Discovery, value mapping & the 90-day pilot

The fastest way to lose an enterprise deal is to open your laptop and start a demo. Your job isn't to show what Cursor does. It's to find out what is actually broken in how this org ships software, then prove Cursor moves that needle. Diagnose first, prescribe second.

A demo that lands on the wrong pain is worse than no demo. You've spent your one moment of attention proving you didn't listen. The Field Engineer who wins can sit with a VP of Eng and reconstruct, in their language, exactly where their delivery pipeline stalls before showing a single completion.

That reframe earns you three things:

• It signals you're an engineer, not a vendor.
• It pushes them to expose the real bottleneck instead of a sanitized one.
• It buys you the right to be selective about what you demo.

Selectivity is credibility.

Structured discovery beats charisma. You map seven dimensions and every answer gets one of three tags:

• A fact (they told you, you verified).
• A hypothesis (your inference, still needs confirming).
• An unknown (you don't know and it matters).

That tagging discipline is what separates a real account plan from a wishlist.

Instrumenting the 'current AI usage' dimensionread adoption, don't guess it

'Current AI usage' is the dimension reps fudge the most. They log 'they use some AI' and move on. There's a concrete instrument for reading it. Once a customer is in (or in a pilot), the admin analytics give you two signals you read together: a team's or person's request volume and their acceptance rate of committed AI code. Plot them as a quadrant and each cell prescribes a different action.

In nearly every enterprise you walk into, engineers are already pasting code into consumer chatbots and leaning on unsanctioned assistants. Procurement doesn't know. Security definitely doesn't. A bad rep's instinct is to weaponize this: 'your developers are leaking IP!' Don't. Fear sells one meeting and poisons the relationship.

This is where Cursor's enterprise control plane becomes the pitch, not the features. Ungoverned chatbot usage means no audit trail, no data-handling guarantees, no model allowlist and source code leaving the building. Governed Cursor usage gives the org Privacy ModeCursor's setting that routes requests under zero-data-retention terms so providers don't store or train on your code. + ZDRZero Data Retention. A contractual guarantee that the model provider won't store your code or train on it., model/MCPModel Context Protocol. A standard that lets an AI agent pull in context from outside the repo, like Jira tickets or internal docs./repo allowlists, SSOSingle Sign-On. One company login (usually via SAML or OIDC) instead of a separate password per tool./SAMLAn enterprise standard that powers single sign-on./OIDCOpenID Connect. A modern standard that powers single sign-on, built on OAuth. with SCIMSystem for Cross-domain Identity Management. A standard for automatically creating and removing user accounts when people join or leave., RBACRole-Based Access Control. Granting permissions by role rather than configuring each person individually., audit logs and AI-code tracking. Same productivity, now with a paper trail security can defend.

Discovery surfaces pain. Value mapping converts it into a number the economic buyer cares about. The chain is always the same:

1. 1A persona's pain.
2. 2The Cursor capability that addresses it.
3. 3The metric that proves it.
4. 4The economic translation into engineer-hours, cycle time or time-to-first-commit.

The translation step is non-negotiable. A capability with a metric but no economic unit dies in the procurement review. 'BugbotCursor's automated PR reviewer that posts inline findings and can push fix commits from isolated VMs. finds 10% more bugs' is a feature. 'Bugbot cuts escaped defects and at your incident rate that's N fewer Sev-2s a quarter at $X each' is a business case.

ROI is a journey: qualitative → quantitative → dollarsmeet the buyer where their proof is

Proving ROIReturn on Investment. The value gained versus what it cost, the language an economic buyer funds deals in. isn't one number, it's a staged climb and where the customer sits on it tells you what evidence they'll accept. Start with qualitative signal (are engineers excited, do they feel more productive), move to quantitative productivity (PR velocityHow quickly pull requests are merged; the easiest delivery metric to measure, though it sits furthest from the customer outcome., code quality, bug counts, % of code turned over), then land on real dollars.

Use the highest-credibility evidence the customer is ready for, and reach for the dollar framing the moment a CFO is in the room. The 'pull-forward revenue' line is the one that lands with finance, because it reframes the spend as revenue acceleration rather than a cost. (Treat the specific percentages as perishable and verify before quoting them.)

The most common adoption failure treats rollout as a headcount ramp: 'get 200 seats live.' Wrong axis. You stage capabilities by trust, not users by quantity. Each rung is a more autonomous way of working and you only climb when the org has earned the trust to hold the rung below.

1. 1Assistive - completions and in-editor chat. The human writes every line, AI accelerates. The trust floor.
2. 2Governed workflow - sanctioned rules (.cursor/BUGBOT.md), model/repo allowlists, Privacy ModeCursor's setting that routes requests under zero-data-retention terms so providers don't store or train on your code. on. AI inside guardrails.
3. 3Repository workflow - BugbotCursor's automated PR reviewer that posts inline findings and can push fix commits from isolated VMs. on PRs, codebase-aware agents operating across a repo with review gates.
4. 4Pipeline / SDK - agents wired into CI/CDContinuous Integration / Continuous Delivery. The automated pipeline that builds, tests and ships code so changes reach production safely and often. and programmatic flows; bounded fixes prepared by isolated cloud agents and reviewed by humans.
5. 5Higher autonomy - Cloud AgentsAgents that run in a Cursor-managed virtual machine, check out the repo, do the work and open a pull request, then shut down, with no load on your laptop. with reusable environments, /in-cloud subagents, local/cloud handoff, terminal + browser, minimal supervision.

Locate the org with the 'three eras' vocabularyattention, not features

The ladder maps onto Cursor's own framing of where the industry is, defined by where a developer's attention goes. Use it to place an org out loud:

The gate between rungs: artifact-verifiabilityforeground vs background

What actually lets an org climb a rung is whether the work can be verified through an artifact. That's the foreground-vs-background decision rule and it's the most useful heuristic you can give a customer. Foreground agents are for interactive, ambiguous, security-sensitive or steered work where you make decisions and watch every step. Background / cloud agents are for execution against clear goals and acceptance criteria. The test: if the output is reviewable through an artifact (an image, a recording, logs, a diff), push it async; if it's fuzzier, stay foreground.

For 100–500 engineers, the pilot runs in three 30-day acts and the sequencing is the strategy: guardrails first, enablement second, expansion third. Flip that order and you get a viral tool with no governance and a security team that kills it in month two.

Start narrow, then earn the expansionthe rollout pattern

Don't roll out to everyone at once. The pattern Cursor recommends: pick a repo, a few tasks and a few engineers; prove measurable impact; expand to a couple more teams; share results up; and only scale org-wide once you've seen strong ROIReturn on Investment. The value gained versus what it cost, the language an economic buyer funds deals in. on specific tasks. The narrow start is what gives the four-lens scorecard a clean signal and what keeps the security team comfortable while trust is still being built.

The recommended first automationlow risk, high stickiness

When a pilot team asks 'what's the first automation we should set up?', there's a data-backed answer: 'Summarize Changes Daily.' It posts a Slack engineering digest of notable repo changes every day at 10am PST. Cursor's data science found it the stickiest automation at 89% retention last month. It's the ideal opener because it's low blast radiusHow much breaks if a change goes wrong; the scope of potential damage. (it reads and reports, it doesn't touch code), it's visible to the whole team and it builds the daily habit that makes everything above it on the ladder feel normal.

You measure against a four-lens scorecard and you set the baseline before you start. A pilot with no day-zero baseline proves nothing, because every result answers 'compared to what?'