How Workday Leading Enterprise Generative AI Revolution

Workday’s chief technology officer, Jim Stratton, shares his perspective on the recent buzz around generative AI, how Workday is utilizing its capabilities, and what it means for the future of work.

The hype surrounding generative AI is palpable, with businesses of all sizes rushing to harness its transformative potential or, at the very least, its marketing potential. A lot of the hype really is just hype, but at Workday, we’ve been using large language models (LLMs), like those which power generative AI, for years. We’ve already been able to deliver tangible benefits for our customers leveraging these technologies, and we’re investing even more, looking beyond the hype cycle to deliver meaningful business value and tools that will redefine the way we work. 

We’re currently building capabilities that leverage generative AI for various language and image-related tasks, including natural language generation, document understanding, and content search, summarization, and augmentation. These new capabilities will enable our customers to unlock increased productivity through streamlined tasks and processes, increased efficiency, and better decision-making. These are not far off in the future—in fact, our customers can expect access to these cutting-edge features within the next 6-12 months. 

So let’s take a deeper look at how Workday is leading the enterprise generative AI revolution.

What We’re Doing Differently

Our approach to generative AI is different in several ways—most notably because of our unrivaled dataset. We firmly believe that the effectiveness of generative AI hinges upon the quantity and quality of the data it is built on. As evidenced by the many stories highlighting how generative AI chatbots have provided biased or incorrect responses, LLMs are only as good as the data that feeds them. 

The foundational models that have dominated headlines recently have been deliberately trained to solve a broad class of problems from the broadest set of data available. That vast set of data is not all of equal quality or of well understood provenance—resulting in well-documented erratic, incorrect, unsafe behavior, or infringement of intellectual property. We have also seen that the safeguards on responses put in place to deal with poor training data do not hold up over time. To address this for our critical use cases, we focus on targeted, domain-specific models and high data quality above all else to provide outputs customers can have confidence in.

One of our key differentiators is that all customers are running on the same version of Workday, including the same data model. At Workday, we have over 60 million users who contribute to nearly 450 billion transactions processed by the system every year—and growing. With our customers’ permission, we utilize that data as the fuel for our generative AI capabilities. This massive, high quality dataset allows us to build models that consistently generate accurate, meaningful, trustworthy results.

Our approach to generative AI is different in several ways—most notably because of our unrivaled dataset.

We also follow the same platform strategy for development and deployment of generative AI as we do with other AI and ML technologies. This allows us to rapidly leverage emerging technologies like foundational models to build new features quickly and easily, while maintaining a consistent experience throughout the entire Workday environment. It also helps us remain at the forefront of the rapidly evolving AI landscape by being able to embrace new models quickly.

Our approach to generative AI is also unique in that we are adopting a hybrid, vendor agnostic model. Not only are we developing our own domain-specific LLMs, we’re also working with multiple leading third-party providers to create blended or ensemble models. This method lets us harness the best technologies available while delivering performant, cost-effective, trustworthy solutions to our customers.

We can also improve third-party models with our high-quality structured data through prompt engineering and retrieval augmented generation (RAG). Our goal is to ground third-party LLMs in truth by integrating context and factual information from Workday. By augmenting these models with Workday data, we aim to provide responses that combine the strengths of leading LLMs with the accuracy of verified data. This will result in a more robust and dependable solution for our customers.

Our approach to generative AI is also unique in that we are adopting a hybrid, vendor agnostic model.

Implementing Generative AI Responsibly

While the promise of AI technology is undoubtedly thrilling, we also acknowledge that its use can pose some risk. Upholding data security and privacy standards is of utmost importance to us, so we’re focused on the alignment of these technologies with our privacy principles. We leverage Workday’s responsible AI governance program to support the development of trustworthy generative AI solutions in accordance with our AI ethics principles. All development of new AI technologies, including those that leverage generative AI, goes through our responsible AI risk evaluation process, and adheres to the relevant set of responsible AI guidelines. By adopting these approaches, we aim to drive responsible AI innovation without sacrificing any of the benefits that innovation brings to our customers. 

We also believe that even though AI provides ample opportunity to automate business processes, our emphasis remains on augmenting—not displacing—people. Our approach entails fostering collaboration between humans and machines, and is designed to integrate human expertise. Through our AI practices, we help ensure that human judgment retains its pivotal role as the ultimate decision-making factor, and holds the ultimate responsibility and accountability.

In addition to our focus on responsible AI for all aspects of our business, we’re also taking a leading role in shaping AI-focused policy discussions at various levels of governance. Within the United States, we actively participate in federal, state, and local policy dialogues to advocate for thoughtful and effective regulations governing AI applications. Beyond national borders, we’ve built strong partnerships with the European Union and other global governments, working collaboratively to develop comprehensive policy approaches that promote the responsible use of AI worldwide.

We firmly believe that AI technologies must be subject to meaningful regulation. By imposing responsible rules, we can help ensure that AI advancements are guided by ethical principles and potential risks are appropriately managed. As we navigate the ever-evolving AI landscape, we remain committed to striking a balance between innovation and responsibility, shaping a future where AI benefits society as a whole.

We’re currently building for common business use cases where we can deliver clear and immediate benefits.

What’s Next From Workday

Just as with all of our AI innovations, our goal is to amplify human performance and empower our users to make informed decisions faster—allowing them to focus their time and energy on the tasks that matter most. 

Our journey to building impactful generative AI for the enterprise is well underway. We’re currently building for common business use cases where we can deliver clear and immediate benefits. Next month, we’ll unveil new capabilities like growth plans for career development, job description content creation, offer letter content creation, financial contract summarization, and anomaly detection, to name a few. Through these new features (and more), we look forward to further driving efficiency and cost-effectiveness for our customers—ensuring that hype doesn't overshadow practicality.

As mentioned, we’ll preview these new generative AI capabilities and the rest of our latest AI developments at Workday Rising, taking place from September 26 to 29 at the Moscone Convention Center in San Francisco. We hope to see you there!

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