GenAI for enriching employee experience: So much excitement! So much fear!

HFS recently facilitated a roundtable session in New York City in partnership with Ciklum and Kore.AI. We were joined by top HR and digital transformation leaders. The discussion focused on the role of generative artificial intelligence (GenAI) in driving employee experience (EX) and featured leaders from companies like Prudential, Oppenheimer, and Rackspace. The eagerness in the room to learn how they could leverage GenAI—this shiny new tool everyone is talking about—was palpable. But so was the nervousness about its legal and privacy risks, underlying data challenges, and organizational and cultural change requirements.

Generative AI will bring more humanity into the workplace

At HFS, we define EX as the cumulative impact of an employee’s interactions over the employee journey, from recruitment to exit. We look at this journey through four dimensions (see Exhibit 1). With GenAI, enterprise leaders can inject humanity into each dimension by tailoring experiences to individual needs and fostering a sense of belonging.

Exhibit 1: GenAI can inject humanity into the four dimensions of EX

Note: Examples are illustrative, not comprehensive
Source: HFS Research, 2024

Organizations can ultimately create a more human-centric workplace that promotes engagement and fulfillment by leveraging GenAI. Here is how they can accomplish this across each EX dimension:

• In the physical environment where employees work, GenAI can help craft a physical environment that promotes collaboration and productivity and ensures physical well-being. For example, Siemens’ Comfy app uses AI to optimize office layouts and manage resources effectively, improving employees’ physical workspaces.
• In the digital environment supporting employee tasks, GenAI can lead to personalized, role-based tools, fostering collaboration and communication within a secure digital environment. AI-powered tools like Slack’s AI search and Google’s Smart Compose in Gmail help employees communicate more effectively. Unilever has implemented AI-driven platforms to handle its recruitment process, which includes scanning resumes, conducting initial assessments, and scheduling interviews. The platforms speed up the hiring process and ensure a more diverse and inclusive selection.
• Role advancement captures employees’ roles and progression in the organization. GenAI can streamline role advancement, fostering innovation and personalization. For instance, JPMorgan Chase’s product, IndexGPT, utilizes GPT (generative pre-trained transformer) software to analyze and select securities tailored to customer needs. A large UK automobile manufacturer leveraged Kore.AI and Ciklum to implement a virtual assistant for job-related inquiries, scheduling interviews with calendar integration and automated reminders to reduce no-shows and providing new hires with essential onboarding information and company culture insights.
• Well-being captures employees’ psychological safety and sense of belonging. GenAI can impact well-being by promoting diversity, inclusion, and mental health while improving work-life balance. Companies like Woebot and Ginger offer AI-powered mental health support to employees, helping them cope with stress and anxiety.

GenAI’s upside for EX could be immense, but the journey could be uncomfortable

The market is awash with BS about GenAI—and you already know it. Simply put, the whole enterprise world is absorbing GenAI information overload, and we need to take a deep breath and crystalize some issues that we will need to overcome to drive enterprise adoption.

Maintain a critical focus on governance and explainability

Most data privacy laws try to mitigate a “black-box” approach to AI: where something goes inside a black box, something comes out, and no one understands what happened inside the box—it lacks transparency and visibility.

While most machine learning is considered a black box due to a lack of explainability, the recent surge in interest in large language models (LLMs) has intensified concerns about transparency. Ultimately, you need explainable AI to protect civil liberties and address bias. To this end, upcoming legislation like the US’ AI Bill of Rights and the EU’s artificial intelligence liability directive emphasize this necessity. However, the lack of a standardized reporting format in responsible AI laws is causing confusion and delaying corporate compliance efforts.

Get on top of enterprise data management

Anything touching employee data is more scrutinized than ever, and GenAI opens up a whole new can of worms when it comes to immersing it into the enterprise. Due to ethical, legal, and security challenges, getting enterprises to share private employee data with third parties will be challenging, if not impossible. While data anonymization and data impact assessments are potential mitigation strategies, the ultimate effectiveness of these measures often falls under judicial evaluation, as evidenced by cases involving GDPR. Enterprises must also address the technical challenges of model drift and unpredictability in AI model outputs.

Address employees’ apprehension about GenAI

You gotta love the discussions of how AI will replace jobs. It feels like the whole narrative equating automation and job losses just got reloaded with a GenAI sugar frosting. Instead of merely rehashing the old narrative that equates automation with job losses, there’s a compelling need to shift the focus toward how GenAI can create new value.

A pivot from a narrow emphasis on productivity use cases with GenAI to broader value creation is essential in addressing employees’ apprehensions effectively. By defining the business case for GenAI in terms of value—how it can innovate, enhance quality, and improve employee experience—the conversation transitions from fears of replacement to opportunities for augmentation and growth.

Scaling GenAI is expensive—start building the business case now

Forget ChatGPT 3.5. For enterprises, GenAI is not free. On the contrary, attracting talent for data management, finding the rare breed of prompt engineers, and running your foundational model require deep pockets—and that is before the debate around AI’s carbon footprint even starts. In addition, getting access to the IT infrastructure to build and develop these language models gets expensive, and building business cases and longer-term viable cost models will dominate sourcing discussions in the coming months.

Understand what GenAI is—and what it isn’t

GenAI uses machine learning trained on information provided by disparate sources. Don’t expect a simple “42” thrown at you as the answer to life, the universe, and everything. The next frontier for AI is becoming objective and goal driven, yet we are early in terms of foundational research.

From an enterprise point of view, all of this boils down to integration and governance. We have to learn so much more about GenAI and beyond. Cutting through the market noise is an essential early step on that journey.

The Bottom Line: Don’t let GenAI become a solution in search of a problem!

By evaluating GenAI’s impact across the four dimensions of EX, organizations can drive a workplace transformation beyond efficiency gains, cultivate an empowered workforce, and infuse more of the human touch into the workplace. HR and talent leaders should explore GenAI solutions for EX that are fast and simple to build confidence and make a difference.

But this is easier said than done. GenAI’s big challenge is cleaning up enterprises’ messy data so they can benefit from the tools. Enterprise leaders should engage with no-nonsense partners that can understand the business context behind their data needs instead of teams of highly expensive technical and domain consultants charging $2 million to show up and document the problems!