Monday AI: A Verify-Before-You-Trust Playbook

Where to find monday's AI features and what they actually do

Start with location, because this is the part most guides skip. AI Blocks live inside monday Docs. Open a doc, click into a block, and either type /ai or use the block's "+" menu to pull up the AI options: Summarize, Translate, Extract action items, Generate a table. They operate on whatever text is in that block — nothing else.

The AI Assistant is a separate chat panel, opened from the AI icon in a board's top-right toolbar. It's a natural-language interface for building formulas, drafting automation recipes, or generating board structure from a plain-English request. Automation recipe suggestions show up inside the Automations center as proposed recipes based on patterns the system notices in your board activity, and can also be requested directly through the Assistant.

Two operational questions come up before any rollout, and neither is answered cleanly in public documentation:

• Where's the admin off-switch? monday's admin console should include some form of AI enable/disable control at the account or workspace level. Not documented — the exact menu path isn't listed consistently in public help materials, so confirm it directly inside your own admin console (look under account-level settings, not board settings) before you plan a rollout around the assumption that one exists.
• Does one action burn one credit, or several? Whether generating a single summary or a single formula consumes credits per generation, per document, or pooled across the workspace isn't stated in monday's pricing materials. Not documented — get this in writing from sales before modeling usage against a seat count.

Four worked examples, with the exact input and the shape of what comes back:

1. Summarize a Doc block. Paste a meeting recap into a block, then select AI → Summarize. Output: a 3–4 bullet summary appended directly below the block.
2. Extract action items. Same doc, AI → Extract action items. Output: a table with Task / Owner / Due Date columns, auto-populated from whatever the text implies.
3. Generate a formula via the Assistant. Type "flag items overdue by more than 3 days" into the panel. Output: a Formula-column expression (something like an IF() statement referencing your due-date column) inserted and ready to accept or edit.
4. Draft an automation recipe. Type "when status changes to Done, notify the item owner." Output: a recipe card with trigger and action pre-filled, sitting in draft state until you activate it — it won't fire until you confirm.

Each example takes under two minutes to try. That's also the size of your first real test: run it once, then decide if it's worth building a verification process around.

Key takeaway — The highest-leverage move with monday's AI isn't a clever prompt. It's a short verification pass that catches subtle failures — like a summary that quietly drops a blocker — before that output reaches a client or feeds an automation.

## Using monday AI well: prompts that work, and ones that don't

The gap between a useful Assistant output and a wrong one usually comes down to how much ambiguity you left in the prompt.

Patterns that work:

• Name the exact column. "Flag items overdue by more than 3 days" forces the Assistant to guess which date field you mean if your board has both a Due Date and a Target Date column. "Flag items where 'Due Date' is more than 3 days past, output to a new Formula column called 'Overdue Flag'" gives it one column to read and one place to write.
• Specify the output format. "Summarize this update" produces a generic paragraph you have to re-read to audit. "Summarize this update as 3 bullets: what shipped, what's blocked, what's due next" produces a structure you can scan and check line by line.
• Split automations into trigger, condition, action. "When 'Due Date' has passed and 'Status' is not 'Done,' notify the person in 'Owner'" maps directly onto how the recipe builder works. Vague requests get mapped onto whatever built-in template is closest, not your actual process.

Prompts that fail, and why:

Prompt	What goes wrong
"Make this board better."	No column, no metric, no defined outcome. The Assistant returns a generic structural suggestion (add a Priority column, add a Timeline view) that ignores your actual data.
"Summarize all my updates from this week."	"This week" and "my updates" aren't board fields. Without an explicit date range and person filter, the Assistant pulls whatever's in view, not what you meant.
"Fix the formula in this column."	The Assistant can't diagnose an error it can't see. A #VALUE! from a Formula column pointed at a Text column instead of a Date column needs the actual error pasted in to be fixable.
"Automate my whole approval process."	Multi-step, multi-condition logic defaults to the closest built-in recipe template instead of your process. Break it into one trigger-action pair per prompt.

When a generated formula errors or a recipe points at the wrong column:

1. Paste the exact error back into the Assistant, naming the columns and their types — "this returns #VALUE!, 'Due Date' is a Date column, 'Status' is a Status column" — instead of re-asking the original question. The Assistant edits from a specific error far more reliably than it debugs blind.
2. Check column references manually before activating anything. A drafted recipe sits in an editable state; open the trigger and action lines and confirm each one points at the column you meant, not a same-named column it guessed from board history. If it's wrong, edit the reference in the card directly — re-prompting and hoping for a different guess wastes a cycle.

