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.
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.
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:
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.
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.
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.
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.
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 in presenting an attractive picture of your idea and showing that it can be implemented on a large scale. Hiring generative AI consulting services for AI PoC development further increases your chances.
Like every other model and process, proof of concept is not without a few disadvantages. However, these cons can be overcome with careful planning and help from AI product development consulting companies.
The whole idea behind creating an AI PoC is to have a narrow focus on its feasibility and viability. However, it can also result in a limited scope, which doesn’t consider real-life scenarios and complications. So, even if the proof of concept gives encouraging results, the end product may not. This challenge can be addressed by implementing AI PoC best practices that allow you to consider the bigger picture despite having a narrow focus.
Any project requires resources, even if it is on a small scale. So, even a proof of concept for an AI product will need time, money, expertise, etc. These can drain your resources if you don’t have enough to spare. That’s why several businesses partner with top AI consulting firms for AI PoC development. The consulting company will use its resources to work on the project. You only pay for the service provided. Your employees can continue to work on their core activities.
Not every PoC can be scaled seamlessly, especially if you have to integrate it with third-party tools and software. Factors like compliance, upgrades, compatibility with hardware, and other performance issues may still arise once the product goes into full-scale development. Consider scalability along with feasibility when working on the PoC to overcome this challenge.
This is more of a strategic failure than a disadvantage of AI PoC. The strategy for proof of concept development has to be aligned with your business vision, mission, and goals. You cannot lose sight of what you want from the product and how it will help your enterprise and customers. To ensure this, partner with a service provider for AI business validation by defining the metrics, identifying the red flags, etc.
The right approach to AI PoC development can bring greater innovation, creativity, and flexibility to the enterprise. Hire generative AI consulting services from reputed companies and take advantage of their end-to-end solutions for developing proof of concepts and expanding them into viable and scalable AI products. Gain a competitive edge and enhance customer experience with your business.
AI product development is an end-to-end, tailored solution offered by offshore service providers to help businesses build, deploy, implement, and maintain diverse artificial intelligence tools and software. The process involves PoC, prototyping, and MVP development as well as long-term maintenance, support, and upgrade services to ensure consistent returns from the AI product. Partnering with the top AI consulting firms gives businesses a definite edge over competitors and strengthens their position in the market.
Check out the articles below to learn more about AI product development services.
A few successful examples of AI Proof of Concept are as follows:
You can start developing an AI PoC by following the steps below:
Once you have all the necessary information, discuss the details and make the final decision.
There are several benefits of AI PoC development, such as the following:
You can get a glimpse of the long-term ROI the AI product can generate and get approvals and investments in the early stages of development.
The following are some of the use cases for proof of concept development for AI implementation:
Many leading multinational organizations have launched AI products and implemented them on a large scale after a successful proof of concept.
Consider the below metrics for a successful AI PoC development:
Tracking these metrics ensures seamless AI project management by allowing you to make strategic adjustments whenever necessary.at you clarify which services are included and which will cost extra before you sign the dotted line.
Fact checked by –
Akansha Rani ~ Content Creator & Copy Writer
