How to overcome Vision AI automation hurdles

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How can you overcome the hurdles and obtain real success in industrial Vision AI automation? If you’re struggling to scale Vision AI automation in industrial applications, you’re not the only one. Gartner’s statistics reveal the bottleneck: 54% of AI models are stuck in pre-production due to a lack of automated pipeline management. 

The only constant is change

AI-powered machines operate in environments that are increasingly subject to change: variations in product types, new quality requirements driven by regulation or policies, changes in the machine’s environment or lighting conditions, or the desire to track new types of defects, just to name a few. 

Change is constant in today’s production environment, and operator insights are invaluable. However, frontline teams aren’t AI automation specialists – and they shouldn’t have to be. That doesn’t mean you should have data scientists on the manufacturing floor to maintain AI models. Why not? Because it would create a large limitation on the scaling of Vision AI operations. Data scientists are hard to find as it is, and they prefer innovative roles to operational ones.

Operationalising Vision AI automation isn’t about AI

In order to operationalise Vision AI for industries, organisations should have a comprehensive understanding of how to build and support AI-powered products in a repeatable, timely manner to shorten the development, lower overhead, and reduce risks. 

To reduce risk, machine builders and their customers need a way to trust the performance of the AI-powered solution while operational. But currently, most solutions use software and product development practices that are not optimised for managing the continuous change throughout the AI lifecycle. 

Multiple, often unsupported tools are combined in an unstructured way to build and deploy models. The manual processes used to manage the lifecycle, introduce risk and make it very hard to scale across different machines, different solutions and different operating sites.

From concept to profit with Vision AI machinery

These challenges make machine builders worry and think that Vision AI projects lack revenue and profit predictability. It’s making them doubt their business cases before they even get started. What can I promise my customer? How much service will they need? Naturally, this is holding them back to design AI-powered solutions.

Standardised machines for customised operations

Every machinery customer expects a tailored solution, but the machine builder’s business model is mass production and minimal customisation. They want to procure components, set up assembly facilities, deliver to the plant and send the invoice. It’s very understandable: as a machine builder, you don’t want an extra customisation cost for every machine you sell. Or even worse, a continuous operational cost for every machine in the field.

So what machine builders need is a way to ensure their machinery clients can implement the customisations themselves. This enables them to play in the Vision AI-powered machinery market without hassle. The machine builders bring the machines, while their customers bring the data and the subject matter operational expertise. The AI software workflows turn the machinery  into intelligent solutions.

Vision AI automation solutions that scale

Robovision bridges the gaps with a platform where end-users can adapt AI models to deal with new circumstances. They can do so without constant involvement of highly skilled users like data scientists, software engineers or devops. Thanks to the platform, they are self-sufficient and remain in control of their business continuity. It also ensures that Vision AI solutions can scale, drastically reducing the machine builders’ time-to-revenue for AI-powered solutions, because they don’t need to customise the machine for each specific customer solution. 

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