Exploring Oracle's Unique Approach to AI and ML
- deeksha gupta
- Mar 5
- 3 min read
It's fascinating to delve into Oracle's distinctive approach to AI & ML, particularly as you enhance your understanding of Oracle's Modern Best Practices with AI in ERPM.
Key Highlights:
Oracle's strategic journey began by building everything on SaaS, evolving from classic AI to embedding generative AI, and now incorporating agent technology AI. Every Oracle initiative is infused with AI.
With Oracle's SaaS products, you gain access to 150 AI features and agents as part of your subscription.
Oracle stands out as the only vendor offering bare metal GPUs, crucial for training large language models (LLMs).
Oracle takes a unique approach to data processing by bringing the algorithm to the data, ensuring it operates directly where the data resides. This means data remains securely within private enterprise databases or systems of records, and client data is never used to train LLMs.
Oracle offers an AI database that can autonomously patch, load balance, optimise, and repair itself without human intervention.
Oracle's pre-trained AI services, licensed separately, power applications across PaaS, SaaS, and infrastructure.
Oracle is heavily investing in AI and is actively working towards building AGI (Artificial General Intelligence) to achieve human-level intelligence.
Notably, four of the world's largest LLMs, including X Dot AIS, Meta's Llama, Microsoft Bing, and OpenAI's ChatGPT, are hosted on Oracle's cloud infrastructure.
What Sets Oracle Apart in the AI and ML Landscape:
1) Cost Efficiency: Oracle avoids premium pricing for AI usage within the SaaS ecosystem, unlike some vendors which charges per AI use.
2) Single Platform Integration: Unlike many SaaS providers, whose AI extends across multiple platforms, Oracle offers a seamless experience on a single platform, eliminating the complexities of cross-platform integration.
3) Comprehensive Offerings: Oracle is unique as the only cloud vendor delivering infrastructure, agentic AI data, and SaaS applications at scale.
I have been exploring Oracle AI in Enterprise Performance Management (EPM) course and here are the key take aways:
Machine learning is invaluable for variance assessment, with tools like IPM Insights offering capabilities such as root cause analysis and visualisation.
When revising forecasts, ML-generated predictions (e.g., Predictive Planning, Predictive Cash Forecasting) can be reviewed for necessary updates.
Management reports can leverage AI-generated content for enhanced insight, facilitated by EPM Digital Assistants throughout various forecasting processes.
Efficient financial planning, aligned with strategic goals, is supported through capabilities like what-if analysis and predictive analytics, driving consensus and accountability.
Oracle's technologies empower financial analysts with predictive tools, improving forecast accuracy by identifying trends, anomalies, and correlations.
In validation, built-in time series regression techniques in EPM AI and ML toolsets enable scenario modelling, adjusting for market changes.
EPM digital assistants streamline data updates through conversational AI, enhancing efficiency during planning and budget collaboration.
Harnessing Oracle's EPM AI Approach with Modern Best Practices
In the realm of financial organisations, Oracle's EPM AI approach stands out, with the "Report to Forecast" process being a hallmark of best practice. It enhances efficiency for both the accounting and FP&A teams.
This process operates in a monthly cycle, commencing with the closure of the books and culminating in the generation of management reports for your finance executive team. By integrating ERP financials with EPM Cloud business processes, it ensures a seamless flow. The journey starts in ERP, while subsequent steps are executed within EPM.
Oracle's AI and ML capabilities are integral to this process. For instance, when projecting forecasts, analysts can utilise EPM's AI or ML tools such as Predictive Planning, Predictive Cash Forecasting, Auto Predict, or the Bring Your Own Model feature within Machine Learning Model and IM Insights. These tools accelerate analysis and unveil hidden trends, correlations, and anomalies.
Machine learning also plays a crucial role in variance assessment. IPM Insights excels in this area, offering root cause identification and visualisation tools. As forecasts are revisited, earlier ML-generated predictions serve as invaluable references for necessary updates.
Management reports are enriched by leveraging AI-generated content within narrative reporting, providing insightful presentations for management. The EPM Digital Assistant facilitates data viewing, report generation, and other routine tasks throughout the forecast to the OMBP process.
By understanding these powerful features and strategic approaches, you can appreciate how Oracle's AI and ML capabilities are crafted to drive transformation and create value for organisations across industries.
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