Since ChatGPT burst onto the scene, most people have seen how AI can understand and generate text. Fewer people know that the same leap is currently happening in vision: computers can now make sense of images and video, effectively giving software a pair of “eyes” when connected to a camera.

This capability, called computer vision, powers practical applications across manufacturing, logistics, retail, healthcare, construction, agriculture, and more. These modern vision systems are driven by large “foundation models” trained on vast amounts of image data, similar in spirit to large language models. That means faster deployment, more accurate results, and the ability to learn new tasks with less labelled data. 

In this article, we explain computer vision in plain language, outline the key tasks it can perform, show where it delivers real value, and provide practical guidance for moving from pilot projects to full-scale production – safely and responsibly.

What is computer vision? And why now?

Computer vision is software that looks at photos or video, and answers questions about what is there. Sometimes the question is simple, “Is there a box?”, and sometimes it is more involved, “How many boxes are on the conveyor, and does any of the boxes contain a defect?” Vision can run on live video, recorded footage, or still images.

Computer vision has been around for decades, but what makes it powerful today is how recent advances have made it practical for real-world industrial use:

• Better algorithms: Vision foundation models (for example, CLIP for image-text alignment and self-supervised models like DINO) give computers a strong “visual prior” that quickly allows us to teach the models to understand a new problem.
• Cheaper, better hardware: High-quality cameras are everywhere. From the smartphone in your pocket, CCTV, industrial cameras, and drones. This combined with edge devices that can run real-time AI models makes computer vision more accessible than ever.

What eyes like these can see – and make possible

Most computer vision systems are built around a handful of core skills. The first is classification, which determines what an image shows at a high level, such as “Type A vessel” versus “Type B,” or “defect” versus “no defect.” When you need to locate objects and count them, detection and tracking step in, identifying each item and following it over time to measure flow on a production line or movement in a warehouse. If there is text in the scene, OCR (optical character recognition) reads it – from lot and batch codes to meter readings and price labels.

For precise inspection, segmentation outlines the exact shape of an object down to the pixel, helping detect surface defects, cell boundaries, or lane markings on a road. When space matters, 3D reconstruction and SLAM build a digital model from ordinary video while tracking the camera’s position within it – useful for digital twins, as-built vs. as-designed checks, and autonomous navigation. And when movement or posture is key, pose estimation tracks points on the human body (or articulated machines) to assess ergonomics, safety, or performance.

In practice, these skills are often combined. A packaging line might use detection plus OCR to count items and verify labels. A construction team might pair 3D reconstruction with detection to compare site progress against plans. A warehouse may mix tracking and pose estimation to reduce risky lifts while improving people flow.

The state of computer vision – from AlexNet to everything

It has been ten years since deep convolutional networks such as AlexNet first outperformed handcrafted pipelines on ImageNet. This breakthrough opened the door to industrial quality control and autonomous driving.

In 2020, Vision Transformers (ViTs) arrived, replacing local convolutional filters with global self-attention. When pre-trained on massive collections of unlabelled images, ViTs learn both fine-grained detail and global context, matching or surpassing CNN accuracy while adapting to new tasks with minimal data.

Today’s foundation models – CLIP, DINO, Segment Anything, Stable Diffusion – share one trait: they learn generic visual concepts that can be prompted, queried, or fine-tuned for almost any downstream use case. For your business, that means:

• Less labelling: One or two hundred high quality annotated images are often enough to reach production accuracy.
• Faster iteration: New inspections or SKU classes can go live in days, not quarters.
• Multimodal workflows: You can ask a vision model questions in plain language, “Show me every missing weld from yesterday’s night shift,” and receive a video clip plus counts.

Pixels to profit – Six areas where vision pays 

We now know what vision can do and why the timing is right. The next question is simple: where does it show up in the P&L today? The examples that follow are production deployments, not lab demos. They translate pixels into throughput, yield, safety, and working‑capital gains across core industries.

As such, these examples can be seen as patterns you can reuse. Anywhere people currently inspect, count, or verify, computer vision can help reduce latency and variability – provided detections trigger action within the system of work. Simply put, when a model can halt a robot, quarantine a vial, issue a restock, or reconcile inventory automatically, images become outcomes.

Your 2025 computer vision playbook

Turning those outcomes into your outcomes is a delivery challenge, not an algorithm hunt. Treat computer vision as an operations programme anchored to KPIs the business already trusts, and design it around real-world constraints: your cameras and lighting, your networks, and your privacy rules. 

Foundation models and edge compute mean you can move from camera to KPI in weeks, not quarters. Start small and at the edge, fine-tune rather than reinvent, and measure model quality and business impact side by side. Do the governance work up front – legal, security, and works councils – so a successful pilot can scale without rework. After 90 days, the goal is one live use case with a signed-off ROI and a funded backlog to expand.

Eyes on the future

By 2026, computer vision will feel less like a patchwork of point solutions and more like a single, omnilingual colleague. Foundation-scale “PerceptionGPT” models will natively juggle pixels, LiDAR, radar, and even text, giving every camera a plug-and-play “one model, any modality” brain. Thanks to efficiency breakthroughs such as Token-Merging Vision Transformers and Flash-Attention v3, the same intelligence will run locally and fast – halving latency, cutting VRAM use by 60%, and fitting into sub-2-watt AR glasses without melting the wearer’s ears.

Regulation will tighten in parallel: the EU AI Act is set to require auditable lineage for every training frame, likely spawning an entire data-provenance sector to track who captured what, where, and when.

And because these systems will finally “speak human”, a plant manager will be able to ask, “Show me every conveyor jam from yesterday,” and receive clipped highlights and anomaly stats instantly – no SQL, no Python, just answers. In short, vision tech is about to become native, portable, accountable, and conversational all at once.