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What You Should Know Before Investing in Computer Vision

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Today, every business is a data business, so you’re probably collecting a lot of information from disparate websites, apps, systems, etc. To make use of this information, you need to help computers understand what they’re seeing through computer vision.

Computer vision models use digital images, videos, and other visual inputs to “infer” meaning, which can then be applied to a number of business problems. For instance, some computer vision projects include video surveillance, medical imaging, and more. KUNGFU.AI worked with a client that used computer vision to identify potential vehicle collisions.

If you want to use your data to its fullest capabilities, then you’ll want to invest in computer vision. But how do you determine if it’s the right solution? And how do you ensure success? Although computer vision models improve accuracy and reliability and boost your capabilities, training them can be expensive and time-consuming. You’ll want to make sure you’re making a sound investment before getting started.

6 Questions to Ask Before Starting a Computer Vision Project

To determine if computer vision is the solution to your problem, we recommend you spend time thinking about the following questions with your team:

• What challenge are you facing?

• If the challenge were solved, how would your organization benefit?

• Do you have visual data associated with the challenge?

• If not, do you have the means for capturing this visual data?

• How are employees attempting to solve the challenge today?

• Is an off-the-shelf solution available, or do you need a custom solution?

Once you answer these six questions, you’ll know whether a computer vision solution is relevant, viable, and valuable. From there, you can start the project planning process.

Connecting a Computer Vision Project to Business Objectives

In our experience, computer vision projects must be tied to business objectives to be successful. Objectives provide direction. Otherwise, projects might be slow in bringing about results, and those results might not be what you intended. What’s worse, funding might be pulled if that happens.

The objective you identify will inform decisions around your computer vision project. Get it wrong, and you could squander the opportunity to leverage business data.

For example, let’s say you want a computer to automatically detect product defects during the assembly process. You should consider how much money you lose annually in wasted resources to make sure this is the best computer vision project to invest in. If wasted resources are a significant drain on your revenue, then the computer vision project is tied to the business objective of improving profitability.

Exploring Business Use Cases for Computer Vision

KUNGFU.AI provides model development and model implementation, coupled with strategic input, to help businesses maximize the value of their digital images and videos. It all starts with pinpointing the unique challenge you’re facing. From there, we assess your tech stack, team, and goals before building a model that turns data into a solution.

Computer vision projects require a higher investment of time and money initially, but they provide long-term ROI. Additionally, these services can help you offer better products and services, as computers generally make fewer errors, require less downtime, and increase overall operational efficiencies.

If you’d like to learn more about how computer vision can solve your business challenges, download our whitepaper today.

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