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Artificial Intelligence has transformative impacts on businesses in diverse industries. If you are ready to take a plunge into AI solutions, you probably are impressed by the personalization it offers, the conversational systems, and the predictive analytics. 

And you can leverage any and all of these benefits once you choose to deploy an AI solution into your business model. However, the market has ready-made AI products, plus, it has machine learning algorithms that allow you to create custom AI products. Which one would you choose? 

 

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Ready-to-Use AI Products

Ready-made AI products started getting traction when non-tech businesses identified their potential and realized there was no need to invest heavy IT budgets to build such solutions from scratch. 

For reference, AI has subsets called image recognition, natural language processing, voice recognition and automated conversations. Most of these functions can be performed with tools like ready-made chatbots.  

Pros

  • Ready-made AI products offer high-end business experience. Solutions especially made for analytics have accurate results and high-quality prediction capabilities. 
  • Ready-to-use AI solutions have absolutely zero development cost. you don’t have to engineer the product in-house or feed it with chunks of big data. Plus, your IT staff won’t have to test it for accuracy and performance. 
  • These are easy and ready to deploy. Most ready-made AI solutions only need you to sign up, pay a subscription and you can start using. 
  • You don’t have to maintain the AI product. The software company that produced and launched the AI solution keeps updating such programs themselves. 

Cons 

  • Ready-to-use AI solutions have very little room for data customization. So if your business needs advanced analytics or has a specific audience to cater to, ready-made AI won’t be the right fit. 
  • You may have to pay a high subscription cost if your data analytics or machine learning training is of high magnitude. 

 

Custom-made AI Solutions

Custom AI solutions require businesses to create their own, tailored machine learning algorithms. Companies that use custom-made AI solutions have specific types of data and a target audience that uses those predictive analytics. These are ideal for businesses in industries like: 

  • Transportation (to create an automotive technology solution, a traffic optimization tool with transportation data)
  • Healthcare (to create electronic health records and diagnosis research with huge biomedical data)
  • Real estate (to create local and international property databases for automated mortgage calculations)

As a big business that relies heavily on data, you can choose to create these machine learning algorithms in-house or outsource the task to specialized companies. 

Pros 

  • A custom AI solution gives you full control and flexibility over data customizations. You can add or remove features depending on the use cases of your business. 
  • You pay for developing an AI solution and its maintenance while you keep using it. There are no hidden fees or subscription charges, unless you are using a lot of APIs to boost the functionality of your solution. 

Cons 

  • A small custom AI solution can be built and trained within 3 months. But bigger solutions can take even 2 years to be fully capable of giving accurate analytics. 
  • While a custom AI solution might be the need of time for your business, they need a heavy development budget. 

 

 

While custom AI is a great option of tech businesses that utilize data for core decision-making, there are decent ready-made AI products smaller businesses can deploy. But if you still find yourself debating on the use cases, know that there are different factors that will help you make an informed decision. Also, you can choose to go for bespoke AI solutions that strike the right balance between customization and ready-made. There are platforms that have backends, dashboards and tools for analytics, but they give you full control over data feeding and customization. 

 

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