AI PoC Development: The Good, The Bad, and The Essentials
AI PoC development is an important part of creating a new and powerful AI product aligned with business and customer expectations. Here, we’ll discuss the pros and cons of AI proof of concept development and ways to ensure success. With artificial intelligence becoming an integral part of organizations, there is a high demand for AI-powered tools, apps, software, etc., for internal and external purposes. Businesses want to quickly build and launch AI products to attract more customers and/ or increase overall efficiency. According to Grand View Research, the global AI market is expected to be $1,811.75 billion by 2030 with a CAGR (compound annual growth rate) of 35.9%. However, building an AI product comes with certain risks and challenges. Not every idea is feasible and will convert to a worthy and useful AI product. Investing heavily in a project that may or may not deliver the expected results can lead to losses and other complications for your business. This can be handled by developing an AI PoC (Proof of Concept) that shows whether or not the idea is viable. In fact, AI PoC development has many benefits. Of course, it is not without a few concerns, but you can overcome them by partnering with an AI product development company. In this blog, we’ll read in detail about AI proof of concept and its pros and cons. What is AI Proof of Concept Development? Artificial intelligence proof of concept is the smart way to assess whether an idea or design is suitable for full-scale implementation. It is a strategic move to work on a mini project to test the waters and determine the chances of success. Proof of Concept is a part of the AI development process, though it doesn’t have to be a compulsory step. However, it is worth going for a trial phase before investing a heavy amount into an extensive project. According to the latest IDC report published in the Lenovo CIO Playbook, 88% of the AI PoCs did not make it to full-scale deployment. The report says that only four in every thirty-three AI proof of concepts were feasible enough to be final products. This clearly shows the need for AI PoC development, the gap between ideation and viability, and the lack of readiness in the business due to various factors. For example, you want to offer an AI chatbot for customers to get personalized recommendations and advice for their queries. You want the chatbot to specialize in giving accurate medical advice. But can you be sure of the results? What if it doesn’t work? How do you assess if it is feasible? How do you convince the top management, stakeholders, or investors to allocate budget and resources to this project? That’s where PoC implementation enters the picture. With an AI proof of concept, you test the feasibility of the idea using minimal resources and a small team. You identify the risks, choose datasets to train the chatbot, select the best tools and technologies to build the AI chatbot, and decide if the project can be scaled or if it should be scrapped. You use the AI PoC model to convince the investors that the AI chatbot solutions for personalized medical advice (as used in the example) will be a success. Collaborating with experienced AI consultants enhances the advantages of proof of concept development and minimizes the disadvantages to increase the success rate of converting the PoC into a full-scale AI product. Pros of AI PoC Development Proof of concept can be a vital part of the AI implementation roadmap if you are not sure about the complexity and want to get answers regarding its feasibility, technology, data, and other factors. Here are a few important advantages of AI PoC development: Risk Mitigation The biggest reason to go for PoC instead of full-scale development is to reduce the risk of complications and losses. There’s no point in committing your resources to a project that could eventually fail. With PoC, you can validate the feasibility of the AI product, create a robust generative AI strategy, and decide if you want to continue with it. It is like how you test drive a car to check if it meets your requirements. Focused Approach AI PoC has a narrow focus, allowing you to pay attention to the more important details instead of considering everything related to the project. Since PoC is one of the initial stages of AI development, it ensures you understand how the product will turn out, what it can do, how to improve it, what problems might arise, and so on. Furthermore, it allows focused innovation, where you can use cutting-edge AI technologies and tools to build the product. Detecting Problems Early No product will be perfect and flawless from the first step. In many instances, the problems appear during the production and implementation phases. This can lead to higher expenses and delays. However, with AI PoC development, you can detect potential problems early and find solutions for them. For example, during the PoC development for a machine learning prototype, you can evaluate if the data used for training is the right choice and if the output is aligned with your expectations. Ensuring Data Readiness Data is vital for AI product development as the tools have to be trained on large datasets to perform the required actions. But how will you know if the data used for training is of high quality, unbiased, and relevant to the project? Proof of concept helps by showing the results of using the selected data for the purpose. You can measure if the data is clean and AI-ready or if you have to process it further before using it in the project. Get More Support and Funding When you show early results and positive updates for the AI project through PoC, you can attract more investors and like-minded people to support it. Whether you want stakeholder support or outside investors to fund the project, the results of AI PoC can help
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