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Realize business value with Oracle's generative AI

Posted by Michael Olanipekun on 28-Jun-2024 15:58:51

Transform your business data into valuable insights

In the modern business landscape, where data reigns supreme, companies are on a continuous quest to leverage AI's transformative power. From uncovering hidden patterns to optimizing workflows and enhancing decision-making, AI offers a versatile toolkit for every step of the data journey. By understanding AI's diverse capabilities and their practical applications in various business scenarios, organizations can fully harness the potential of this game-changing technology.

Figure 1: The four general categories of generative AI use cases.


Unleashing Innovation: AI as a Catalyst for Creative Generation

AI is revolutionizing creativity and productivity by generating innovative content, code, and human-like language. For example, Oracle's Select AI empowers users to effortlessly create SQL syntax from natural language queries. Meanwhile, Natural Language Generation (NLG) enables automated report creation, personalized content like customized sales emails, and conversational AI interfaces that simplify access to complex corporate systems.

By harnessing AI's generative prowess, businesses can streamline workflows, elevate personalization, and unlock new levels of efficiency. A prime example is the integration of generative AI with Retrieval-Augmented Generation (RAG), which transforms corporate search. Instead of manually combing through endless files, employees can use natural language queries to instantly retrieve and generate precise answers, as illustrated in the following gif.


Figure 2: Optimizing search with generative AI and retrieval-augmented generation.

Oracle employees often face challenges finding information within the company's extensive internal resources, leading to increased IT tickets and HR inquiries. To address this, Oracle implemented MyOracle Search, leveraging OCI Generative AI and RAG to enable natural language search capabilities.

MyOracle Search delivers answers directly inline, grounded in references to original source materials, empowering employees to become more productive and self-sufficient. As a result, 25-30% of common IT requests are now resolved through generative AI, freeing up IT agents to focus on complex issues and reducing the burden on HR.

Distilling Insights: AI-Powered Summarization and Extraction

AI-powered summarization and extraction techniques condense vast amounts of information into easily digestible formats, highlighting key points and essential details. For instance, topic detection automatically identifies and extracts underlying themes or topics from collections of text data, facilitating tasks like document classification and trend analysis.

Extractive summarization pinpoints the most important sentences or passages within a document, creating concise summaries for quick comprehension. This is particularly valuable for lengthy or complex documents. For example, generative AI can break down complex insurance policies filled with industry jargon, extracting and summarizing key points in a more accessible manner for readers.

Figure 2: Optimizing search with generative AI and retrieval-augmented generation.

Sifting through mountains of documents is a time-consuming process prone to human error, where crucial information can be overlooked. Generative AI, however, can be trained on vast amounts of diverse data and generate accurate summaries in various languages, condensed to any desired length. This significantly reduces the time and effort required for humans to process information, ensuring critical details are not missed.


Adapting Content: AI-Powered Rephrasing and Transformation

AI-powered rephrasing and transformation tools excel at adapting content to diverse needs, including rewriting text while preserving its original meaning and converting documents into different formats.

Translation, a prime example, utilizes AI to accurately convey the meaning and nuances of text across languages, fostering communication and information exchange in diverse settings. This is vital in international business, diplomacy, and content localization efforts.

Consider the scenario of a customer service response drafted with unintended negative sentiment, potentially harming the customer experience. Generative AI can intercept this response and rephrase it, adopting a more positive, solution-oriented tone.

 Figure 4: An example of rephrasing a customer service response.

Human communication, while focused on facts, can inadvertently contain ambiguity, unintended tone, or errors. Generative AI, however, can transform this raw input, correcting mistakes, aligning it with corporate style guidelines, and ensuring technical accuracy. By refining the language and tone, AI-enhanced communication improves the customer experience. This also allows authors to focus on content creation, rather than spending excessive time on wording and style, leading to increased efficiency and productivity.

From Insight to Action: AI-Powered Reasoning and Decision-Making

In the "reason and act" category, AI demonstrates proficiency in inference tasks, such as answering queries and making predictions based on data. This doesn't involve human-like reasoning, but rather a systematic approach to problem detection, diagnosis, and resolution.

