Most founders do not need an AI that makes every decision.
They need an AI that knows which decisions it must not make.
That distinction matters. If you review every summary, reminder, and internal update, automation becomes a slower way to work. If AI can send promises, change pricing, or take sensitive actions without review, speed becomes risk.
The answer is an approval queue: a clear place where AI work pauses when human judgment matters.
The approval ladder
Classify every step in an AI workflow into one of three levels.
Level 1 — Auto
Let AI complete internal, reversible work automatically.
Examples include:
• summarizing a call
• sorting enquiries by category
• extracting tasks from meeting notes
• preparing an internal brief
• reminding an owner about an overdue action
The test is simple: if the output is wrong, can your team spot and fix it before it affects a customer, commitment, or payment?
If yes, automation can usually move forward.
Level 2 — Review
Require approval for customer-facing drafts and recommendations.
AI can still do most of the preparation. It can draft a response, assemble a proposal outline, suggest a follow-up, or flag a likely next step. But a person checks the context before anything leaves the workspace.
The reviewer should answer three questions:
Is the information correct?
Does the message match our operating rules and tone?
Are we making a promise we can actually keep?
This level gives you speed without pretending that a plausible draft equals a sound decision.
Level 3 — Decide
Keep promises and consequential actions with a person.
Pricing changes, contractual commitments, refunds, sensitive customer messages, access changes, and financial actions belong here. AI can collect context and prepare options, but a named owner makes the decision.
The rule is straightforward: more consequence requires more human judgment.
Example: a lead-response workflow
Imagine a qualified lead submits an enquiry.
Auto: The system gathers the form, relevant account history, service information, and availability. It summarizes the request and flags missing details.
Review: AI prepares a useful response and recommends the next step. The founder checks fit, tone, and accuracy.
Decide: The founder approves scope, pricing, delivery promises, and any exception to the standard process.
Finished outcome: The lead receives an accurate response, the decision remains visible, and the next follow-up has an owner.
The workflow stays fast because AI handles the routine preparation. It stays trustworthy because the system pauses before consequence.
For a longer workflow example, read Cora’s client-onboarding playbook:
Read the client-onboarding playbook: https://heycora.in/guides/agency-client-onboarding-playbook/
The example comes from agency operations, but the approval ladder applies to any founder-led business that serves customers, makes commitments, or handles sensitive information.
Where Cora fits
We are building Cora around controlled workflows: AI should move work forward, preserve context, and surface the moments that need a person.
That does not mean every approval feature described here is already available inside Cora. We will show released capabilities as released, experiments as experiments, and roadmap ideas as roadmap ideas.
Explore Cora: https://heycora.in/
Do it now
Choose one workflow you want AI to help with. List its actions, then label each one:
• Auto — internal and reversible
• Review — external draft or recommendation
• Decide — promise, payment, access, or sensitive consequence
If everything needs approval, shrink the workflow until AI can safely own at least one repeatable step.
If nothing needs approval, look again for the moment where a mistake becomes a promise.
Your goal is not maximum autonomy. Your goal is reliable progress with judgment in the right place.
The proof
This issue introduces an operating framework, not a performance claim. We are not presenting the approval ladder as a released Cora feature.
Future issues will show a real product workflow, screenshot, or measured result when that evidence is available.
Reply and tell me: which decision should your AI co-founder always leave to you?
— Dravya
