The time for computer vision adoption is now

Top five takeaways (and why they matter)

1. Computer vision isn’t magic: but the results and impact to your business can be magical. By turning cameras into intelligent tools, computer vision allows businesses to monitor, analyse and act on new data in ways once out of reach. Whilst it doesn’t initially fix problems directly, it consistently and rapidly reveals what’s really happening, so you are empowered to take action.
2. Start small (and choose something meaningful). A narrow, well-scoped use case like detecting forklift near-misses can deliver fast ROI and real safety impact.
3. Your existing infrastructure provides you with a great starting point. Getting hardware up and running – or efficiently using what already is in place – unlocks the ability to scale rapidly.
4. Success comes from iteration, not perfection. You don’t need to wait six months to launch something “perfect.” Going live with your first application gives you a true insight into what iterations are needed then you can improve it with real-world data.
5. Tackle today’s challenges while preparing for tomorrow’s needs. Are you building one simple application that will enable you solve for the challenge you have right now?  It is likely that you will be able to repeat that across multiple sites and reach fast ROI and then consider more applications as you identify additional needs.

Setting the scene: why computer vision, and why is the time for computer vision now? 

Computer vision enables and unlocks changes and improvements at speed. It is transforming visual data into actionable insights, and we are starting to see more rapid adoption. This is being driven by two forces:

Automation goals

Businesses are driving AI adoption to improve automation and see computer vision as a way to automate manual oversight tasks.

Underused infrastructure

Businesses have already invested heavily in large camera installations for CCTV purposes and now want to do more with the wealth of data that they are already collecting.

As hardware costs drop and AI literacy rises, computer vision is more accessible than ever, especially for organizations ready to solve targeted, operational challenges. However, whilst it isn’t a silver bullet (yet), it is a tool for gathering insights first, rather than a quick fix. Crucially it gives change-makers the data they need to act smarter and faster. Let’s look at three real-world use cases. 

Real-world use cases: from factory floor to warehouse 

Quality assurance (QA), parcel, package and pallet scanning, and Health and Safety monitoring are three impactful and interesting real-world use cases.  

By implementing computer vision, a significant impact on the business is achieved. This is especially true for the manufacturing industry.   

Quality Assurance (QA)

‘A manufacturer optimized machine processes, but human error was still a risk.’ 

Computer vision now monitors workbenches 24/7 and alerts QA teams of process deviations instantly – boosting both quality and compliance. 

Detecting defects in real time can transform quality assurance by identifying issues as they happen. Using advanced computer vision, it monitors and spots imperfections that might escape human eyes.  

By addressing defects immediately, you can see reductions in waste and enhanced consistency, delivering consistent quality.

Parcel, package and pallet scanning

‘In logistics, spot checks on damaged parcels, packages and pallets, for example, just weren’t cutting it.’ 

Computer vision facilitates the quality of goods throughout the supply chain. With AI camera systems detecting anomalies automatically. The ability to auto-scan parcels, packages and pallets, for barcodes, condition and patterns in damage, is truly a game-changer.   

Health and Safety Monitoring

‘Health and Safety managers can’t be everywhere.’ 

Workplace safety continues to be a major concern in manufacturing, with accidents leading to costly downtime, regulatory penalties, and reputational damage. Manual monitoring and periodic safety audits are often insufficient, leaving hazards unnoticed until they result in incidents. 

Computer vision systems detect PPE compliance, risky behaviours, and even near-misses. This in turn gives safety leaders a window into what is actually happening, not just reported incidents. 

Implementing computer vision: where best to start? 

At viso we always recommend keeping it simple. 

Start small, but with impact. 

Choose a use case with clear business value – think forklift near-miss detection, QA automation, or PPE detection.  

Ask yourself: 

• Do you know how many incidents or process errors you currently have? 
• Can you quantify their cost and impact? 
• What visual inspections are people doing manually? 

Make use of what you already have. 

If you’ve got cameras or a video management system (VMS), use them.  

You might need to reposition a few or upgrade resolution but it’s cheaper and faster than starting from scratch. 

The first step is data collection. 

Whether using existing footage or capturing new streams, you’ll create a dataset. Think of it as a curated photo album with examples of what you want the model to learn. 

Choose the right tech: think ‘flexibility first’ 

Edge devices are the best choice for performance and privacy. Therefore, when choosing hardware, consider: 

• Environment: factory floor or climate-controlled? 
• Cameras: how many streams will it handle? 
• Budget: what’s your cost envelope? 

Entry-level setups can start at $5-8k for a single location but really are dependent on every situation.  

Software flexibility is critical. 

Your first model won’t be your last.  

Choose a platform that lets you iterate fast and adjust as your needs grow. Scaling successfully from pilot to production depends on it. 

From pilot to production: what to expect 

Scope it tight.

Your first pilot should go live in 6–8 weeks. Get one location working, then scale. 

Retraining models is fast.

Adding a new location? Re-training a model can take as little as 2–3 weeks. 

Iterate to improve. 

Don’t aim for perfection. Get real-world feedback, spot trends (e.g. peak times for errors), and evolve your processes. 

Avoid the #1 mistake: waiting. 

If you’re unsure, just start! Put up a camera and start by collecting a single day’s worth of data. You’ll quickly see what’s feasible. 

Planning for scale and success 

Once your pilot delivers value: 

• Extend to new sites with minimal model tweaks 
• Build additional use cases in the same location 
• Bring in your data team to find patterns and inform operations 

Additionally, when choosing providers be wary of “instant out-of-the-box” claims.
Real-world success always involves some fine tuning. 

Final thoughts: computer vision is ready – are you? 

From quality to safety to logistics, computer vision is proving its value.  

What’s exciting isn’t just the tech, it’s the mindset shift: that seeing everything, all the time, leads to better decisions, safer environments, and smarter businesses. 

And if there’s one final takeaway: remember, the time for computer vision is now.

It really is simpler than you might think. 

Start small. Get moving. The value is real.. and best of all, it’s ready now!