Why Adopting AI Beyond Tech Companies Can be Tricky and Hard?

Can AI be expensive for other industries?



Artificial Intelligence has been booming like never before. But AI is only used in the big tech companies as of now. However, it is not used outside the consumer internet companies. Here are the challenges that are commonly faced during AI adoption.


Small datasets

Most consumer internet companies have numerous users, engineers have millions of data points that their AI can learn from. In the case of other industries, the dataset size is a bit smaller. And so it becomes difficult for Artificial Intelligence to learn from.


Cost of customization 

The big internet services employ several skilled engineers to build and maintain AI systems that are capable of creating value in a specific field. But while coming to other industries, there are many projects each of which needs a custom of AI systems. And the value for this might be huge and sometimes it is not even possible to hire a large dedicated Artificial Intelligence team due to the finances. This is due to the shortage of Artificial Intelligence teams which further adds up to the costs.


Gaps in the process 

While an Artificial Intelligence system works in a lab, there is a need for engineers to be deployed there to monitor the process and production. And deploying the AI is not an easy task. It would take 12-24 months to get them ready to work. 

There is a need for a systematic approach to solving these problems across all industries. The one-step solution for this can be data centric-approach to Artificial Intelligence, along with tools designed for building, deploying, and maintaining AI applications they are called as machine learning operations (MLOps) platforms. With the help of this Artificial Intelligence can attain its full potential. The companies who adopt this approach can have an upper hand in the market.


Data-centric AI

AI systems are made up of software, the computer program that includes an AI model and data that is used to train the model. To build an AI system for automated inspection in sectors such as manufacturing, an AI engineer might create software and implement deep learning algorithms that can comprise pictures of good and defective parts for differentiating and learning differences between them. 

A lot of researchers driven by software-centric development have taken place over the decade. In this research data is fixed and teams attempt to optimize programs to learn from the data that is available.  Many big tech companies have used large amounts of datasets from millions of consumers to drive innovations of Artificial Intelligence. At the time of Artificial Intelligence talent storage, a data-centric approach can allow several subject matter experts to contribute to Artificial Intelligence development. 

Here are things companies are doing to bridge the gap that leads to deployment in production. 

  • Quality is prioritized over quality of data. 
  • Take a data-centric approach rather than a software-centric approach. 
  • Be sure to plan the deployment process and provide MLOps tools in support of it. 

Having a data-centric mindset along with tools can help the industry domain experts to precipitate in the designing and deployment of AI systems, ensuring that all industries can benefit from Artificial Intelligence technology.