For example, AI-powered multi-turn conversational interfaces can tackle complex issues like supply chain disruptions through logical inferences. Additionally, root cause analysis dialogues enable AI to recommend resolution steps based on user input.

While AI can provide valuable insights and solutions, best practice currently involves human validation of AI-generated outputs before final decisions are made. This approach ensures accuracy and builds trust in AI's recommendations over time, while gradually paving the way for increased automation in the future.

Supply chain disruptions are expensive, so real-time monitoring, incorporating external data, is crucial. A conversational interface allows us to logically dissect problems and assess potential solutions, simulating a dialogue with a knowledgeable colleague. This streamlines the response to potential disruptions, enabling us to explore a wider range of solutions quickly and effectively.

Harnessing AI's Power to Revolutionize Your Business

In today's rapidly evolving digital landscape, AI is a game-changer for businesses seeking to thrive. By harnessing AI's capabilities across all stages of the data lifecycle, companies can unlock unprecedented opportunities, optimize processes, and make data-driven decisions that drive growth. Embracing AI for business transformation goes beyond simply implementing new tools; it requires a commitment to continuous learning, experimentation, and adaptability in the relentless pursuit of excellence.

Contact Hiperdist today to explore our cutting-edge storage solutions and discover how we can help you unlock the full potential of your financial data.


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Topics: Oracle, AI, Enterprise Management, Finance

The Future of AI in Finance: 6 Use Cases for Enterprises

Posted by Michael Olanipekun on 11-Jun-2024 10:51:16

Grow Your Revenue Faster with AI for Finance

AI is demonstrating that it is far more than a trendy technology. It is one of those rare breakthroughs, akin to the internet and cloud computing, with the potential to transform the business landscape. For CFOs and their teams, this advancement has arrived at a most opportune moment.

A constant challenge for finance leaders is to grow revenue while expanding margins. Achieving this necessitates running businesses more efficiently and utilizing technologies like AI to identify growth opportunities and uncover inefficiencies.

Looking ahead to the future of finance, Hiperdist envisions a significant transformation in the finance function. Although AI is unlikely to completely replace finance team members, it is expected to become an integral part of their daily activities.

What can companies do now to prepare for increasing AI use over time? It begins with understanding how AI is transforming finance.


How Is AI Transforming Finance?

Currently, AI is already being utilized in several areas to enhance decision-making, boost efficiency, and improve the bottom line, including:
  1. Financial Forecasting and Planning
    AI is revolutionizing the financial forecasting and planning process through predictive analytics, which employs data, statistical algorithms, and machine learning to forecast future outcomes by analyzing historical data and current trends.

    Using predictive analytics, finance teams can project future cash flows using both historical company data and industry-wide data. Unlike traditional financial forecasts that require manual adjustments when circumstances change, AI-driven forecasts can recalibrate based on new data, ensuring forecasts and plans remain accurate and relevant. Generative AI can also automatically generate contextual commentary to explain forecasts and highlight key factors driving the predictions.

  2. Regulatory Compliance
    With the increasing complexity of regulatory compliance worldwide, the cost and resource burden of regulatory reporting has surged in recent years. Organizations allocate significant time and resources to meet these requirements. AI can alleviate some of this workload by automating compliance monitoring, audit trail management, and regulatory report creation.

    Natural language processing (NLP) is particularly valuable in regulatory compliance. NLP, a branch of AI, enables computers to understand and generate human language. It can swiftly parse large amounts of textual data, transforming raw text or speech into meaningful insights. NLP can analyze lengthy documents, contracts, policies, and other text sources to extract critical information, identify pertinent changes, and highlight potential compliance risks. Additionally, NLP can enhance document management by automatically classifying documents based on predetermined criteria.

  3. Cash Flow Optimization
    Effective cash flow management is a top priority for CFOs and their teams, and AI is proving to be an invaluable tool in this area. Due to the vast amounts of data involved, most finance professionals require more than a day to build a consolidated view of their cash and liquidity, and even then, forecasts can contain errors and become quickly outdated.

