Application artifact · Maura K. Randall · April 2026

The Always-On Operator: redesigning onboarding, support, and growth for an AI-native Toast

The growth imperative

Toast serves 140,000 locations and is targeting 500,000. The GTM motion that got you to 140K was built around human teams — onboarding consultants, support specialists, customer success managers. That motion is exceptional. It’s also the constraint on getting to 500K.

The redesign brief

You can’t hire your way from 140K to 500K locations. But you can build an AI-native GTM motion that carries the standard your human teams set — to every operator, at every stage, simultaneously. That’s not a cost-cutting story. That’s a scaling story.

The provocation

The best onboarding, support, and growth experience a Toast operator ever has is the one they never had to ask for — because Graze was already there.

🌿

Meet Graze

An AI-native agent that stays with every Toast operator from contract signed to multi-location expansion. Graze doesn’t hand off to onboarding, then to support, then to customer success — Graze is all three, always carrying the full context of that operator’s business. Named for the way it works: continuous, ever-present, always ready.

This is not a headcount story

Toast’s human teams built the standard Graze delivers. They become the architects, quality monitors, exception handlers, and relationship owners — working at a leverage ratio that was never possible before. Graze doesn’t replace the human who cares about the operator. It means that care reaches every operator, every day — not just the ones who got lucky with their consultant.

1

Before the first transaction

Onboarding reimagined

Current motion

  • Dedicated consultant assigned post-contract
  • Kickoff call to outline the process
  • Menu build, POS configuration, hardware install
  • Toast University + training calls
  • Go-live with live support

What Graze makes possible

  • Graze processes the menu upload and generates a custom POS configuration plan in real time — operator sees it immediately, iterates conversationally
  • Kickoff becomes a live co-build: Graze runs the session, builds the setup, project-manages the timeline
  • Training is contextual and operator-specific — not generic modules
  • Contract-to-live time measured in days, not weeks

Human teams focus on

  • Complex multi-location setups requiring on-site judgment
  • Operators who want a human in the room
  • Setting the standard: reviewing Graze’s outputs, catching edge cases, feeding corrections back
2

Every day after launch

Support redesigned from reactive to predictive

Current motion

  • 24/7 phone, chat, and ticket support
  • Screen sharing for troubleshooting
  • Toast Central knowledge base
  • Reactive: operator contacts Toast when stuck

What Graze makes possible

  • Live chat is no longer generic — it’s a custom Graze instance that knows this operator’s menu, staff, and history
  • Graze flags issues before the operator feels them: menu pricing anomaly, labor scheduling conflict, inventory pattern predicting a Saturday stockout
  • Proactive updates on open tickets — no more chasing status
  • Voice commands during service — support that works when the operator can’t stop to type

Human teams focus on

  • Complex, novel issues Graze escalates with full context already packaged
  • Operator relationships requiring human empathy and judgment
  • Defining resolution standards Graze is measured against
3

As the business grows

Growth as operational intelligence

Current motion

  • Account teams drive expansion conversations
  • Upsell prompts for add-on products
  • Growth motion largely disconnected from operator’s operational data

What Graze makes possible

  • Graze reads operational signals across 140K+ locations and surfaces the right product at the right moment — online orders crossed a threshold, delivery integration appears
  • Labor costs spike three weeks running — Graze prompts the scheduling tool before the operator thinks to ask
  • Second location — Graze already knows location one’s setup and starts location two from that context

Human teams focus on

  • Strategic enterprise relationships requiring executive-level judgment
  • Defining the expansion playbook Graze executes at scale
  • New product positioning that Graze hasn’t learned to surface yet
🧪

Closing the loop internally

Graze as product intelligence and experimentation engine

What Graze observes

  • Every friction point across 140K+ onboarding journeys
  • Features consistently abandoned in weeks two and three
  • Workarounds operators invent that the product team never anticipated
  • Support patterns that repeat across segments and geographies

What Graze delivers internally

  • Monthly and quarterly opportunity briefs — prioritized by frequency, severity, and operator segment
  • Real-time friction alerts when a new issue pattern emerges across multiple operators simultaneously
  • A/B and funnel signals that inform experiment design — grounded in what operators actually do, not what they say in surveys

Why this changes product strategy

  • Roadmap decisions informed by real operator behavior, not just NPS scores and ticket volume
  • Every customer has a Graze — Toast has a continuous pulse on the entire operator base that no competitor with human-only GTM can match
  • Product teams make faster, more confident decisions because the operator signal is continuous, prioritized, and already waiting for them

How you get there responsibly

Onboarding, support, and growth don’t get AI injected all at once. Each stage moves through a roadmap of iterative tests — clear hypotheses, defined success metrics, A/B frameworks to catch any downstream impact before it compounds. Success looks like: decreased contract-to-live time, reduced adoption friction, fewer reactive support contacts, and measurable expansion revenue driven by operational signals rather than sales cycles.

A note on this artifact

I’ve worked in restaurants. My partner is a chef. I know what it means to be in the weeds during a dinner rush when the system goes wrong — and I know that the best technology in a restaurant kitchen is the technology that disappears, because it just works. This is a first-principles point of view built from the outside. The best version gets pressure-tested with the team that knows the operator data from the inside.

Maura K. Randall

maurakrandall@gmail.com · Austin, TX