Using Generative AI in Service of Our People

700 U Krewers around the globe collectively put Generative Artificial Intelligence (GenAI) through its paces

It’s hard to imagine a technology world before Generative Artificial Intelligence (GenAI), yet it was only a couple of months ago when we were all first introduced to ChatGPT and Bard. As with many new technologies, these powerful tools immediately set off a wave of blue sky thinking about the opportunities they may present. 

GenAI is still in its early stages, but it could transform how we interface with our preferred devices or favorite applications. As one vice president of product recently quipped, “Gen AI is my new best friend.”

Using Generative AI in Service of Our People Purpose      

Three months and “48 Hours”

At UKG, we spent three months expanding our work in GenAI to understand how we can leverage it most effectively in service of people. Then in mid-April, those efforts informed our global hackathon, known as 48 Hours, a two-day, organization-wide development sprint. 

More than 1,100 U Krewers (our nickname for employees) joined in to develop novel, impactful, and feasible applications and experiences that will help UKG transform how our customers empower their people and run their businesses. We formed more than 150 teams, building new experiences and product/service innovations. 

Teams were encouraged to: 

1. Leverage Google Cloud’s latest LLM and enterprise tooling.       
Given the nature of the event, we brought onboard our cloud partner, Google Cloud, who generously enabled access to their enterprise GenAI, large language models (LLMs), and AI toolkit for participating teams. Google Cloud AI experts also joined UKG AI leaders, hosting sessions to accelerate our learnings and provide critical support and guidance to participants. 

2. Augment existing AI use cases.       
We have a long, rich history helping the HCM industry break new ground in AI, helping people achieve more in the flow of work. Over the years, we’ve introduced experiences including AI-powered analytics, real-time recommendations, proactive reminders, sentiment analysis, anomaly detection, and long-range forecasting, among others. Our efforts continue to accelerate, and we are making even bigger leaps that will help businesses responsibly leverage and apply our capabilities to their people strategies.

3. Apply our proprietary data.       
Quality and accuracy of AI applications are significantly influenced by the data and information used to train and benchmark them. In the case of GenAI and LLMs, which are trained on a broad corpus of information, they can also be fined tuned to specific use cases using more specialized data sets. Teams were given access to curated data sets on which to experiment. In addition to one of the largest people and workforce management datasets, we also have the benefit of proprietary insights from Great Place To Work®. 

4. Iterate quickly, maximize learning and collaborate.        
As with many hackathons, teams were encouraged to rapidly build prototypes and share the result of their work with each other. Collaboration was encouraged both in-person and over Microsoft Teams. The energy was infectious!  

What was unique in this case, is the deliberate focus on rapidly levelling-up as many people across our Engineering, Product, Cloud, and Digital organizations while engaging colleagues from our Support, Service, Customer Success, and many other support functions. They included dedicated learning webinars, “office hours,” and ongoing support through local champions. It was interesting to observe how quickly colleagues incorporated the idea of becoming “prompt engineers” and using design thinking to explore the possibility of this new interface to facilitate existing experiences such as navigation and discovery.

We used this opportunity to engage the broader organization on security best practices and ethical AI. There are many well publicized examples of companies' code being shared in the consumer versions of GenAI such as ChatGPT that have led to security leaks. We have also seen examples of biases being created by the choices of training data. Some of our event educational webinars have explored these issues and gave participants the opportunity to reflect on practices that will be essential to leverage this new capability moving forward and reinforce our commitment to using GenAI in an ethical manner. 

Finally, we explored the economic impact of using LLMs. These models are compute-intensive and have the potential of changing the economics of SaaS businesses if not designed properly. We explored enterprise needs to operate multi-corpus and multi-model environments that are fit for purpose and can leverage feedback to avoid model drift. These are difficult engineering problems, but I was excited by the creativity and resourcefulness of our teams.

What’s next?

While it is too early to share which concepts are making their way to our next releases, some of the most popular concepts developed by our teams identified new opportunities for UKG to help workers and people leaders, whether it be understanding intent behind requests (in app or for support), anticipating needs to surface additional helpful information or recommended actions, or fundamentally reimagining what it means to engage with software. This said, we are already working on some exciting updates to make these innovations accessible and consumable for our users and customers as a community, even if not as a direct feature in our offerings.

As a people-centric workplace software company, we are focused on bringing together operations and culture to help organizations unlock the full potential of both their people and their businesses. GenAI is poised to be a generational breakthrough and this exercise is an example of the Democratization of AI in Practice that will help us realize the potential of GenAI in delivering on our people purpose.

We’re eager to pull back the curtain to show you what we’ve been working on very soon.