Answer extracted from the The Josh Bersin Company podcast — listen to the full episode below.
What foundational elements must precede successful AI implementation in HR transformation?
Before pursuing AI initiatives, organizations must first establish data governance, process redesign, and knowledge management—the three foundational blocks that enable AI to deliver real impact. These fundamentals create the clean data, efficient processes, and accessible knowledge that AI systems depend on to function effectively.
Why the order matters: foundations before innovation
Many organizations rush to implement AI without preparing the ground beneath it. Diana Mouhan, Executive Director of Transformation and Change at Seneca Polytechnic, observed this tension during her institution's HR transformation journey. When budget pressures emerged, the team had a choice: pause the entire transformation indefinitely, or redesign it using the tools and fundamentals already at hand.
They chose the second path. Rather than asking which new technologies to invest in next, they refocused on working with what they had and ensuring the basics were right. This shift proved decisive. By strengthening data governance first—ensuring consistent, clean data across systems—and redesigning processes to eliminate unnecessary steps, they created the conditions for both current operations and future AI adoption to succeed.
As Diana explains in the episode, this discipline meant refreshing over 100 knowledge articles to improve user experience and self-service, which in turn fed into faster response times—eventually dropping from nearly two weeks to less than 24 hours.
The three pillars: data, processes, knowledge
Data governance is the first pillar. Without clear ownership, validation rules, and maintenance protocols, AI systems inherit garbage and amplify it. Seneca made this explicit: who owns each data field? When is it updated? What counts as valid? These questions, while unglamorous, determined everything downstream.
Process redesign is the second. Seneca's team didn't simply automate broken workflows—they examined every HR process and asked whether it was necessary at all. Some steps vanished. Others were consolidated. The result: employees interacting with HR through a single, modern platform rather than scattering requests across email, phone, and fax. More than 90% of interactions consolidated onto ServiceNow.
Knowledge management is the third. Seneca invested in making their internal knowledge base searchable, current, and useful. This doesn't sound like "AI transformation," but it enables self-service, reduces support volume, and gives AI agents better content to reference when they do engage. A point detailed in this podcast.
"Trust isn't just telling people don't be afraid. Trust is actually built on acknowledging the concerns that they have and being super vulnerable in that journey with them."
Diana Mouhan — Executive Director of Transformation and Change at Seneca Polytechnic. A transformation specialist with over a decade of expertise in organizational strategy, AI adoption, and the future of work, Diana has led transformations across Deloitte and Kearney before moving into executive roles, where she now guides institutions through the intersection of technology and human change.
Trust, in Diana's view, isn't manufactured through reassurance alone. It emerges from honest acknowledgment of concerns and transparent partnership in the change process. When she describes building foundations first, she's also describing a change management posture: admit what is uncertain, show up alongside people in the discomfort, and build capability together rather than imposing solutions.
Seneca's team began with only 25% to 30% of the HR staff trusting AI for workspace utilization. Rather than dismiss that skepticism, they used it as feedback. They strengthened the fundamentals, ran pilots transparently, and let results speak. The cultural shift followed naturally once people saw faster service, fewer errors, and their own workload becoming more strategic.
The broader lesson surfaced in the full conversation: when you establish these three pillars before deploying AI, the technology becomes an accelerator of what's already working, not a band-aid for what's broken.
See also
What operational improvements result from digitizing HR service delivery and automating routine work?
More than 90% of Seneca's employee interactions moved onto the ServiceNow platform, compared to being spread across email, phone, fax and other channels, enabling faster response times and improved self-service capabilities.
Why should AI agents be formally integrated into organizational charts?
Diana intentionally embedded agents into Seneca's org chart to create clarity and ownership around who manages each agent and holds responsibility for its performance and governance.
What practical AI use cases can HR teams implement across workforce operations?
Seneca Polytechnic deployed AI across workforce planning, organizational design, job architecture, change management, and compensation analysis to drive operational efficiency and inform strategic decisions.