Oracle has been at the forefront of innovation in (AI), machine learning (ML), and for decades. It’s in our DNA. Our team of innovative and groundbreaking leaders brings not only vast knowledge and experience but also constant curiosity and passion for learning. These dedicated visionaries collaborate relentlessly to deliver embedded AI and ML technologies within applications, databases, and cloud services. This “baked in” AI approach to technology allows Oracle to deliver efficiencies to business and IT organizations.
Meet two of our foremost innovators and learn how they’re changing the way Oracle and our customers operate thanks to AI and ML.
AI Operations: The foundation of a well-oiled machine
Sandesh Rao, vice president of AI Operations (AIOps), started off his IT career in Mumbai and experienced the city’s age of innovation first-hand. The fast-paced environment was well-suited for a person who is always learning or working on something new. That thirst for knowledge and innovation has also taken him to more than 700 cities around the world.
Now, he and his team are at the forefront of AIOps at Oracle HQ Redwood Shores. “At its most basic, AIOps boils down to not just the right hardware and software, but also the right team: developers and engineers with the skills and knowledge to integrate AI into existing company processes and systems ,” explained Rao.
This integration is key. A recent article by warned that AI models and one-time projects don’t fulfill the promise of AI: “It’s the well-oiled machine, powered by AI, that takes the company from where it is today to where it wants to be in the future.”
At Oracle, Rao’s AIOps team tools that optimize systems, minimize downtime, and are invisible to customers. Think of them as part workflow manager and part traffic cop. These tools are embedded within Oracle solutions to predict capacity, spot anomalies, troubleshoot problems, and detect compliance violations. This automation finds opportunities to update and repair systems without affecting customers or letting them realize there was ever a potential issue.
For example, one solution that Rao’s team has developed finds dynamic maintenance windows when patches can be applied or updates made without interrupting customers who use the system. The team uses analytics to predict usage patterns and pinpoints compatible users who can share services without affecting performance.
Solutions like Oracle Autonomous Database relies on AIOps to deliver a single converged, multi-model database. Its broad set of algorithms can operate with various data types and data models, improving your operations and security.
Prebuilt apps with a customer focus = A powerful combination
Miranda Nash, Oracle's VP of product management for AI Apps and a , thrives on a work ethic instilled by her parents: Do the hard, detailed work to get the results you want.
Nash immerses herself in customers’ worlds, defining solutions to meet their needs, such as touchless back-office transactions, seamless job candidate matching, and enhanced customer service through enriched data. By embedding AI and ML directly into Oracle applications, she and her team help relieve customers from the burden of development and finding data scientists, while giving them a competitive head start.
Organizations can speed time to value with prebuilt AI capabilities such as which are cloud-based and capable of adapting in real-time using embedded AI, ML, and internal and external data. Oracle’s applications span the entire suite across HCM, Finance, Customer Experience, and Supply Chain. An automated learning loop allows the solutions to iterate and improve with an overarching goal: deliver exceptional data insight value to customers, while making AI easy to leverage.
Create your future: Partner with Oracle
What Rao and Nash share is a commitment to increased customer excellence. Eventually, no matter what kind of data you have, Oracle AI will be able to apply the right algorithm and extract the value you need, Rao says. That’s the power of using collective knowledge to benefit all customers. According to , organizations who use AI in financial systems report an average 33% improvement in productivity and a 37% reduction in errors. Complex monthly financial closes take about four days less when AI is incorporated into finance systems.
Automated maintenance also takes less time and is integrated seamlessly into operations with little or no down time. Without that, customers would end up paying an “AI tax” to maintain their solutions or accept a progressive reduction in their effectiveness.
To keep an eye on what’s next in Oracle’s AI innovations, as well as new products, visit .