Computer vision (CV) infrastructure can fundamentally change how firms perform tasks, automating manual work, closing safety gaps, and enabling real-time decision-making. However, not every team, project, or firm is a prime candidate for full-service computer vision infrastructure. Before making the leap to implementation, you must assess whether you are ready for such a transformation. This guide takes you through a structured framework to help decision-makers determine computer vision readiness.
Each step contains Yes/No questions to guide you to the next phase. By answering these questions and following the steps, you will gain an understanding of how close your organization is to leveraging computer vision effectively.
About us: Viso Suite is the end-to-end computer vision infrastructure developed by viso.ai for enterprises. With a platform that covers all stages of the application development lifecycle. Viso Suite makes it possible for firms to develop, deploy, maintain, improve, and scale their applications securely. To get started, book a demo with our team of experts.
This guide is developed in conjunction with our partnership with Intel. We’ve come together to make top-of-the-line computer vision infrastructure faster and easier to run. To learn more about combining power vision AI and Intel hardware devices, we suggest reading our Intel and viso.ai partnership blog.
Checkpoint 1: Aligning Vision and Business Goals
Define Business Goals
The first step is to clarify your business objectives for computer vision adoption. Do you have a clear vision of how computer vision aligns with your business goals?
| Yes, go to the next checkpoint | No, review business goals and come back |
Identify Use Cases
Next, you should identify specific use cases. Have you identified relevant computer vision use cases (e.g., facial recognition, people counting, analog instruments reading) that align with your industry?
| Yes, go to the next checkpoint | No, revisit use cases and then proceed |
Checkpoint 2: Assessing Technical Readiness
Evaluate Current Infrastructure
The first step is to clarify your business objectives for computer vision adoption. Do you have a clear vision of how computer vision aligns with your business goals?
| Yes, go to the next checkpoint | No, perform an infrastructure audit and proceed |
Checkpoint 3: Evaluate Data Strategy and AI Readiness
Analyze Data Availability and Quality
You need a solid data strategy to feed your AI models. Do you have access to the required visual data sets (digital images, videos), and is it of high enough quality for machine learning purposes?
| Yes, go to the next checkpoint | No, build a data collection strategy and then proceed |
Validate AI Capabilities
Check whether your team is prepared for the AI integration that computer vision demands. Do you have internal AI expertise, or will you need to hire or outsource AI skills?
| Yes, go to the next checkpoint | No, plan to upskill or hire talent before moving forward |
Checkpoint 4: Assess Scalability and Security
Plan for Scalability
As your computer vision project grows, your infrastructure must scale efficiently. Is your current infrastructure scalable to handle the increased data demands and real-time processing of computer vision?
| Yes, go to the next checkpoint | No, create a scaling strategy before continuing |
Ensure Security and Compliance
Security and compliance are critical when dealing with video data. Is your organization compliant with relevant data privacy laws (GDPR, HIPAA) and equipped with robust security measures (encryption, access controls)?
| Yes, go to the next checkpoint | No, review security and compliance needs and proceed |
Checkpoint 5: Team Readiness and Change Management
Evaluate Team Capabilities
Determine if your team is capable of managing computer vision systems and technology. Does your team have the skills and knowledge to deploy and maintain a computer vision infrastructure?
| Yes, go to the next checkpoint | No, build a training plan or consider external help |
Change Management
Introducing new technologies often disrupts existing workflows. Plan accordingly. Do you have a clear change management plan to help employees adapt to new workflows and technology?
| Yes, go to the next checkpoint | No, develop a change management strategy before moving forward |
Checkpoint 6: Perform a Pilot or Proof of Concept (PoC)
Develop and Test a Pilot
Before full deployment, it’s essential to test the technology. Have you identified a high-impact use case to pilot with clear success metrics (KPIs) for evaluating the test?
| Yes, go to the final checkpoint | No, design a focused pilot project before proceeding |
Checkpoint 7: Full Deployment and Continuous Improvement
Plan for Full Rollout
Once the pilot is successful, plan for a larger rollout. Do you have a structured plan for scaling the computer vision solution across your organization without causing major disruptions?
| Yes, go to the next checkpoint | No, develop a phased rollout plan before continuing |
Measure and Optimize
Optimization is a continuous process that ensures the long-term success of computer vision. Have you implemented feedback loops and performance monitoring mechanisms to continuously optimize the system?
| Yes, go to the next checkpoint | No, set up continuous feedback mechanisms and proceed |
What’s Next
Congratulations! If you’ve made it all the way to the end of our computer vision readiness assessment, you are ready for CV infrastructure implementation. The true value of visual artificial intelligence (AI) is just beginning to be unlocked, and we predict that in just a few years, innovation teams will need to meet business objectives through the use of computer vision.
Viso Suite infrastructure makes it possible for teams to swiftly and effectively implement large-scale AI systems that scale and evolve as their requirements take shape. By consolidating the entire machine learning lifecycle into a single interface, teams can produce and manage intelligent applications in one place. When run on Intel’s state-of-the-art hardware, Viso Suite makes it possible to carry out fast and accurate computer vision.
Get Started With Enterprise-Grade Computer Vision
To start using Viso Suite for your AI initiatives, book a demo with our team. We’ll discuss your use case and how Viso Suite can help solve it.
If you’re not yet ready for infrastructure implementation but are interested in learning more about the value of enterprise computer vision initiatives, we suggest reading:
- The Business Value of Computer Vision
- What Does Computer Vision Cost?
- 100 High-Value Computer Vision Use Cases
Further Reading
We suggest checking out our other articles to learn more about the computer vision for enterprise implementation:
- Computer Vision in Healthcare: Common Applications
- A Rundown of Convolutional Neural Networks (CNNs): the Basis of Many Computer Vision Models
- A Guide to Computer Vision Tasks
- Image Classification
- Object Detection/Object Recognition
- Image Segmentation
