\n<\/p>\n
Just because generative AI is trendy right now, doesn\u2019t mean it has to be applied to every application. But try telling that to Workday.<\/p>\n
The enterprise management platform vendor today announced a suite of new generative AI features aimed at \u201cincreasing productivity\u201d and \u201cstreamlining business processes.\u201d Soon, Workday customers will be able to automatically compare signed contracts against contracts in Workday to surface discrepancies, create personalized knowledge management articles and generate statements of work for service procurement.<\/p>\n
The announcements were made at Workday Rising, Workday\u2019s annual customer conference, which is taking place in San Francisco this year.<\/p>\n
Some of the additions seem genuinely useful \u2014 or harmless at the worst. But one is a bit concerning to this reporter: AI-generated employee work plans.<\/p>\n
\u201cManagers [will be able] to quickly create a summary of employees\u2019 strengths and areas of growth, pulling from stored data including performance reviews, employee feedback, contribution goals, skills, employee sentiment and more,\u201d Workday writes in a press release.<\/p>\n
I see a few problems with this.<\/p>\n
Studies have shown that text-analyzing AI can exhibit biases against people who use expressions and vernacular that fall outside the \u201cnorm\u201d (i.e. the majority).<\/p>\n
For example, some AI models trained to detect toxicity see phrases in African-American Vernacular English (AAVE), the informal grammar used by some Black Americans, as disproportionately \u201ctoxic.\u201d And Black Americans aren\u2019t the only minority group that suffers. In a recent study, a team at Penn State found<\/a>\u00a0that posts on social media about people with disabilities could be flagged as more negative or toxic by commonly used public sentiment and toxicity detection models.<\/p>\n So what if Workday\u2019s models fail to understand the nuance of a performance review or employee feedback because of how it\u2019s written, leading the models to draw the wrong conclusions about someone? Good question.<\/p>\n Then, there\u2019s the \u201cemployee sentiment\u201d piece.<\/span><\/p>\n Biases can once again rear their ugly head in AI models trained to detect sentiment from a sentence. Research<\/a> has shown that, for instance, text-based sentiment analysis systems can exhibit prejudices along racial, ethnic and gender lines \u2014 e.g. associating Black people with more negative emotions like anger, fear and sadness.<\/p>\n In response to these concerns, Workday says that it\u2019s \u201ctransparent about how its AI models are designed\u201d (albeit unwilling to reveal the exact data used to train its models) and built the work plan feature to show managers \u201chow the data inputs contribute to a strength or area of growth.\u201d<\/p>\n \u201cAs with other Workday<\/span> generative AI use cases and our human-in-the-loop approach, users are encouraged to review the results as a strong first draft that they should edit, iterate on and finalize,\u201d Shane Luke, head of AI and machine learning at Workday, told TechCrunch via email. Let\u2019s hope that managers using Workday heed that advice.<\/p>\n As for Workday\u2019s other new generative AI features, they\u2019re less problematic on their faces.<\/p>\n AI-generated job descriptions in Workday leverage information already stored in the platform, including the skills needed for a role and job location details, to simplify the process of writing job listings. This reporter was initially worried that Workday might be training its description-generating models on HR employees\u2019 copy without their knowledge or permission, but Luke assures me that this isn\u2019t the case.<\/p>\n \u201cWe don\u2019t train on dedicated job description data,\u201d he said. \u201cWorkday<\/span> customers control and configure how their data is used for AI and machine learning purposes, including whether data is used for training purposes \u2026 We make it clear from the product fields what data is being used in generation, and users are encouraged to evaluate responses as first drafts and apply their own judgment.\u201d<\/p>\n Elsewhere, Workday will soon be able to automatically craft \u201cpast due\u201d notices with recommendations on the tone of the correspondence, driven by how late a customer is or how often they\u2019re late. (Finance teams will be able to use the capability to automate letters in bulk, Luke says.) And procurement leaders will be able to get suggestions for relevant clauses to include in procurement contracts, depending on the type of project, project location and type of deliverables.<\/p>\n With the aforementioned contract analysis feature, powered by generative AI, Workday customers will be alerted to potential errors in contracts and receive proposed corrections. And with the knowledge article generation feature, users will be able to draft articles like talking points for managers and takeaways from company videos with suggestions on tone and length. (Workday stresses users are more than welcome to ignore those suggestions.)<\/p>\n Developer Copilot marks Workday\u2019s first foray into the generative coding, introducing text-to-code capabilities to Workday Extent, its platform for creating custom apps that run on Workday. Developer Copilot \u2014 embedded in Workday\u2019s app builder \u2014 delivers \u201ccontextually aware\u201d code suggestions for Workday apps, complete with curated content and search results, similar to code-generating services like GitHub Copilot<\/a> and Amazon CodeWhisperer<\/a>.<\/p>\n And \u2014 piggybacking on the popularity of ChatGPT \u2014 Workday is piloting a number of conversational AI experiences. Luke says that they\u2019ll \u201cenhance users\u2019 ability to interact with information and tasks\u201d in a natural way, taking advantage of the generative AI capabilities such as summarization, search and maintaining context.<\/p>\n \u201cWe believe that when used responsibly, generative AI can drive impactful business outcomes,\u201d Luke said. \u201cFundamentally, our AI approach is focused on human-machine teaming. The user is always the final decision maker and moderator.\u201d<\/p>\n Workday expects the new generative AI features to launch within the next six to 12 months. Unfortunately, it didn\u2019t provide a more specific timeline than that.<\/p>\n<\/p><\/div>\n