AI is moving faster than traditional operational models can handle. Design teams are expected to move quickly, adopt new tools, and centralise systems - making static playbooks obsolete. What they need instead is the ability to adapt without losing the essence of design.
Sara presents a practical blueprint for Adaptive DesignOps: shifting from playbook-driven operations to a pilot-driven system that learns, flexes, and scales with technological and organisational change.
Drawing on experience across multiple Amazon organisations and FinTech - including AI-assisted workflow pilots, modernising design lifecycles, building centralised workload taxonomies, and running AI experimentation cohorts - Sara shares mechanisms that make adaptability rigorous and scalable.
The Builder–Operator Role
Why DesignOps leaders must both design the system and operate it, shaping workflows and refining mechanisms in real time.
Enabled by:
- Centralised Scaffolding: Shared checkpoints, intake taxonomies, and guardrails that support local experimentation without breaking the system.
- Pilot-Driven Operations: Lightweight pilots for testing workflow changes, rituals, and AI tools before scaling.
Adaptability as a Capability
Resilient organisations aren’t the most compliant - they’re the ones that can evolve quickly without losing clarity, intent, or design’s bar-raising purpose.
Enabled by:
- Learning Velocity: Short-cycle experiments, AI-assisted synthesis, cohort testing, and inspection points that turn ambiguity into insight.
- Operational Sensing: Capacity signals, workflow friction, sentiment pulses, and usage patterns that surface misalignment early.
You’ll leave with an actionable model for adopting AI safely, scaling responsibly, and protecting design’s strategic advantage - human judgment and intent.