Whose AI is it, honestly

Provenance is murky. We could not locate a model card, a named subprocessor list, or an AI-specific DPA addendum on trust.monday.com or in monday's product documentation. That's a status, not a permanent verdict — trust pages get updated without an announcement. Check the link yourself and ask your rep directly before you rely on the absence.

Some competitors publish model provenance and subprocessor names on their trust pages; monday's public materials do not. Verify both yourself before assuming parity in either direction.

Why it matters: GDPR and HIPAA-adjacent residency rules, vendor risk assessments that require named subprocessors, and whether board content leaves your tenant to help train someone else's model. None of that is answerable from monday's public materials right now. Close it before you scale usage, not after.

The four features, and their failure profiles

Treat these as four separate pilots, not one:

• AI Blocks in Docs — most likely to touch sensitive client language, since docs often hold meeting notes and scope discussions verbatim.
• AI Assistant — errors are usually structural (a malformed formula, a recipe with the wrong trigger), which makes them easier to catch than prose errors.
• Automation recipe suggestions — useful for discovery, risky if accepted without reading the trigger-action logic line by line.
• Update/activity summarization — the highest-risk feature, because it compresses narrative content instead of generating structured logic. A team that only tests the formula assistant and finds it reliable has learned nothing about whether summarization drops blockers.

What a dropped blocker actually looks like

This is an illustrative example, not a measured test result — construct your own version from a live board before you rely on the pattern:

Thread input:

• Jan 14, Maria: "Vendor contract sent to legal for review."
• Jan 15, Dan: "Design assets 80% done, waiting on final logo file from client."
• Jan 16, Maria: "Blocked — legal flagged a liability clause. Need client sign-off before we can move forward with the vendor."
• Jan 17, Dan: "Logo file received. Design on track for Friday."
• Jan 18, Maria: "Still waiting on client for the liability clause. No movement."

AI summary output: "Design assets are complete and the vendor contract is in progress. Project is on track for Friday delivery."

The summary is fluent and confident. It's also wrong in the way that matters most: the actual blocker — a legal sign-off stuck for two days — never appears. "In progress" replaces "blocked," and nobody reading only the summary would know to escalate. This is the failure mode your pilot needs to catch before summarization touches anything client-facing.

The two-track playbook

Split the work. One track is documentation-first and belongs to IT or procurement. The other is workflow-first and belongs to whoever runs your PM or ops team. Run them at the same time.

Track	Owner	Goal	Timeframe
Documentation request	IT / procurement / security	Confirm model provenance, retention, subprocessors, opt-out mechanics	1–2 weeks
Low-stakes pilot	PM team lead	Test accuracy and usefulness of summaries, formulas, and automations against real data	2 sprints (2–4 weeks)
Cost verification	Finance / procurement	Get a tier-specific AI pricing quote before seat expansion	Before renewal or upsell

Track A: the documentation request

Before rolling AI out to a team touching regulated data or client PII, send a direct written request and get answers in writing — not a recap of what a salesperson said on a call.

Ask for:

1. Subprocessor list — which third parties, if any, process data sent to AI features, and where.
2. Model provenance — proprietary, licensed, or a wrapper around an external API?
3. Data retention — how long input data is kept, and where.
4. Training usage — is customer data used to train or fine-tune any model, and is there an opt-out?
5. DPA coverage — does an existing agreement name AI processing specifically?
6. Feature-by-feature scope — do the answers differ between Blocks, Assistant, automations, and summarization, or is it one blanket policy?

If sales can't produce these on request, that's information too: keep AI confined to non-sensitive boards until documentation catches up.

Track B: the low-stakes pilot

Step 1 — Pick a contained project. An internal ops tracker or a completed project you can test retroactively. Skip active client deliverables on this pass.

Step 2 — Define a test set per feature. Pull 10–15 real tasks or status threads. Run summarization against each. Separately, run 10–15 real automation requests through the Assistant. Don't mix the datasets — a clean formula result tells you nothing about summary quality.

Step 3 — Score against ground truth. Someone who knows the project rates each output on accuracy and usefulness, 1–3 scale. You're building a pattern, not a benchmark.

Step 4 — Repeat with variation. Run the same input twice, a few days apart. Inconsistent output on a stable input means the feature is less deterministic than it looks.

Step 5 — Log failure modes. Not just pass/fail — how it failed. That list becomes your standing reviewer checklist.

Build the scorecard as a board, not a spreadsheet

Skip the blank-spreadsheet step. Create a new monday board named "AI Pilot Scorecard" and add these five columns directly — Status columns for the pass/fail fields, a Text column for notes.

