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How to Choose a Vision AI Partner for Your Visual Inspection Use Case?
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How to Choose a Vision AI Partner for Your Visual Inspection Use Case?

By October 23, 2024October 29th, 2024No Comments

Table of Content:

Introduction
What Can Vision AI Do for Your Visual Inspection Use Case?
What Are The Challenges of Building Vision AI Solutions In-House?
Why Do You Need an Expert Vision AI Partner?
How to Choose an Expert Vision AI Partner?
Conclusion

Introduction

Vision AI‘s visual data analysis at unparalleled speed and precision opens numerous opportunities for businesses to boost efficiency, refine operations, and achieve a competitive advantage. AI computer vision aids in spotting defects in products, spotting signs of illness in medical images for early diagnosis, confirming worker safety in workspaces, enriching customer experiences with customized retail engagements, and much more. The influence of AI Vision is unmistakable.

CxOs and senior decision-makers confront the question of adopting Vision AI, pondering not the “why” but rather the “how”. The focus is on the pathway to implementation. As the computer vision market surges, from $25.80 billion in 2024 to an estimated $46.95 billion by 2030, due to AI progress, critical fields such as image identification are projected for notable expansion. Image recognition, in particular, is anticipated to swell by 64%, reaching $22.6 billion by 2030, highlighting Vision AI’s potential to deliver significant commercial benefits across various sectors.

However, building and deploying robust Vision AI solutions in-house can be cumbersome and resource-intensive. This mostly will involve forming specialized teams that have expertise in data science, machine learning, and software engineering, apart from significant investment in hardware and infrastructure. That’s not every enterprise’s cup-of-tea!

This is where partnering with a specialized Vision AI provider becomes critical. With the right partner, you will rapidly accelerate your time to value and minimize risks while unlocking the most significant value of Vision AI in your particular use case for visual inspections.

However, from the great number of vendors offering everything from simple capabilities to full-blown solutions—like Google Vision AI and Azure Vision AI—choosing the right partner can be tricky. The following article will help you navigate how to choose a vision AI partner that aligns with your organization’s unique needs and goals.

Credit: Statista Market Insights
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What Can Vision AI Do for Your Visual Inspection Use Case?

1.Insurance

Historically, insurance adjusters tediously evaluated vehicle damages post-accidents, a process marred by human error. Vision AI, however, allows for rapid image assessment of damaged cars, pinpointing exact areas of damage. This expedites claim processes, slashing durations and boosting customer contentment. AI-led damage evaluation fosters uniformity and precision in assessments, which is advantageous to insurance firms and policyholders. It ensures equitable settlements, aligning benefits for both parties.

2.Manufacturing

Vision AI-driven solutions automate defect spotting on assembly lines, detecting tiny imperfections in goods or equipment that could evade human scrutiny. Real-time quality checks thus cut down on faulty outputs, decreasing waste and recalls. Furthermore, Vision AI employs monitoring to enforce safety standards by confirming that personnel are equipped with protective items, such as helmets or safety vests in hazardous manufacturing environments.

3. Healthcare

Vision AI systems analyze medical images including X-rays, MRIs, and CT scans for disease early detection, particularly cancer. These AI tools enhance tumour, lesion, or abnormality identification more swiftly and precisely than conventional methods. Automation of visual examinations boosts healthcare professionals’ ability to offer timely diagnoses and tailored treatment strategies, thereby improving patient results and minimizing diagnostic blunders.

4. Retail

Cashier-less stores, exemplified by Amazon Go, harness Vision AI to monitor customer transactions and product selection internally without checkouts. Shoppers go in, select items, and exit; the system then bills them. Vision AI bolsters personalized retail experiences by instantaneously evaluating customer actions and inclinations, enabling targeted product suggestions, precise promotions, and a more captivating shopping journey.

5. Telecom

Maintaining telecom infrastructure, particularly towers, poses challenges because of their heights in distant or risky locations. Vision AI streamlines the process by automating inspections, and utilizing drones or stationary cameras to evaluate structural soundness, spot damage, and pinpoint equipment failures. This AI-integrated method cuts down on the dependency of human operators for ascending towers, thereby boosting safety, and promoting quicker detection of problems.

6.Energy

Vision AI significantly impacts energy sector operations, notably through automation in wind turbine and oil/gas pipeline inspections. Given the remote placement of wind turbines, these structures need regular checks for natural wear, cracks, or corrosion. Employing Vision AI with drones enables thorough visual assessments, spotting issues early and preventing severity. Similarly, in oil/gas, Vision AI helps in the surveillance of pipelines for leaks, corrosion, or damages, enhancing safety and mitigating environmental risks.

What Are The Challenges of Building Vision AI Solutions In-House?

