Oracle is making a major move into AI, training its formidable marketing machine on the landscape in the wake of years of positioning by Salesforce and IBM, to name two competitors in enterprise applications and cloud services.
Step one: redefine the landscape in terms specific to Oracle
Salesforce calls it Einstein; IBM calls it Cognitive Computing. Now Oracle calls it “adaptive intelligence” – good because it can employ the familiar AI acronym, plus it implies something needs to be adapted, namely in this case, the suite of applications in the Oracle product line that now are moving to AI.
Oracle is infusing its lineup of software-as-a-service (SaaS) applications with machine learning and artificial intelligence to help move its customers into the new AI age. This is expected to lead to speed in innovation, the ability to disrupt competitors or industries, and the ability to stay in front of rapidly-shifting marketing dynamics, according to an account in Forbes.
Machine learning and AI are at the center of the effort by Oracle, and blockchain and IoT are also being embedded within all appropriate SaaS applications across the Oracle product line that covers ERP, Financials, CRM/Customer Experience, Supply Chain Management and Manufacturing.
This development is strategically important for Oracle, whose SaaS business is the largest component of its cloud portfolio, producing 74% of Oracle’s cloud revenue, according to Forbes. The SaaS revenues make Orale the world’s second-largest provider of SaaS applications behind Salesforce, and Oracle sees an opportunity to become the leading player.
Reasons include an anticipated $2 billion in new enterprise SaaS subscriptions in 2018, a total it believes no competitors can match; the breadth and depth of its product line giving it a scale beyond that of Salesforce; and a leadership position in the SaaS ERP Market, which Oracle founder and CTO Larry Ellison says is the largest SaaS category, once in which Salesforce does not compete.
In addition, Oracle is committed to its new breed of Adaptive Intelligence applications to create more personalized experiences for users based on the AI and machine-learning components that learn as they are used.
Tara Roberts, an Oracle vice president for AI applications, was quoted in Forbes as describing why Oracle’s Intelligent Commerce and Marketing applications provide an advantage. “The AI algorithms analyze data that retailers have gathered about their own customers, as well as data in Oracle Data Cloud, a collection of more than five billion consumer and business IDs and 7.5 trillion data points These algorithms create a continuous feedback loop that retailers can use to deliver their best product recommendations to customers. Retailers can then generate dynamic category pages that personalize search suggestion and present real-time offers to provide a “friction-free” shopping experience.”
Oracle sees an opportunity to provide similar capabilities across other segments of its SaaS product line: ERP, to handle supplier information and payments more effectively; Supply Chain Management to better automate complex and time-consuming demand processes; and Human Capital Management (HCM) to find best-fit candidates projected to be high performers.
The feedback loop will learn from increasing volumes of data to offer better and more optimized choices over time. Adaptive intelligence within the applications will over time be accessible in a variety of forms including chatbots video messaging and augmented reality.
Oracle EVP and head of applications Steve Miranda was quoted in Forbes as saying, “Two years from now, we’ll probably be talking about a whole new set of things in this category that none of us is even thinking about today.”
Oracle AI Platform Cloud Service
Oracle is offering the AI Platform Cloud Services to provide machine learning practitioners and data scientists with a fast way to set up an environment to work on machine learning in the cloud. Customers set up a managed image on Linux or Ubuntu that includes pre-installed tools for working on machine learning, such as frameworks for building and training advanced ML models. Practitioners can also experiment with new algorithms, use popular AI libraries, get access to best-in-breed BPUs for accelerated training, and gain access to existing data sources. Once the machine learning models are trained, they can be exported for use in applications, with assistance from Oracle’s application development services and tools.
Data scientists working in Oracle’s Machine Learning Research group have spent years devising advanced enterprise algorithms, as part of Oracles R&D investment. The researchers work to continually improve the instructions behind machine learning and add those capabilities to the Oracle Adaptive Intelligent Applications. These are AI-infused applications that adapt and learn from the data they process.
– By John P. Desmond, AI Trends Editor
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