    By leveraging predictive analytics and machine learning, companies can automatically compile data from all relevant sources—both historical and current—to continuously predict future cash flows. With faster and more accurate cash flow forecasting, companies can make proactive decisions to maintain healthy liquidity levels. For example, if there is excess cash, they can take advantage of early payment discounts with suppliers or identify opportunities for reinvestment in the business. When cash is tight, they can reassess loan positions or trigger foreign exchange transfers between subsidiaries. Finance teams might also use AI to optimize working capital by applying the right early payment incentives to select suppliers based on market conditions, payment history, and other factors.

  4. Expense Management
    Expense management can quickly become a source of frustration. For employees, adhering to expense policy rules by manually collecting receipts, filling out forms, and submitting expense reports is arduous and prone to errors. Finance teams also struggle to manually review every expense to ensure compliance. AI offers a powerful solution to streamline expense management and reduce its complexity.

    For instance, optical character recognition (OCR)—a form of AI that scans handwritten, printed, or image text, extracts relevant information, and digitizes it—can assist with receipt processing and expense entry. OCR scans uploaded receipts and invoices, automatically populating expense report fields such as merchant name, date, and total amount.

    AI's role in expense management goes beyond OCR. Companies can use AI to automate approval workflows, flagging only those expenses that need the finance team's review based on predetermined rules, and fostering a "manage-by-exception" culture. AI-enabled expense assistants are also becoming more common, helping employees by automatically categorizing expenses, populating and filing the required documentation, and providing guidance on the company's compliance policy.

  5. Task Automation
    One of the most common and impactful capabilities of AI is task automation. AI can automate numerous manual, time-consuming finance processes that previously inundated finance teams, including:

    Data Entry: Extracting and inputting relevant information from statements, invoices, receipts, and other financial documents into a company’s system.
    Invoice Capture and Processing: Scanning, reading, and digitizing paper invoices.
    Matching and Verification: Automatically comparing and matching invoices against corresponding purchase orders and delivery receipts.
    Payment Execution: Facilitating the payment process, including scheduling payments to optimize cash flow, selecting the preferred payment method, and executing payments automatically or with minimal human intervention.
    Reconciliation: Carrying out automated reconciliation processing by matching payment transactions with bank statements to verify that amounts and beneficiaries align.
    Account Code Combination Defaulting: Predicting and automatically defaulting segment values of the account code combination on non-PO accounts payable invoice lines.
    Financial Close Processes: Intelligent process automation (IPA) orchestrates the financial close, monitors task status across multiple systems, recommends new rules for ongoing automation, automatically initiates close processes as dependent tasks are completed, and updates the close calendar.
    The advanced automation of high-volume, repetitive, and mundane manual tasks brings numerous benefits, including time and cost savings, decreased errors, and higher employee satisfaction as finance staff can focus on more strategic, value-added tasks.

  6. Financial reporting and analysis
    AI can automate and enhance various aspects of the financial reporting and analysis process. Initially, it can extract relevant financial information from multiple data sources. It then cleans and processes this data by identifying errors, inconsistencies, or missing values and notifying finance staff of areas needing attention.

    AI can subsequently use the cleaned data to generate financial statements, such as income statements, balance sheets, and cash flow statements, transforming the data into reports that highlight key performance indicators (KPIs), trends, and observations. It also assists with regulatory reporting by filling out necessary forms with data provided by the finance team for review and confirmation.

    Generative AI (GenAI) can produce narrative reports, providing context by combining financial statements and data with explanations. GenAI can even prepare first drafts of 10-Qs and 10-Ks, including footnotes and management discussion and analysis (MD&A).


Concluding thoughts

What can your business do now to prepare for increasing AI use over time? First, aggressively automate your processes to reduce transactional work. Second, train your staff so they have the skills to effectively interact with AI tools, building analytical capabilities that capitalize on the technology. Giving finance staff an increased understanding of AI will also be critical in ensuring the proper security, controls, and appropriate use of the technology.

Ready to harness the power of AI to revolutionize your financial operations, drive revenue growth, and make informed, data-driven decisions? Don't get left behind in this era of AI-powered finance.

Contact Hiperdist today to explore our cutting-edge storage solutions and discover how we can help you unlock the full potential of your financial data.

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Topics: Oracle, AI, Enterprise Management, Finance

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