Example of First Test Ad Formats from OpenAI Revealed

On 16 January 2026, OpenAI published a short post on X showing what it described as an example of the first ad formats it plans to test. The post itself is brief, but the implications aren’t—especially if you work in marketing, sales, or revenue operations and you rely on automation to keep campaigns tidy.

I’ve shipped enough campaigns to know that “new ad format” rarely stays a purely creative topic. It quickly becomes an operational one: tracking, attribution, approvals, brand safety, reporting, and—if you’re like us at Marketing-Ekspercki—automations in tools such as make.com and n8n. If you want to be ready for a new inventory type, you don’t start by designing banners. You start by designing your process.

This article breaks down what OpenAI’s announcement does (and doesn’t) tell us, what it could mean for advertisers, and how you can prepare your marketing and sales systems—without making up details that haven’t been confirmed.

What OpenAI Actually Shared (and Why It Matters)

OpenAI’s post states: “Here’s an example of what the first ad formats we plan to test could look like,” and includes an image.

That’s the full extent of the verified information in the source text. We don’t get a rollout date, targeting specs, auction mechanics, measurement options, creative rules, or partner requirements. So I won’t pretend we do.

Still, the message matters for one simple reason: it signals intent. When a platform publicly shows a potential ad unit—even at “example” level—it usually means internal prototypes exist, policy work is ongoing, and feedback loops have started.

What you can safely infer (without guessing specifics)

Testing will likely be limited at first (that’s what “plan to test” typically implies).
Formats are being productised, meaning this isn’t a one-off sponsorship concept.
Advertisers will ask for measurement and controls, so platform governance becomes part of the conversation.

From the marketer’s side, the most practical takeaway is this: you can start preparing your workflows now, even if you can’t book inventory yet.

The Strategic Context: Why Ads in AI Products Feel Different

I’ve worked with ads across search, social, and content networks, and AI surfaces add an extra layer of sensitivity. In a chatbot-style environment, the user experience is closer to a conversation than a feed. That changes what people find acceptable, and it raises the bar for tone, placement, and disclosure.

User trust becomes the limiting factor

In many ad channels, you can get away with a little noise. In a conversational interface, noise feels like interruption. If a user asks for help and receives a promotional message that feels sneaky, they won’t just ignore it—they’ll lose confidence.

So, as an advertiser, you’ll want to treat this as trust-first inventory. The best-performing brands will likely be the ones that:

Offer clear value tied to the user’s task
Keep claims conservative and verifiable
Use a tone that fits the context (helpful, not shouty)
Respect disclosure and labelling

It’s closer to “assisted decisioning” than interruption marketing

Even if the ad unit looks familiar at a glance, the psychology isn’t. In AI-assisted environments, users often arrive with a specific job-to-be-done. That makes relevance more important than flashiness.

From experience, when the user is already solving something, you don’t need fireworks. You need a calm, competent offer that doesn’t waste their time.

How This Could Affect Marketers and Revenue Teams

Whenever a new ad surface emerges, marketing teams tend to focus on creative first. I get it—creative is visible. Yet in practice, the winners tend to be the teams that operationalise faster: they ship compliant assets, track results cleanly, and can iterate without internal chaos.

What might change for your campaign operations

New creative specifications and approval rules
New tracking patterns (UTMs, click IDs, server-side events)
New brand-safety checks tailored to AI contexts
New reporting needs that don’t map 1:1 to existing dashboards

If you run lean, you’ll feel this immediately: one “small test” can create a dozen side tasks. That’s exactly where automation pays for itself.

What to Do Now: A Practical Readiness Plan (No Platform Access Required)

You can’t control when a platform opens ads to you. You can control whether you’re ready to run a proper test the moment you get access. Here’s a plan we use with clients when an emerging channel appears.

1) Define what “success” means before you see the first click

I always start here because it prevents vanity metrics from hijacking your test. Decide what you’ll call a win. Examples:

Lead quality: SQL rate, meeting show rate, pipeline created
Efficiency: cost per qualified lead, cost per meeting
Downstream impact: conversion to paid, retention signals

Then document it in one page. If you can’t write it simply, your team won’t execute it consistently.

2) Build a clean campaign taxonomy (so reporting doesn’t become a guessing game)

Before you launch, set naming conventions that your tools can parse. I like to keep it boring and machine-friendly.

Channel (e.g., “ai_test”)
Objective (leadgen, demo, trial)
Audience (segment or ICP label)
Creative theme (pain-point, feature, proof)
Date (yyyy-mm)

This makes it far easier to automate: tagging, routing, dashboards, and post-test analysis.

3) Prepare your tracking stack for ambiguity

Early ad products often change. Parameters get renamed, reporting fields appear and disappear, and attribution can be “rough around the edges”. So I plan for defensive tracking.

Standardise UTM structures and validate them automatically
Use server-side events where possible for core actions
Create a single source of truth for campaigns (sheet, database, or CRM object)

When the platform finally stabilises, you can refine. Until then, keep evidence in your own systems.

Automation Playbook: What We’d Build in make.com and n8n

At Marketing-Ekspercki, we spend a lot of time making sure marketing tests don’t collapse under their own admin. When a new channel arrives, we typically set up a small automation layer that gives you:

Consistency (naming, tagging, routing)
Speed (fewer manual steps)
Safety (approval flows, guardrails)
Visibility (dashboards and alerts)

Below are automation patterns you can prepare now. They don’t depend on any specific OpenAI ads API. They depend on your internal workflow, which you already control.

Workflow A: Creative intake → approval → asset library

When ads are new, stakeholders get nervous. