Answer extracted from The Josh Bersin Company podcast — listen to the full episode below.
How can an HR organization continue transformation when technology funding disappears unexpectedly?
Rather than pausing the transformation until funding returns on an indefinite timeline, redesign the transformation itself to work with available tools and reduced dependencies. This means shifting from "what technology must we invest in?" to "how do we accomplish our goals with what we already have?"
From perfect vision to practical reality
When Seneca Polytechnic faced an unexpected budget cut in the higher education sector in Canada—stemming from structural changes in regulatory funding—Diana Mouhan's transformation team faced a binary choice: pause everything until money returned, or rebuild their roadmap entirely.
They chose the latter. The team stopped asking which new technologies they needed to acquire and started asking fundamentally different questions: how to redesign with existing tools, how to reduce funding requirements without abandoning vision, and how to adapt to a leaner operating model. As Diana Mouhan explains in the episode, this wasn't a retreat—it was a recalibration.
The shift required vulnerability and transparency with the team about constraints. Rather than presenting a false sense of unlimited possibility, Mouhan's leadership acknowledged the real barriers and invited the organization to problem-solve within them. This honest approach actually strengthened trust and ownership across the transformation office.
Practical outcomes of constraint-driven design
Working within these tighter parameters, Seneca's HR team automated processes using ServiceNow—a platform they already had—and redesigned their knowledge management. Over 100 knowledge articles were refreshed to enable more self-service, driving employee response times down from nearly two weeks to less than 24 hours.
More than 90 percent of employee interactions moved to the ServiceNow platform, a result achieved not through expensive new technology but through thoughtful redesign of existing systems and clearer user pathways. The transformation continued forward, proving that funding constraints don't have to mean transformation pause—they can mean transformation refocus, as detailed in this podcast conversation.
"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. Diana is a transformation specialist with over a decade of experience in organizational strategy, AI adoption, and the future of work. Her career spans roles at Deloitte and Kearney, bringing deep expertise in navigating organizational change and technology transformation during uncertainty.
What made Seneca's pivot especially instructive is how Diana Mouhan's team maintained their strategic vision while fundamentally changing their operational approach. Rather than scaling back ambition, they scaled back assumptions about how the work had to be done. Learn more about how her team maintained agility and managed stakeholder expectations in the full episode on Listenly.
Key takeaways
- Transformation doesn't require new technology—redesign your roadmap to maximize existing tools and capabilities.
- Shift from "what do we need to buy?" to "what can we achieve with what we have?" as your guiding question.
- Acknowledge constraints openly and invite your team to problem-solve within them—this builds trust and ownership.
- Practical redesign can yield measurable results: Seneca reduced response times from two weeks to under 24 hours using existing systems.
See also
What is the adoption curve when introducing AI-driven enablement tools to traditional L&D and business teams?
Greater than 60 percent of people initially fear that AI technology will replace their jobs, but disarming these concerns through messaging that emphasizes the augmentative nature of AI helps drive adoption significantly.
How can data integration from multiple organizational systems enable skills inference and personalized enablement at scale?
By consolidating data from sources like Salesforce, product infrastructure, and customer interaction systems into a unified data lakehouse, organizations can create comprehensive skill profiles and deliver personalized learning experiences at scale.
What role do subject matter experts and non-technical business users play in AI-powered learning systems?
Non-technical business users tend to adopt and innovate with AI technology more effectively than technologists because they focus on solving business problems rather than technical constraints.