In implementing HR tech, put the user experience at the forefront, says Travis Windling of RBC
The biggest piece of advice when implementing a new piece of HR tech? Put the user experience at the forefront of everything you do.
That’s key, according to Travis Windling, Senior Director, TA Strategy, Operations & Contingent Workforce at Royal Bank of Canada, along with prioritizing a human-centric approach to development.
“[It’s about] making sure that we have a lens to where are people going to be consuming this,” he tells HRD. “I think this is going to become prevalent. Every vendor thinks that [their] platform is the be all and end all - that this is where people should play. This is a gap we have in the marketplace.
“As I think about it from an enterprise perspective, it's more about figuring out the data management techniques that I want my recruiters to use beyond the tried and tested. Who applied first? That's what I look at first in the age that we're in today with the volumes that we see. That's where I would bridge between the analytics, data and technology landscape and the human experience side. It's taking that human-centered design approach.”
With nearly 15 years of experience in managing tech-driven transformations, Windling is bringing his sector knowledge to HRD’s upcoming HR Tech Summit on June 4th at The International Centre in Mississauga. In Windling’s session, he’ll be speaking about how to update your approach to talent acquisition and retention by leveraging new modes of outreach, such as TikTok and Instagram – as well as looking at the challenges of implementing large-scale SAS solutions.
“The biggest challenge in that is risk,” adds Windling. “Especially with the regulatory environment going where it is, there’s now a lot more rigor around what we need to do from a privacy compliance and transparency perspective.”
But again, this all comes back to managing the human-centric approach in tech rollouts, he says.
“There’s two personas you need to be concerned about: the first is recruiters in the flow of work; the second is, from a candidate perspective, making sure the candidates know what to expect. We’re looking to build a framework where candidates know what to expect when they apply to a job.”
Such transparency not only clarifies the process for candidates but also builds trust by detailing the elements involved in decision-making—particularly when AI and statistical models are in use. Explaining further, Windling emphasizes the importance of transparency in the modeling process itself, a critical aspect of mitigating risk and ensuring compliance.
“If we are using AI, or any kind of statistics and prioritizing across our model, then we'll tell [candidates] what that looks like in terms of what elements we're using. From a risk perspective, it goes a long way to be able to articulate to your risk and compliance folks that we don't use school names, we don't use gender or ethnicity. And from a socioeconomic status perspective [issues] like location can be problematic as well. I think it's being able to clearly articulate what you don't use in your model as much as what you do.”
From a people analytics perspective, Windling follows the same ‘human first’ approach – beginning by looking at what’s important to the people and the business and building a strategy out from there.
“It's really important to bifurcate your team into the new build versus the ‘business as usual’ side of things. From a people analytics perspective, where I would take that in terms of an organizational structure to give people the time and space they need to deliver, it's about having one segment of your team focused on research, new development, figuring best practice. The second segment, once you have those insights, should build this into the flow of work.”
And it’s this implementation of analytics findings where Windling's insights become particularly intriguing. He discusses the operationalization of a common analytics tool known as the flight risk model, which predicts the likelihood of employees leaving the company.
"There's a lot more nuance in how you actually operationalize that flight risk model. What are your employees and your organization ready for? Where do I put this information? Who sees the information? Who's going to act on this information? What are they going to do? How am I going to measure those actions that they take? That's the second piece - it's both a technical and non-technical implementation perspective.”
Looking ahead to what the future holds for data handling in HR, Windling tells HRD that it’s very much AI’s game.
“From a generative AI perspective, it's really changing the landscape around analytics and talent attraction in particular - it's lowered the barrier to entry for candidates.”
However, this shift brings its own set of challenges. As more candidates use generative AI to enhance their applications, distinguishing between genuinely qualified candidates and those merely proficient in using AI tools becomes increasingly difficult.
"A lot of the tools that we've used in the past aren't going to work anymore because everybody's going to look the same," adds Windling. “That level of validation, can we put that beyond the scope of the generative AI so you're not giving people the ammunition or the prompts to feed into the environment? It's much harder for AI to predict what the right answer is.”
By focusing on attributes like whether a candidate is more driven by execution and project planning or by innovation and creating new things, employers can gain a more nuanced understanding of how a candidate might perform in a specific context. This method also safeguards the recruitment process from being gamed by candidates using AI to craft seemingly perfect responses.
"Your answer key is a little bit more hidden in terms of what that looks like. So candidates can't necessarily use AI to trick you in the same way.”
At the upcoming HR Tech Summit, Windling will be speaking on:
Don’t miss your chance to join Windling – and other industry icons such as Hudson Bay, Krispy Kreme Doughnuts and IBM – book your tickets here.
