food safety requirements.
2. Cost Reduction Through Automation: Automation can reduce labor costs and enhance operational consistency. In high-volume environments like convenience stores, computer vision automates inspection processes, so they require less human oversight. Stock replenishment is an area in which computer vision technology can help eliminate manual inventory counts and provide accurate comparisons of sales by SKU at each location.
3. Real-Time Risk Mitigation: In my experience, one of computer vision’s most compelling features is its ability to detect hazards or violations in real time. Maintaining a safe crowd density can be a significant challenge for many businesses. Computer vision can monitor crowds and alert employees if safety issues arise.
4. Streamlined Reporting And Documentation: Industries like quick service restaurants require detailed documentation. Computer vision can power automatic reporting. Employees spend less time reporting and more time selling, while business owners can count on compliance-ready records for audits or insurance claims.
How is computer vision evolving?
Emerging technologies like generative AI are expanding computer vision’s capabilities, pushing its application to new frontiers. Business owners should be prepared to see new features and functions from computer vision, such as:
1. Multimodal Features: Multimodal AI models integrate multiple data types, like text, images, audio and video. Gartner Research forecasts that “40% of generative AI solutions will be multimodal by 2027.” I expect computer vision to go along for the ride, integrating current data with other sensory inputs such as audio and touch.
2. Self-Supervised And Foundation Models: Computer vision has traditionally required vast quantities of labeled training data, which represents a significant barrier to entry for many businesses. These models are getting better at learning patterns from unlabeled data, reducing the administrative burden that comes with data labeling. Other foundational computer vision models will come to business owners pre-trained, so you can plug them into your business immediately and start reaping the benefits.
3. Real-Time And Edge AI Processing: Real-time computer vision processing will soon move to edge devices, like smartphones, security cameras, digital signage, etc., rather than the cloud. Moving processing to the device itself reduces the bandwidth each action requires, a crucial step for applications like smart surveillance, which needs the model to make decisions instantly.
How do you implement computer vision?
To successfully implement computer vision, organizations should consider the following best practices:
1. Assess organizational readiness. Evaluate whether existing infrastructure can support computer vision systems and identify areas where the technology can have the greatest impact.
2. Pilot and scale. Begin with small-scale pilots to identify challenges and refine implementation strategies before rolling out larger systems.
3. Train staff. Educate employees about how to work with AI systems to enhance adoption and reduce resistance.
4. Measure ROI. Track key performance indicators (KPIs) to measure the impact of computer vision on operational efficiency, compliance and profitability.
Conclusion
Computer vision has transitioned from a fringe, futuristic concept, yet many businesses are still behind the curve. By understanding where the technology is heading and the best practices to adopt it correctly, companies can better position themselves to succeed alongside this technology’s growth and advancement.
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