AI adoption at SoundCloud was happening the way it happens everywhere: enthusiastically, in parallel, and without a shared spine. Teams across product, engineering, legal, marketing, trust & safety, and operations were each evaluating vendors, writing their own prompts, and forming private opinions about what was safe. The energy was real. The duplication and risk were also real.
The request was
“Pick our AI tools and set the policy.”
Discovery showed
The company did not have a tooling problem. It had a decision-making problem, no shared way to judge which work should be automated, who owned the outcome, and what evidence made a use case safe to ship.
A policy alone would have produced governance theater: a document nobody reads, applied by a committee nobody wants to schedule. What was actually missing was an operating model, access, routing, and guardrails built into the path of least resistance, plus a roadmap that ranked opportunities by impact rather than by whoever asked loudest.
- Interviewed leaders across six functions to inventory in-flight AI experiments and the decisions each one was actually blocked on.
- Mapped support, trust & safety, monetization, and internal ops workflows against volume, cost, error tolerance, and reversibility.
- Analyzed spend and shadow usage to size the real cost curve rather than the projected one.
- Pressure-tested legal and privacy constraints early, so the roadmap was built inside the real boundaries instead of discovering them at launch.
Exhibit
Task Autonomy Framework
| Knowledge depth | AI suitability | Integration | Autonomy level | |
|---|---|---|---|---|
| 01Tier 1: Repetitive, low ambiguity | Shallow, documented | High | Read-only | Full autonomy, sampled review |
| 02Tier 2: Judgment inside known rules | Policy + context | High with retrieval | Read + write | Autonomous, human on exception |
| 03Tier 3: Judgment with real consequence | Tribal + policy | Assistive | Write with audit | Human in the loop, always |
| 04Tier 4: Novel, contested, or regulated | Expert only | Drafting only | None | Human decides, AI supports |
The tiers did the political work: teams stopped debating whether AI was 'safe' in the abstract and started placing individual tasks on a shared ladder.
Exhibit
Enterprise AI Reference Architecture
01Governance & policy
Data classification, acceptable use, vendor risk, retention
02Evaluation & telemetry
Eval harnesses, quality signals, logging, cost and latency
03Orchestration & agents
Shared prompt and tool registry, agent patterns, handoff rules
04Model access & routing
One gateway, per-team budgets, model selection by task tier
05Data & retrieval
Classified sources, retrieval boundaries, audit on sensitive flows
Shared infrastructure, not point solutions, the difference between 30 pilots and one platform.
- 01An AI-first operating model aligning product, engineering, legal, marketing, trust & safety, and operations around one strategy.
- 02A data-driven AI roadmap that surfaced the opportunities with the biggest payoff across the business and sequenced quick wins against platform investment.
- 03Centralized enterprise AI access, routing, and governance with cost and risk controls built in.
- 04The AI Collective, an internal community of practice that accelerated responsible experimentation without approval friction.
- 05Production AI agents in customer operations, plus the guardrails, evaluation patterns, and playbooks that made them repeatable.
She consistently cut through the noise, evaluating opportunities and pressure-testing their potential.
Maria Von WatzdorfVP, Product at SoundCloudRead in full →- Strategy
- Owned the company-wide AI strategy and roadmap end to end.
- Architecture
- Set the reference architecture with engineering and data partners.
- Governance
- Built classification, access, and vendor-risk standards with legal and security.
- Facilitation
- Founded and ran the AI Collective; office hours, demos, enablement.
- Operating metrics
- Defined containment, CSAT, and deflection targets; reported to executives.
- Reduced ticket volume and increased self-resolution through deployed customer-operations agents.
- Moved AI spend from untracked shadow usage into governed, budgeted, attributable routing.
- Established containment, CSAT, and deflection as executive-visible operating metrics.
- Left behind reusable AI Ops patterns that continued to scale past the original implementations.
I underestimated how much of this job was social. The architecture was tractable within weeks; the trust took quarters. If I did it again I would launch the community of practice first and let the platform follow the demand it surfaced, rather than the other way around.