The Artificial Intelligence Capabilities of Oracle EPM Cloud and Oracle Analytics Cloud: A Comprehensive Overview

Organisations today are increasingly adopting Artificial Intelligence (AI) and Generative AI to optimise their processes, improve decision-making, and derive actionable insights from their data. This whitepaper focuses on integrating AI technologies into Oracle's Enterprise Performance Management (EPM) suite and Oracle Analytics Cloud (OAC) platforms. It explores the transformative capabilities of AI-powered features like the EPM Digital Assistant, Intelligent Narrative Generation, Auto Predict, Insights, and Machine Learning (ML) model import in EPM. Additionally, it discusses OAC's advanced AI capabilities, including Natural Language Processing (NLP), Automated Charts and Recommendations, and Natural Language Generation (NLG). With these advanced technologies, organisations can increase efficiency, foster innovation, and unlock new opportunities for growth and success.

Deep Dive: Everything you need to know about Oracle Intelligent Performance Management (IPM).

Introduction

In the past few years, Artificial Intelligence (AI) has become a game-changer in enterprise software. It offers unprecedented opportunities to revolutionise traditional business processes. Oracle, a global leader in cloud applications and platform services, has been at the forefront of integrating AI into its EPM and Analytics solutions. Using AI-driven features and capabilities, organisations can optimise performance management, enhance data analytics, and make improved strategic decisions.

Oracle has also entered a strategic alliance with NVIDIA, the frontrunner in producing best-in-class GPUs critical to AI and ML-based model runs on large datasets. You can read more about this partnership, its importance to finance organisations, and their transformation agenda in our Revvence blog post here: How Oracle and Nvidia's Partnership Gives Oracle Financial Services Clients an AI Advantage.

The following section highlights the critical AI and ML features in Oracle's EPM and Cloud Infrastructure offerings.

To gain a deeper understanding of how these features can be used to transform finance organisations into NextGen business partnering teams that transition to AI and ML-based auto-generated forecasts and plans and work as partners with the business in strategy creation and profitability uplift, please Follow our Blog (👆🏻) to receive our paper on ‘Continuous Planning for NextGen Finance Organisations’.

AI Features in Oracle EPM

EPM Digital Assistant

The EPM Digital Assistant is a tool built on the Oracle Digital Assistant (ODA) platform that offers a new way for users to interact with and complete tasks related to Enterprise Performance Management. Using natural language conversations, users can easily accomplish various EPM business processes such as Account Reconciliation, Financial Consolidation and Close, Tax Reporting, Planning, and Planning Modules. The Digital Assistant is like the next generation of SmartView reporting, which allows finance leaders to access ad-hoc reporting without needing to build complex reports and boilerplates.

Using Digital Assistant, your CFO can simply type in a query like, "What is the return on equity (ROE) for Product A among the 18-25 age group in Europe, and what is the difference compared to our latest forecast?" The Digital Assistant will respond concisely and comprehensively, reducing the need to generate bulky reports.

Intelligent Narrative Generation (Narrative Reporting)

Oracle's Intelligent Narrative Generation feature automates generating narrative reports based on predefined conditions. It creates natural language narratives tailored to user language preferences, resulting in more efficient and effective reporting. When integrated with IPM Insights, this feature ensures consistency across the EPM Cloud, making proactive reporting and analysis easier.

Narrative reporting adds natural language generation to your reporting framework, eliminating the need for manual commentary on automated reports. This approach results in end-to-end reporting automation without requiring manual input from subject matter experts. Additionally, it identifies anomalies in data and provides narration that automatically explains the reason for the anomalies. This capability helps finance colleagues to analyse and report on large datasets more quickly and accurately, eliminating the possibility of human error.

Auto Predict and Insights

Auto Predict is a powerful tool that can help users in the planning process and improve forecast accuracy by automating predictive capabilities. With Insights, powered by financial pattern recognition, users can automate insight discovery, uncover trends, anomalies, and forecast bias in their data, streamline data analysis, enhance forecasting accuracy, and strengthen decision-making.

Some finance organisations may be unsure about utilising certain features for forecasting purposes. Therefore, we want to emphasise the significance of carefully configuring Auto Predict, allowing prediction to improve over time and generate more accurate predictions. For starters, we suggest using Auto Predict and Insights as challenger models that are automatically created overnight and become a part of standard variance analysis reporting. Additionally, automatic journal posting mechanisms should be created to automatically act on the variance analysis and bridge any gaps. These journals will then feed subsequent predictions to the Machine Learning algorithm, allowing it to understand anomalies and create better forecasts for the future.

Machine Learning (Bring your own ML)

The Machine Learning Model Import feature enables business users to perform What-if-Analysis using trained ML models imported into EPM applications. With this feature, organisations can import fully trained ML models and deploy them within planning applications. This capability enables them to use advanced predictive modelling techniques to generate more accurate forecasts and make better business decisions.
The Bring Your Own Model (BYoM) feature provides endless possibilities. We recently used this feature with a client to train an R-based model to evaluate the cumulative impact of multiple macroeconomic variables on P&L account lines across products and client segments by region. Based on this approach, the result was near-accurate predictions of the modelled P&L forecasts for the product portfolio.

AI Features in Oracle Analytics Cloud (OAC)

Automated Charts and Recommendations

OAC's Automated Charts and Recommendations feature provides advanced analytics with just one click. It includes features such as quick forecasts, trend lines, clusters, and reference lines that can be customised to suit specific business use cases by adjusting prediction intervals and model types. Additionally, the Explain capability offers contextual insights and helps identify data anomalies without requiring coding skills.

Natural Language Processing & Generation

OAC's natural language processing (NLP) and natural language generation (NLG) capabilities allow users to interact with analytics using simple language queries, making exploring and understanding data easier. NLG generates intelligent textual descriptions of visualisations that are linked live to the data source. This approach enhances the accessibility and interpretability of analytics insights.

Conclusion

Integrating AI technologies in Oracle's EPM suite and OAC platforms is a significant milestone in the evolution of enterprise software. AI-driven features like the EPM Digital Assistant, Intelligent Narrative Generation, Auto Predict, and NLG empower organisations to transform their performance management, analytics, and decision-making processes. To effectively utilise these powerful innovations in the finance landscape, it is essential to analyse current finance processes and identify areas where these features can revolutionise the finance function.

How can we help?

Revvence can help in several valuable ways:

• Check out Revvy, our Narrow-GPT for Finance Transformation. Read all about Revvy here.
• Review your existing finance processes to recommend where Oracle's AI capabilities will have the most impact.
• Create a proof-of-concept to show you the art of the possible and help you build your business case for change.
• The design and delivery of end-to-end solutions.