The News: Oracle announced in late June several generative AI-powered capabilities within Oracle Fusion Cloud Human Capital Management (HCM) that are designed to improve productivity, enhance the candidate and employee experience, and streamline HR processes. Delivered via the Oracle Cloud Infrastructure (OCI) generative AI service and leveraging foundation models from Cohere, human resources workers can summarize, author, and recommend content with the help of generative AI, reducing the time required to complete tasks, improving the employee experience, and enhancing the accuracy of workforce insights. Oracle says these generative AI capabilities will be generally available by the end of 2023. You can read the full press release at this link.
New Generative AI-Powered Capabilities in Oracle Fusion Cloud HCM Announced
Analyst Take: Oracle is introducing several generative AI-based capabilities within its HR-focused Oracle Fusion Cloud Human Capital Management solution, with the goal of reducing worker effort, streamlining processes, and providing a better employee experience. The incorporation of generative AI technology within an HR-focused suite is a smart strategic play for the enterprise software vendor; HR use cases are a natural fit for generative AI-based tasks because many of the inquiries are repetitive in nature, the source material for responses is usually contained within a single organization, and the end output is not targeted at external parties or the public.
Leveraging Generative AI While Grounding Models to Company-Specific Data
One of the key ways in which generative AI can be deployed more reliably is to ground a large language model (LLM) with company-specific, proprietary data. With Oracle Cloud HCM, customers use their own data to refine models for their specific business needs, so that each customer’s dedicated generative AI models are only tuned on the customer’s own proprietary data. This ensures that no proprietary data is fed back into a public LLM, and ensures that the model is focused solely on a restricted and vetted data source.
Note, however, that Oracle is not discussing the use of any other LLMs beyond Cohere at this time, so it will be interesting to see whether the company eventually takes an agnostic approach to incorporating different LLMs, based on the task or use case, as some other vendors have done.
Improving HR Efficiency Through Generative AI Capabilities
The new generative AI capabilities in Oracle Cloud HCM include assisted authoring, suggestions, and summarization. These capabilities are designed to improve productivity, reduce time spent on repetitive tasks, and generally improve efficiency and accuracy of content-based work.
Assisted Authoring is designed to let a worker author content by entering a short prompt, such as draft title for a job requisition or performance goal, and then the generative AI engine will generate content that can be reviewed, revised, and approved quickly, letting the worker review, revise, and approve so that they can focus on more value-added activities. Oracle provided a few examples of where assisted authoring can be deployed, such as with writing job descriptions and requisitions; automated goal creation, including detailed descriptions and measures for success; and the generation of HR Helpdesk knowledge base articles to help employees efficiently complete HR tasks. As the technology and knowledge bases are refined, it is likely that more expansive use cases, such as the automatic creation of full role profiles, or the generation of job- or individual-specific employee handbooks or manuals could be created.
Oracle Fusion Cloud HCM also offers generative AI-created suggestions, which are designed to help workers complete tasks with greater speed and accuracy. Examples of suggestion use cases include automated recommendations for survey questions based on the type of survey being designed or development tips for managers to provide to their employees. A major efficiency driver is that the suggested content, trained from the LLMs under the customer’s control, will automatically reflect the language style and cultural DNA of the organization itself, instead of requiring the user to edit or rewrite the generated content in the organization’s preferred style and voice.
Generative AI is quickly proving to be a powerful tool to generate content summaries quickly, driving efficiency by surfacing key insights from one or more data sources. One example that likely will save significant time and effort is by utilizing the generative AI tools to create a summary of an employee’s performance based on feedback gathered across the year from the employee, peers, or managers, and goal progress and achievements.
The Need for Clean and Updated Data
Generative AI tools likely will provide significant time and resource efficiencies; one only needs to play around with the widely available ChatGPT, Bard, or CoPilot tools to quickly see how they can help with gathering, summarizing, and creating content. However, the use of these widely available models points to a key issue: the models’ effectiveness in a business setting are totally dependent upon the dataset from which the model has been either trained or grounded.
Organizations that have a piecemeal approach to managing their data may not be able to reap the full benefits of generative AI technology, or may see results that are incomplete, incorrect, or out of date. If an organization is utilizing legacy systems or applications, it may have certain pieces of HR data held in separate silos. If this data is not coordinated, updated, or verified on a real-time basis, any generative AI technology using it as a knowledge source may return conflicting information. If these HR processes are automated using generative AI, or even robotic process automation (RPA) and the data is not verified, workers may not realize they are viewing or using content that is incorrect.
That’s where Oracle may have an advantage in the market. Oracle HCM customers undoubtedly have undertaken the challenge of making sure organizational data is clean, available, and updated. Most organizations that have migrated to an Oracle cloud platform likely have implemented best-practice data organization, management, and security procedures, such as ensuring data validity and establishing a single source of truth for personal, process, and policy data.
Disclosure: The Futurum Group is a research and advisory firm that engages or has engaged in research, analysis, and advisory services with many technology companies, including those mentioned in this article. The author does not hold any equity positions with any company mentioned in this article.
Analysis and opinions expressed herein are specific to the analyst individually and data and other information that might have been provided for validation, not those of The Futurum Group as a whole.
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Author Information
Keith has over 25 years of experience in research, marketing, and consulting-based fields.
He has authored in-depth reports and market forecast studies covering artificial intelligence, biometrics, data analytics, robotics, high performance computing, and quantum computing, with a specific focus on the use of these technologies within large enterprise organizations and SMBs. He has also established strong working relationships with the international technology vendor community and is a frequent speaker at industry conferences and events.
In his career as a financial and technology journalist he has written for national and trade publications, including BusinessWeek, CNBC.com, Investment Dealers’ Digest, The Red Herring, The Communications of the ACM, and Mobile Computing & Communications, among others.
He is a member of the Association of Independent Information Professionals (AIIP).
Keith holds dual Bachelor of Arts degrees in Magazine Journalism and Sociology from Syracuse University.
