The News: Workday announced several new finance and human resources (HR)-focused generative AI capabilities and related use cases, which were showcased at the company’s Workday Rising annual customer conference. According to Workday, the generative AI capabilities will span across the company’s platform and are designed to increase worker productivity, streamline business processes, assess workers’ performance, and improve decision-making.
You can read the original press release announcement on Workday’s website.
Workday Announces Generative AI Capabilities for Finance and HR
Analyst Take: At its annual Workday Rising customer conference, Workday announced multiple new generative AI capabilities, which are targeted at finance and HR use cases and are being rolled out across Workday’s platform. The company highlighted its AI model strategy and reinforced the company’s commitment to its responsible AI (RAI) governance, which revolves around transparency and disclosure on how models are developed and assessed and the company’s commitment to incorporate human review of any output generated from AI technology.
Workday also detailed several specific use cases that will leverage the generative AI technology embedded into the platform over the next 6 to 12 months, including job description generation, contract analysis, knowledge management, automation of collections letters, application development, employee growth assessment and planning, and the generation of statements of work. However, one particular use case might require additional transparency and discussion to engender trust among enterprise clients and their customers.
Models Fueled by Domain-Specific Data
Workday’s generative AI approach notes that its AI models are fueled by more than 625 billion transactions processed by the system every year, which it claims is the world’s largest set of financial and HR data. This data pool can be a major advantage for Workday, as copious amounts of clean and relevant process data can be used to train AI models, resulting in the generation of more accurate, meaningful, and trustworthy results.
Due to their scale – large language models (LLMs) such as ChatGPT, are trained on petabytes of data across the web – LLMs can structure output in a way that humans can easily interact with using natural, everyday language. But AI models need to be tuned or trained on more specific information and datasets to ensure that the nuances, terminology, and processes used in domain-specific situations are reflected in the output, and Workday’s approach appears to be sound in that approach.
Wide Range of Finance and HR-Related Use Cases
Workday’s announcement provided several specific examples of how generative AI embedded in its platform can be used across finance and HR tasks. These use cases tend to leverage the now-familiar underlying capabilities provided by generative AI models, including content generation, summarization and analysis of content, and the abstraction of coding tasks using natural language.
Within the category of content generation, Workday highlighted the following use cases:
- Generating job descriptions: Workday says that its customers create 30 million job descriptions per year, which can each take an average of 1 to 2 hours to complete. Generative AI tools embedded in Workday will leverage information already stored in Workday as the single source of truth for people data, which includes the skills required and location information for each job. With these tools, HR pros can quickly generate job descriptions.
- Creating personalized knowledge management articles: Content creators can quickly generate knowledge base articles that are personalized and tailored to their audience, leveraging generative AI to capture relevant information from existing and vetted sources.
- Generating tailored collections notices: New generative AI capabilities from Workday will enable finance teams to save valuable time by automating the process of crafting past due notices with recommendations on the tone of the correspondence, driven by how late the customer is or how often they are late. Teams will be able to use Workday generative AI to automate letters in bulk – based on configured rules – to easily send an entire package, including past invoices, enabling collections agents to greatly increase their throughput and recapture missing funds sooner.
- Generating statements of work (SOWs) for procurement: Organizations will be able to use the content-generation capabilities of generative AI to automate the creation of SOWs, reducing time and effort by suggesting relevant clauses to be included depending on the type of project, project location, and type of deliverables.
Generative AI Enhancements to Analysis, Comparison, Abstraction, and Assessment
The generative AI technology embedded in Workday’s platform is also being used to enable other types of use cases and functions based on generative AI’s abilities to enhance analysis, compare large amounts of data, and abstract complex information into an easier-to-use format.
The initial use cases highlighted by Workday in this category include:
- Analyzing and correcting contracts: Customers will be able to compare signed contracts against contracts in Workday Financial Management and integrated CRM data quickly, using generative AI’s ability to compare complex data. The feature will alert the user to any discrepancies early in the process and propose corrections, then confirm when discrepancies are fixed.
- Streamlining app development: Developer Copilot, a human-machine teaming capability for Workday Extend app development, will leverage the power of generative AI to support the entire development lifecycle for rapid creation of finance and people management apps. Natively embedded into Workday’s App Builder, Developer Copilot will provide text-to-code generation capabilities to dramatically improve developer productivity and customer time to value by turning natural language into app code. Developer Copilot also incorporates contextual awareness, which can help provide curated content and search results that meet and elevate developers’ skill levels.
- Conducting employee assessments: Using generative AI, managers will be able to create a summary of employees’ strengths and areas of growth, pulling from Workday’s database of insight such as performance reviews, employee feedback, contribution goals, and skills, which can be used to assess areas of strength or highlight areas for improvement. The use of AI to summarize or analyze worker performance likely will be a very contentious feature, as the model will be able to pull only from the inputs within the database and might misinterpret the meaning or intent of comments, particularly if the manager conducting and writing assessments is not using standardized language for all employees. For example, using a model might distinguish between an employee doing a “adequate” job versus one doing a “satisfactory” job, and thereby might assign a different summary based on the language used. As this data can affect promotions or demotions, compensation, bonuses, and the general standing within a position, Workday – and any customers that choose to use the feature – should be willing to fully explain how the model works and how factors are weighted.
The Value of Trust With AI Use
The market has been flooded with announcements from enterprise software vendors about their inclusion of generative AI into their platforms, and it can, at times, seem like a case of “me-too.” Although many of the functions and use cases are similar, what will set certain vendors apart from their peers will ultimately depend on the vendor’s ability to engender trust with the enterprise decision maker.
The promise of a trusted experience with AI will, of course, revolve around a vendor’s ability to communicate and demonstrate that the models they use are accurate; address toxicity, bias, and hallucination; and do not utilize end-customer data to tune or retrain models, unless specific permission has been granted.
The other issue surrounding trust, which is applicable in Workday’s case, is that the vendor will be transparent with all stakeholders around how models that might affect areas of significance – such as a person’s workplace performance – are constructed and operated. Without that knowledge and trust, it is hard to see how the more contentious AI use cases, such as the employee assessment use case cited by Workday, will be adopted.
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 Kirkpatrick is VP & Research Director, Enterprise Software & Digital Workflows for The Futurum Group. 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.