Talent and Expertise

One of the bigger challenges may be a shortage of AI talent. According to Thomson Reuters, there is a 50% hiring gap for all AI-related positions this year, while approximately 63% of IT decision-makers reported AI/ML as their biggest skills gap. Finding and retaining the right staff becomes a big challenge for companies in their quest to develop AI solutions internally.

Resource Constraints

Developing any kind of Vision AI solution takes extensive investment in data, advanced computing infrastructure, and a specialized team, far beyond the capacity of most organizations to efficiently allocate. This makes scaling AI initiatives difficult.

Longer Time to Market

Building Vision AI from scratch is going to considerably delay time-to-market. Complexities in the collection of data, training a model, and deploying it further extend the development timeline, thus keeping a company at a disadvantage compared to its competitors who can use readily available solutions.

Lack of Innovation and Agility

In-house teams can hardly keep pace with the rapid development and improvements in AI vision technologies. Without continuous innovation, in-house solutions tend to get outdated, which negatively impacts the ability to respond quickly in case the market demands something new or presents challenges.

Risk of Failure

Despite the urge to embrace AI, failure rates are high. According to The RAND Corporation, 80% of all AI projects fail—double the rate compared with non-AI IT projects. It underlines the risk involved in the investment of time and resources into developing an AI solution that may not yield the expected results.

Why Do You Need an Expert Vision AI Partner?

Access to Specialized Expertise

Expert Vision AI partners offer exclusive insight and years of experience in various industries that can bring a lot of improvement to your AI solutions. Their teams are well-versed in the latest algorithms, best practices, and industry standards—assuring your project benefits from proven expertise.

Faster Time to Market

With a dedicated partner, the time-to-market will be accelerated. They already have established mechanisms and frameworks in place, thus enabling them to roll out their solutions faster and more effectively to give your business an edge in the market.

Scalability

A seasoned partner will provide scalable solutions that grow with your business. In case of growing demands, they will quickly adapt and scale your Vision AI capabilities to meet such spikes in demand, making sure your technology stays current with the evolving needs of your business.

Cost-Effectiveness

Outsourcing can be considerably more cost-effective than trying to create an in-house solution. One can leverage existing resources, tools, and infrastructure that negate the need for considerable upfront investments in talent and technology.

Access to the Latest Tools, Tech, and Software

Your Vision AI partners will keep you updated with the latest technological advancements—be it in AI/ML/MLOps or DevOps. With them, you get access to the most recent tools, technologies, and updated software so that serving solutions are deployed for optimum performance.

How to Choose an Expert Vision AI Partner?

Demonstrable Expertise in Vision AI Solutions

Seek out partners showing a history of delivering Vision AI solutions. Evaluate their portfolios, read customer reviews, and check ratings to confirm their skills. Look for case studies that highlight successful projects to gauge their expertise and outcomes for previous clients.

Industry-Specific Experience

Select a partner well-versed in your particular industry. Their understanding of unique industry challenges and needs, such as healthcare, manufacturing, auto, or energy, ensures they can customize solutions that closely match your business objectives and goals.

Innovative AI Capabilities and Technologies

Assess the prospective partner’s technological competence and innovation capacity crucially. A robust AI collaboration partner needs to understand contemporary AI innovations, specifically advanced ML methodologies and CV platforms. Their mastery of such tools ensures an efficient and competitive solution.

Collaborative Approach and Agility

Choose a partner that values teamwork and showcases agility in their strategy. An ideal collaborator should closely work with your team, responding to feedback and adjusting to project alterations. Their adaptability remarkably boosts the development process, aiming for superior results.

Post-Deployment Support and Maintenance

Evaluate post-deployment support and maintenance provided by the partner. A trustworthy Vision AI partner should supply continuous assistance to tackle issues post-implementation, ensuring your solution remains optimal and adaptable to new technologies.

Conclusion

As you consider how to choose a Vision AI partner, it’s crucial to pinpoint an expert who comprehends your unique requirements. The key to success lies in opting for a partner boasting profound expertise, relevant industry experience, a commitment to innovation, a cooperative attitude, and dependable support. Aligning this choice with your organizational goals is essential. Accurately identifying an expert who grasps your specific demands is critical, especially in the fast-paced domain of Vision AI. By making a well-informed choice, you tap into the full capability of Vision AI, propelling efficiency and effectiveness across your ventures.

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Nitin
Nitin Tappe

After successful stint in a corporate role, Nitin is back to what he enjoys most – conceptualizing new software solutions to solve business problems. Nitin is a postgraduate from IIT, Mumbai, India and in his 24 years of career, has played key roles in building a desktop as well as enterprise solutions right from idealization to launch which are adopted by many Fortune 500 companies. As a Founder member of Pratiti Technologies, he is committed to applying his management learning as well as the passion for building new solutions to realize your innovation with certainty.

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