> ## Documentation Index > Fetch the complete documentation index at: https://docs.orbit.devotel.io/llms.txt > Use this file to discover all available pages before exploring further. # Automation opportunities: turning conversation P&L into agentable work > How the Automation opportunities surface reads your conversation cost picture and ranks the contact reasons most worth handing to agents — what flips a family into an opportunity, what a recommendation row carries, and how a scaffold click starts agent or flow authoring without anything launching itself. # Automation opportunities The **Automation opportunities** surface (dashboard → Agents → Automation opportunities) answers the question the [conversation-intelligence](/concepts/conversation-intelligence) scores leave open: once every inbound conversation is classified and priced, **which contact reason should you hand to an agent next** — and what would that save per month? This page is the concept model behind that recommendation feed: what qualifies a conversation family as agentable, what a recommendation carries, and why nothing ever launches itself. ## The premise The [conversation-intelligence](/concepts/conversation-intelligence) pass classifies each inbound conversation into a contact reason ("refund", "account access", "delivery status") and the [insights rollup family](/concepts/insights-rollup-model) turns those classes into economics: volume, handle time, human-handling cost. That is the conversation P\&L. Automation opportunities is the read-time layer that takes that P\&L and flags the families where **humans still do work an agent could absorb** — like the rest of the insights family, it is a read-time rollup over conversation records you already have, not a new pipeline. ## Interaction-level signals vs family-level verdict Two levels of decision are at play. * **Interaction level** — each conversation contributes per-interaction signals: its channel, its classified topic, whether it was resolved, how long resolution took, and — critically — whether it resolved **without a human assignee** (that counts as already deflected) or with one (still human-handled). A conversation still open counts as unknown. * **Family level** — those interactions aggregate into a *topic family* verdict: the family's current-window volume, its measured self-service deflection rate, its average handle time, and its trend versus the prior equal window. A family flips into an **opportunity** only when two thresholds hold at once: its current-window volume clears the `min_volume` floor (so long-tail noise never surfaces), and its deflection headroom — target rate minus today's measured rate — is positive. A family already deflecting at or above your target is deliberately excluded: the surface answers "what to automate next", not "what is already automated". An unknown deflection rate counts as zero headroom consumed, because "we never measured deflection here" is itself a strong automate-me signal. ## The recommendation payload Each row in the ranked list is one topic family, and carries: * the family label and its volume, trend, and busiest channel; * its measured deflection rate and average handle time; * the **automatable volume** — the incremental contacts deflection headroom implies; * a **30-day projection** — monthly deflections and monthly savings, priced from your human-handling cost minus the automated per-contact cost; * a relative **opportunity score** (0–100) that ranks the set; the biggest win scores 100, and ranking falls back to automatable volume when no ROI prices out yet; * a **scaffold suggestion** — the Orbit primitive best suited to deflect the family: a self-serve flow for structured transactional reasons (refunds, cancellations, account access), a knowledge-base article for purely informational ones (delivery status, product support), and an AI agent for conversational, judgement-heavy ones. Money is priced from **your own assumptions** — labour cost per agent-hour, automated per-contact cost, target deflection rate, fallback handle time — which you can read and update on the surface's config endpoints. Until you price them, defaults kick in and the ranking falls back to raw volume. ## From offer to agent Clicking a scaffold does not launch anything — it creates a **draft**. For an `ai_agent` scaffold the click deep-links you into the draft agent's page; for a `flow` scaffold it opens the flow builder with the draft loaded; a knowledge-article scaffold is presented as guidance rather than a draft. From there the normal authoring loop applies — ground the agent, review it, and activate it yourself, or assemble several scaffolds into a [squad](/agents/squads); the [creating-agents](/agents/creating-agents) walkthrough covers that loop. The hand-off contract is exactly this: the surface offers a pre-titled draft targeting the family's busiest channel; the decision to activate stays with you. ## Worked example Suppose your 30-day P\&L shows a `cancellation` family with 400 contacts, a measured deflection rate of 10%, and a 6-minute average handle time. With a target deflection rate of 60%, headroom is 50 points, so roughly 200 of those contacts are automatable this window — about 200 deflections a month. Priced at a 240-second handle time against your labour rate, minus a few cents of automation cost per deflection, the family projects a four-figure monthly saving and lands at the top of the list with a self-serve flow scaffold. A sibling family with the same volume but a measured deflection rate already above target does not appear at all — there is nothing left to win there. ## Tenant-owned decision The surface is strictly advisory. It never creates an active agent, never routes traffic, and never changes a write path — it reads conversation records and your own ROI assumptions, and it only ever produces drafts when you click. Tuning the assumptions, ignoring a recommendation, or deleting a scaffolded draft are all tenant-owned choices; the flagged options wait for you. Projected savings are estimates computed from your own cost assumptions, not a billing commitment. Treat the ranking as a triage aid and validate a scaffolded draft on real traffic before activating it. ## Related reading * [Conversation intelligence](/concepts/conversation-intelligence) — the per-conversation scores this surface aggregates. * [The insights rollup model](/concepts/insights-rollup-model) — the read-time-rollup design the whole insights family shares. * [QA leaderboard and evaluations](/concepts/qa-leaderboard-and-evaluations) — review loop for the agents you do activate. * [Creating agents](/agents/creating-agents) — the authoring loop a scaffold starts. * [Squads](/agents/squads) — assembling several agents into one squad.