Column	Type	Pass signal
Factual accuracy	Status (Pass/Fail)	Zero fabricated facts across the test set
Consistency	Status (Pass/Fail)	Minor wording drift only, no factual drift
Automation logic	Status (Pass/Fail)	Zero misfires on a sandbox board
Time saved	Status (Pass/Fail)	Summary genuinely replaces a manual read
Failure notes	Text	Specific description of how it failed, not just that it did

Here's what a filled row looks like, scoring the dropped-blocker example above the same way you'd score a real pilot result:

Input	AI output	Factual accuracy	Failure notes
5-day status thread (Jan 14–18) with one explicit blocker ("blocked — legal flagged a liability clause")	"Design assets are complete and the vendor contract is in progress. Project is on track for Friday delivery."	Fail	Omitted blocker present in 3 of 5 daily updates; summary says "on track" when the thread says "blocked."

That's the granularity you're after — not "summarization needs work," but a specific, repeatable failure tied to a specific input. Duplicate the board per feature area (Blocks, Assistant, automations, summarization) so results don't blend together, and keep the original as your rubric version so a re-test after a model update compares against the same bar.

Matching AI usage to board risk

Board type	Recommended AI posture	Reasoning
Internal ops / process tracker	Automation logic auto-approved after a sandbox test; summaries still spot-checked weekly	Low harm from an occasional error, but the summarization failure above means even low-stakes boards need a light recurring check
Completed or archived project (retroactive test only)	Full testing allowed, no production use	Zero live-decision risk; ideal for building your failure-mode log
Active client deliverable board	Human sign-off required on any AI summary before it's shared externally	A dropped blocker or wrong date reaches a client, not just your team
Sales CRM / pipeline board	Restrict summarization until Track A's data questions are answered	Pipeline data often includes PII and deal terms covered by vendor risk reviews
Finance or budget tracker	Hold AI features off entirely until the DPA addendum is confirmed	Highest regulatory exposure, lowest tolerance for hallucinated figures

Data questions to close before scaling past the pilot

• Where does board data go when AI features process it? Not documented publicly — confirm per Track A.
• Can individual boards or workspaces opt out of AI processing? Confirm the toggle's exact location, not a verbal "yes, there's a setting."
• Does enabling AI change your existing DPA, or does it need an addendum?
• Does the answer differ by feature — is the subprocessor for Doc summarization the same one handling automation-recipe generation?

An opt-out that "exists" and an opt-out confirmed in writing are different facts. Only the second should change your rollout decision.

Pricing: three questions to ask before you expand seats for AI

monday's public pricing page doesn't publish per-tier AI credit figures, so don't plan a rollout around a number you haven't confirmed in writing. Ask your rep three questions instead:

1. How many credits does my current tier include, and what's the overage cost? Get the number, not a range.
2. Is usage metered per generation, per document, or pooled across the workspace? This determines whether ten people summarizing daily behaves differently than one person running batch jobs.
3. Does AI access require an upsell to a higher tier, or is it bundled into what we already pay for? Confirm this before renewal conversations, not during them.

Treat any answer from a sales call as provisional until it appears on a signed order form — the same standard as Track A, applied to cost instead of data.

Power moves once the pilot clears

• Tier your trust by data sensitivity using the board-risk table above, adjusted by your own failure-mode log.
• Keep the failure-mode list alive. What worked on status updates might fall apart on a nuanced scope-change thread.
• Re-run the consistency test quarterly. Vendors update models without always announcing it.
• Assign one owner for the documentation relationship — someone re-requests the subprocessor list and DPA terms as they change, not as a one-time ask.
• Version your rubric. The duplicated scorecard board from earlier makes this a copy action, not a rebuild.

Red flags that should pause a rollout

• Sales can't name which model or vendor powers the feature, even loosely.
• No written answer on retention timelines.
• The opt-out toggle, if it exists, isn't documented anywhere you can point to later.
• A summary confidently states facts absent from the source thread — like the missing blocker above.
• An automation recipe fires on a different condition than the one described, and support can't explain why.

Known vs. unknown, at a glance

Category	Status
Underlying model / provenance	Not located publicly — confirm at trust.monday.com
Trains on customer data	Not disclosed — confirm directly
Data retention period	Not disclosed — confirm directly
Subprocessor list	Not located
Opt-out mechanism	Not documented publicly
Admin AI enable/disable toggle location	Not documented — confirm in your own admin console
Credit consumption unit (per generation vs. per document)	Not documented — confirm with sales
AI feature pricing structure	Not published — requires a direct sales quote
Feature set (Blocks, Assistant, automations, summaries)	Documented and testable today

Winning with monday's AI right now looks less like a clever prompt and more like a paper trail — a documented answer for every row in that table before it touches a client deliverable.

The bottom line

monday's AI feature set is real and testable in minutes; its trust documentation isn't there yet in public materials. Run the pilot, get the data-handling and credit answers in writing, and let that evidence — not a demo — decide how far you scale.