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They have been able to dedicate the resources, teams and time to see AI use cases yield impactful ROI in the production stage.

No need for a large AI team as the off-the-shelf product can be installed & maintained without data scientists because different customers have varying data, constraints and decision-making processes, an AI solution that fits one customer will not necessarily fit another.

The data scheme and structure of customers is likely designed differently with varying features, distributions and data types, while also having missing values at varying places.

When deployed as an AI-as-a-Service solution, it’s possible to ensure that the solution will work in the long-term as data and requirements shift over time, while simplifying the implementation process for the customer.

This customization method is possible because there are often baseline commonalities when developing an AI solution for a problem that fits different customers.

There are core technologies that must be developed to create AI solutions that perform in production, such as technologies to handle extremely small amounts of labelled data, technologies to stabilize the system under the dynamicity of real-world data and technologies to meet regulations and create trust.

A customized method, when built correctly to retrain the capabilities for customer data, needs and constraints, provides the required amount of flexibility.

Building, buying & customization all have benefits depending on an organization’s size, leadership and core product offerings.

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