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Becoming AI Native: What ServiceNow’s People & AI Model Signals for the Future of Building Lifecycle Management

Posted by [email protected] on Nov. 23, 2025  /  Lifecycle Insights: Jump into the Conversation  /  Subscribe 0

ServiceNow’s approach to becoming an AI-native organization offers one of the clearest blueprints yet for how enterprises can embrace AI at scale. In a recent conversation between Josh Bersin and Jacqui Canney—ServiceNow’s Chief People and AI Enablement Officer—several themes emerged that directly align with the transformation now underway in commercial real estate (CRE) and Building Lifecycle Management (BLM).

Rather than treating AI as a standalone technology shift, ServiceNow frames it as a capability shift, emphasizing workforce clarity, confidence building, and practical use cases. As BLM pushes the CRE industry toward long-term, data-driven, and collaborative lifecycle practices, these insights illuminate how organizations can ready their people, processes, and structures for AI-enabled operations.

This article summarizes the key ideas from the ServiceNow discussion and projects how these same patterns may unlock new opportunities for BLM as adoption accelerates.

Workforce X-Rays and Role Mapping: The Foundation of AI-Native Transformation

ServiceNow began its AI transformation not by deploying tools, but by conducting a company-wide capability X-ray. Every role was mapped to understand how AI would reshape daily work, where skills gaps existed, and how personalized learning paths could be developed. Even the board took the same capability assessment—signaling that AI readiness is shared work, not delegated work.

This structured, human-centric approach delivered clarity, alignment, and psychological safety. Training then evolved from vocabulary-building, to daily use, to building with AI, and eventually to preparing managers to lead confidently through change.

Solving Real Friction: AI Use Cases That Deliver Measurable Impact

ServiceNow evaluated more than 1,000 HR-related AI use cases, narrowed them to 27 high-value opportunities, and created an AI control tower to track progress. These were not experiments—they delivered operational improvements, including:
Survey insights made more actionable.
Growth conversations enriched through data-informed prompts.

HR as a Product Team: A Model for Lifecycle Roles Becoming More Strategic

One of the most forward-looking ideas Jacqui Canney shared is that HR is evolving into a function that behaves more like a product organization—owning experiences, iterating continuously, and advising leaders with evidence-based insights.

This shift mirrors a trend already identified in the BLM maturity model: as repetitive tasks become automated, human roles move toward:
Strategic advisory work.
Continuous improvement.
Experience design.
Advanced data interpretation.

Becoming AI Native: Simple, Replicable Behaviors

The interview concluded with five behaviors that define AI-native organizations:

Implications for BLM

As AI adoption accelerates, several themes emerge that directly strengthen Building Lifecycle Management. Workforce readiness becomes foundational, mirroring the importance of capability mapping and shared learning across stakeholder groups. AI’s ability to reduce friction—whether in data validation, maintenance forecasting, or complex coordination—supports BLM’s goals of interoperability and more predictable lifecycle performance. Roles across design, construction, and operations also evolve toward more strategic, advisory functions as routine tasks are augmented by intelligent tools, echoing BLM’s progression toward proactive and predictive management. Together, these shifts enable more integrated data ecosystems, clearer governance, and greater alignment across the building lifecycle.

Conclusion

ServiceNow’s journey provides a clear message for CRE and BLM stakeholders: becoming AI native is not primarily a technology challenge—it is a confidence, capability, and leadership challenge. Mapping roles, enabling shared learning, focusing on high-value friction, and building governance around real use cases produces momentum that technology alone cannot.

For BLM, this represents a strategic opportunity. As AI adoption widens across CRE, lifecycle practices will become easier to implement, easier to scale, and more integrated into daily work. The combination of AI-native behaviors and lifecycle thinking will accelerate progress toward:
Higher asset performance.

What does this mean for your organization? The sooner leaders embrace these human-centered AI practices, the faster they will unlock the strategic, operational, and financial advantages promised by Building Lifecycle Management.

Acknowledgements: This article synthesizes insights from the public discussion between Josh Bersin and Jacqui Canney, Chief People and AI Enablement Officer at ServiceNow, as well as interpretation of commentary originally published by Daan Van Rossum, CEO of FlexOS. These sources informed the analysis presented here and are cited in recognition of their contributions.

Josh Bersin’s conversation with Jacqui Canney

Join the conversation—how is your organization preparing to become AI native and lifecycle ready?

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