Interface-First AI Startup Checklist (2026)

Use this to evaluate your product before you add more models, prompts, or “agent” features.

1) Name the system of record
- What is the canonical source of truth (Salesforce, Jira, GitHub, ServiceNow, Zendesk, Epic, SAP, etc.)?
- If it’s multiple systems, write down which object is authoritative for each (customer, ticket, invoice, incident, PR).
- Define what your product is: system-of-record, system-of-action, or augmentation layer. If you can’t pick one, you’re drifting.

2) Own a minimal, habit-forming interface
- Identify the exact screen where a user spends time daily/weekly (triage queue, PR review, incident channel, renewal pipeline view).
- Define the artifact you create/edit (draft reply, ticket, runbook step, PR diff, proposal) and make it first-class.
- Eliminate copy/paste loops. If users must shuttle context manually, you’re a temporary tool.

3) Build governance as product, not a sales patch
- SSO/SAML and SCIM: plan it early if you sell B2B.
- Audit logs: store who requested what, what data was accessed, what tools ran, what changed, and where the output lives.
- Permission model: map to the underlying system’s permissions (don’t invent a conflicting one).
- Retention/deletion: define policies for prompts, outputs, attachments, and embeddings.

4) Constrain autonomy before you expand it
- Start with “propose + approve” flows in high-risk domains.
- Add tool calls only when you can log them, replay them, and fail safely.
- Implement rollback paths: drafts instead of sends, PRs instead of direct commits, suggested field updates instead of overwriting records.

5) Make model choice a replaceable implementation detail
- Keep a provider routing layer (OpenAI API, Azure OpenAI, Anthropic API, Bedrock, or open weights).
- Store prompts/templates and evaluation cases in version control.
- Design UX so an output can be regenerated or compared without user confusion.

6) Prove value using operational outcomes you can observe
- Define a tight success metric tied to the workflow (time-to-first-response, time-to-resolution, review cycle time, escalation rate).
- Instrument accept/reject/edit signals inside the UI.
- Treat “we used AI” as irrelevant; measure “we shipped/closed/resolved faster with fewer mistakes.”

If you fail any section, don’t compensate by adding more AI. Fix the interface, the workflow ownership, and the governance layer first.