HRTech Interview with Dr. Autumn Krauss, Chief Scientist at SAP SuccessFactors

Dr. Autumn Krauss chats about SAP SuccessFactor’s recent AI findings and how Artificial Intelligence is set to impact the future of work:

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 Hi Autumn, tell us about yourself and your role at SAP SuccessFactors?

As Chief Scientist at SAP SuccessFactors, I lead our Market Insights & Customer Engagement team, which analyzes workforce trends, customer needs, and HCM market dynamics to generate actionable insights. Part of my team includes HR research scientists, who are applied psychologists that identify HR trends and execute a future of work research agenda that drives market thought leadership and informs product strategy.

I’m an Organizational Psychologist by discipline, having spent my career as a scientist-practitioner focused on translating what we know through research and data into practical implications for HR strategy and technology.

We’d love some top highlights from your recent study of over 4000 workplace executives on the use of AI and its impact at work

 Our study revealed that AI literacy, which is the ability to detect, understand, and evaluate the technology, is the most significant factor influencing opinions of AI. Employees with high AI literacy are much more likely to have positive views about using AI at work, with nearly 70% expecting positive outcomes from using AI compared to only 29% of those with low AI literacy. In contrast, employees with low AI literacy hold more negative sentiments, reporting feeling six times more apprehensive, seven times more afraid, and over eight times more distressed about using AI.

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Interestingly, the study also found that respondents with high AI literacy tend to have more positive or egalitarian views about AI’s role in people decisions like performance reviews, career advancement, and compensation. For instance, when assuming workers have the same quality and quantity of work output:

• The majority (55%) of people believe employees who use AI to do their work should have better performance reviews than those who don’t use AI. This sentiment was even higher (64%) for workers with high AI literacy.
• Nearly half (45%) of people believe that employees should have the same promotion opportunities regardless of AI usage. Those with high AI literacy felt similarly, with the majority (57%) believing promotion odds should be equal regardless of AI use.
• Forty-four percent of people with low AI literacy believed that employees who use AI should be paid less than those who don’t use AI. Conversely, 46% of people with high AI literacy reported they believed compensation should be equal, regardless of AI use.

What about today’s state of AI across business functions should employees and employers both be careful about and what factors should they optimize more as they get used to these systems? 

When it comes to the current state of AI, the focus for both employers and employees should be on value and adoption. First, on value, employers should be aligning across business functions on what value (i.e., outcomes) they are expecting to achieve through the introduction of AI and using this to guide their decision-making when it comes to prioritized AI use cases. Likewise, employees have a perspective on this too, and by asking and listening to employees about where they see the value for their jobs, a company can take this into account in their AI strategy. We investigated these questions with our research and found that the top three outcomes that employees most desire from using AI at work are time savings (save time and help me complete my work more quickly), quality (improve the quality of my work), and efficiency (make boring or repetitive tasks happen faster or more easily).

Second, on adoption, prioritizing use cases aligned to key value drivers will certainly help with adoption, but there is so much more to be done here. Until AI is fully baked into systems and employees are in a position to not really know they are using it, there will be a “people factor” that organizations will need to thoughtfully plan for including a comprehensive organizational change and communications strategy. In our research we’ve observed that employees are not all in the same starting position with their “base mindset” when it comes to AI, meaning that one intervention to improve workforce AI adoption may work for a segment of employees, but another group may need something else to get them to adopt. In our most recent study, we found that the overall top things that would make employees more likely to use AI are control over their data, opportunities to try out the tool, and training and support for using the tool. But again, if we divide the sample up based on different characteristics such as those who have high AI literacy or not, then their “adoption drivers” change (those with high AI literacy want an understanding of the impact of the AI tool, while those with low AI literacy want reward and recognition for using the AI tool).

How can employers drive better AI upskilling and training processes? A few examples of how leading brands do it?

As AI becomes more widely used at work, organizations must invest in AI literacy to ensure all employees are equipped to reap its benefits. Our data show that the most important aspects of AI education are knowing how to use AI to achieve one’s goals and being able to detect when a technology uses AI.

Employers can enhance their AI upskilling and training initiatives through several strategies, such as: providing hands-on experiences for employees to work with AI in practical settings, offering structured training sessions and resource libraries, and sharing success stories about how teams have benefitted from using AI in their work.

A few predictions and top of mind thoughts on the future impact of AI on business and workplace norms before we wrap up?

 AI will drive a cultural shift toward continuous learning: Organizations have long struggled with embedding a learning culture, with employees consistently voicing limited time to prioritize learning and development given their day-to-day responsibilities. Given the criticality of AI as a new tool and technology along with the pace of its ongoing advancement, organizations have to focus on cultivating a culture that truly values learning and implementing practices that really allow employees to continuously learn. Creating an environment where employees feel empowered to explore AI technologies, ask questions, and share feedback will become as critical as the technology itself.

• AI business impact results will inform use case prioritization: So far, businesses have largely focused on “low hanging fruit” use cases, those easiest to implement and with perhaps the lowest risk; however, those selection criteria do not necessarily translate to the greatest employee or organizational value. While there have been minimal studies demonstrating tangible evidence of the anticipated positive impact of AI, this body of research will grow over time, offering a more data-driven and informed way to prioritize AI use cases going forward.
• AI usage will eventually be normalized and commonplace: The results of our research show large variability in employees’ experiences with AI and conflicting attitudes towards those who use it. These findings exemplify the moment in time we find ourselves in, where AI is front and center as something novel and people are grappling with making sense of it. In the not too distant future, AI use cases will be more seamlessly implemented, where the fact that a tool or system is enabled through AI won’t be called out so clearly, allowing for it to be relegated to a technology “under the hood” and the focus to shift back to the outcomes workers achieved, not how they achieved them.

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