Q&A | February 24, 2022
Valérie Bécaert relies on her scientific background to translate AI research breakthroughs into enterprise software products
Valérie Bécaert of ServiceNow spends her workdays navigating the priorities of long-term research and customer or product engineering groups, “weaving scientific innovation into the texture of the company,” she says.
With a Ph.D. in chemical engineering and a professional focus on sustainability, data science, and AI research, she brings diverse teams together via brainstorming workshops, hackathons, proof of concept experiments, and pilot projects.
Bécaert, a senior director of research and scientific programs at the company’s Advanced Technology Group, spoke with Workflow about how she helps translate research innovations into production software.
The following interview has been edited and condensed for clarity.
Innovating for the future takes a lot of creativity and intuition. There are three buckets in which you can invest. You can be market-driven: thinking about what future markets might look like. You can be product-driven: thinking about how you want the customer’s experience of a product to evolve. And you can be technology-driven: thinking about what new technologies might emerge in the future and positioning yourself to be on the forefront of discovery. The one constant is that within your organizations you must develop a culture of change.
On my team, we are primarily technology-driven. We’re investing in our ability to see what technologies will emerge in five years. Once you know which bucket you’re in, you can then understand what your team needs to succeed. My small team is heavily invested in collaboration, because we need to work together efficiently to stay on top of new technologies being developed in academia and the scientific community and to evaluate how they fit into the ServiceNow product roadmap. I was involved in fundamental and applied research at ElementAI, which ServiceNow acquired in January 2021, and we are exploring use cases with business units on how we can put this new capacity to use. Some examples include our natural language processing, machine learning, and computer vision research, which are currently being re-platformed to create customer-focused AI tools for the Now Platform.
People talk a lot about breaking down silos so teams can communicate cross-functionally, and I believe that’s more important than anything. But leaders often mistakenly believe that to break down silos, they must create a uniform culture across the organization. Instead, teams must develop different behaviors, skills, and cultures based on their needs. In the research group, for example, I work to create a curiosity-driven culture where creativity and risk-taking are accepted, but you cannot expect a team that has to produce concrete deliverables next week to have the same culture.
Creating cross-cultural teams is about building connections and creating an overarching culture that is open to new ideas and unconventional perspectives. I think this is especially important as new technologies like artificial intelligence and machine learning become more pervasive in the workplace.
Change is not always comfortable or easy, but it can be more comfortable and far easier for organizations with leaders who talk to employees about what’s in store. They need to be clear that what we’re doing right now is probably not what we will be doing 10 years from now. If people understand that narrative, they will be on board with innovation.