Résumé
Katy Simmons
AI & Product Executive
I figure out how work actually happens, what gets in the way, and what has to be true before AI can improve it. Then I build the system, decide how much authority it gets, and check whether the outcome got better.
- 16+ yrs
- Product & operations leadership
- 400%
- Market share growth at Apptegy
- $20M
- ARR contributed to
- 4,000
- Customers at scale
- Global
- Platforms, trust & safety, regulation
01Experience
2026–Present
honeycomb.io
AI Operations Lead
- 36 structured discovery sessions across GTM, Finance, Marketing, and Engineering in my first few months. I ask people to show me the work, not describe the idealized version of it.
- 23 experiments identified, 4 active pilots by late July. I call them experiments when we are still trying to learn something, and solutions when we already understand the problem.
- Designed a GTM operating system: Claude plus MCPs and skills for reasoning and execution, Notion for persistent context, Slack for human interaction, connected systems for data and actions, and write-back so the record stays current.
- Decomposed GTM into 100+ tasks and 400+ subtasks. "Automate sales" tells me nothing. Task level is where AI decisions become real.
- Built a task-level assessment framework covering understanding, context, AI suitability, integration, notification, autonomy, risk, and value. Capability is not readiness.
- Defined four autonomy levels: human only, human in the loop, human on the loop, fully autonomous. "Keep a human in the loop" is too vague to act on.
- Reverse-engineered ARR and iACV logic from contracts and the Snowflake datamart, turning a week of manual reconciliation into exception review at 89% clean contracts.
- Reviewed the existing AI policy set and asked what kind of thing each document was: principle, norm, guidance, or actual operating rule. Employees need answers at the moment they need them, not a binder.
- Presented the first synthesis to EStaff as "AI Operations: the evidence changed the question," reframing the work as operational readiness rather than a list of use cases.
- Defining the function while doing it: discovery, synthesis, experimentation, prototyping, system design, evaluation, facilitation, enablement, and shared methodology that other people can run without me in the room.
2024–2026
SoundCloud
Director, AI Operations
- Built and ran an AI-first operating model across Product, Engineering, Legal, Marketing, Trust & Safety, Community Operations, monetization, and internal operations.
- Moved the company from scattered individual AI usage to centralized enterprise access and routing. Centralization was not the goal. Visibility, reuse, and cost control were.
- Founded the AI Collective, an open forum for demos, questions, and early feedback, so AI Ops never became the approval desk for experimentation.
- Owned support AI end to end: agent deployment, containment, CSAT, deflection, and whether the customer came back five minutes later anyway. A bot conversation that does not solve the problem is not success.
- Rebuilt support knowledge as infrastructure across a 691-article Help Center: architecture, search, quality, ownership, and the loop from failed answer to new content.
- Connected bot and ticket data in Looker so we could see the whole journey instead of grading the bot and the humans as separate systems.
- Led MAIA, AI-generated content detection: 99.36% accuracy, 99.75% precision, 8 tracks per second, roughly 700K per day, in service of transparency, platform integrity, and differentiated monetization.
- Ran age assurance against UK and Australia regulation, evaluating Persona, Yoti, and VerifyMy against accuracy, privacy, friction, integration, and a first-year cost range of roughly $370K to $860K.
- Shipped AI-supported copyright review, license summarization, and contract indexing so Legal spent its time on judgment instead of retrieval.
- Applied AI to artist distribution and monetization review. Some of the best AI work is invisible: the artist just waits less.
- Designed internal agents and a centralized knowledge model, an early version of the agentic operating systems I design now.
2023–2024
The Weather Company
Senior Product Manager, AI
- Launched an AI-first support entry model with the AI Agent as the primary consumer interface, escalating with context instead of handing humans a blank slate.
- Designed a Copilot model for human agents, because AI should improve the work humans still have to do, not only the work it takes over.
- Defined automation confidence thresholds so "the model produced an answer" never meant "send it to the customer." Answer, assist, escalate, or refuse.
- Wrote AI Operating Procedures covering what the system may do, what source of truth it uses, when a human steps in, and who owns the outcome. That made trust operational.
- Led the redesign of support into a Support Hub combining troubleshooting, product education, value discovery, and expertise, since a customer in support is already trying to understand the product.
- Rebuilt the knowledge loop: product changes, knowledge changes, AI knows, customers get better answers, gaps surface. A stale AI support system is a very confident way to be wrong.
- Established shared taxonomy, metadata, and automated classification so routing, analytics, and product feedback had something reliable to stand on.
- Built the analytics foundation around containment, CSAT, BSAT, deflection, and content gaps, and refused to treat containment alone as proof of success.
- Made support a sensor for Product: if the same issue generates hundreds of cases, the goal is not to get very efficient at answering it forever.
- Got sharp about deterministic versus interpretive work. Exact subscription status does not need creativity. An angry customer with a messy explanation might.
2023
The Venture Center
Program Manager, Entrepreneurship
- Designed and facilitated entrepreneurial workshops for early-stage founders.
- Served as product consultant to startups across the FinTech accelerator portfolio.
2018–2023
Apptegy
Senior Product Manager
- Drove 400% market share growth: 800 to 4,000 customers.
- Contributed to sustained growth reaching $20M ARR.
- Launched a second major product, scaling from 2 to 436 customers in two years.
- Established product strategy and operating models that became the foundation for long-term growth.
2015–2019
Arkansas Women's Outreach
Co-Founder
- Built the organization and its direct-distribution model from nothing.
- Focused on women's health access in the homeless community.
2008–2018
BBA Corp
Director of Product
- Initiated the company's expansion into collegiate mobile, earning recognition across the collegiate market.
- $1M first-year e-commerce revenue; scaled into a multi-campus enterprise offering.
- Built and developed high-performing product teams over a decade.
02Focus areas
- AI strategy & operating models
- AI governance, evaluation & cost controls
- Product-led support & self-service
- Trust & safety, age assurance, regulation
- Platform & workflow automation
- Product operations & org design
- Team building & people development
- Go-to-market & scaling
03Deeper context