In the context of contemporary applications, it’s hard to think of an application that doesn’t use a database. From mobile to web to the desktop, every modern application relies on some form of a database. Some apps use flat files while others rely on in-memory or NoSQL databases. Traditional enterprise applications interact with large database clusters running Microsoft SQL, Oracle or DB2. Irrespective of the kind of database, the fact is that every app needs it.
Like databases, Artificial Intelligence (AI) is moving towards becoming a core component of modern applications. In the coming months, almost every application that we use will depend on some form of AI.
Artificial Intelligence is all set to become the new database for the next generation applications.
Here are three steps to start AI-enabling enterprise applications.
Step 1 – Start Consuming Artificial Intelligence APIs
This approach is the least disruptive way of getting started with AI. Many existing applications can turn intelligent through the integration with language understanding, image pattern recognition, text to speech, speech to text, natural language processing, and video search API.
Let’s look at a concrete example of analyzing the customer sentiment in a contact center. Almost all the inbound calls to the contact center are recorded for random sampling. A supervisor routinely listens to the calls to assess the quality and the overall satisfaction level of customers. But this analysis is done only on a small subset of all the calls received by the call center. This use case is an excellent candidate for AI APIs. Each recorded call can be first converted into text, which is then sent to a sentiment analysis API, which will ultimately return a score that directly represents the customer satisfaction level. The best thing is that the process only takes a few minutes for processing each call, which means that the supervisor now has visibility into the quality of all the calls in near real-time. This approach enables the company to quickly escalate incidents to tackle unhappy customers and rude call center agents.
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