AI Becoming a New Battleground for Oracle and Salesforce

  • 4-May-2017

Analytics, fueled with Artificial Intelligence and Machine Learning, has become an intense focus of among big software vendors. Although IBM, Amazon, Google, and Microsoft rightly see a lot of coverage here, there's an intriguing competition brewing on the CRM side between long-time competitors Oracle and Salesforce.

Oracle

Oracle recently released “Oracle Adaptive Intelligent Apps” to provide capabilities for data analytics. Part of Oracle’s AI and Machine Learning play, they will use data and features from Oracle’s Data Cloud, which hosts huge amount of 3rd-party data. In fact, to quote the vendor, "Oracle Data Cloud gives marketers access to 5 billion global IDs, $3 trillion in consumer transactions, and more than 1,500 data partners available through the BlueKai Marketplace”.

Essentially, it’s a mashup of Oracle's own data along with 3rd party data from Oracle Data Cloud and then applying machine learning and AI on top of that.

Fig: Oracle plans to release several such apps across Marketing, Sales and Service. Source: Oracle

As an example, for sales-based scenarios, there are apps for "Offers" and "Next-best Action" use cases. Oracle has also introduced voice-driven virtual assistants that can nominally automate several routine tasks.

Salesforce

Salesforce is also strengthening its AI and Machine Learning play. Termed Salesforce Einstein, the underlying technology uses artificial intelligence, machine learning, and natural language processing to automate and offer new capabilities (e.g., new ways of lead scoring). It has just been introduced and should gradually find its way across different Salesforce Clouds.

IBM and Salesforce also announced a partnership between Watson and Einstein.

What does it mean for you?

This technology certainly has potential for several scenarios. But these are early days and it will be a while before it becomes mainstream.  Nearly all the vendors are still relying on early adopters to perform (read: fund) applied R&D here.

If you are evaluating these type of technologies,be prepared to undertake a lot of experimentation and learning. Don’t buy into the hype, and instead start small, with practical projects (like automating customer service requests for, say returning help manuals).

 

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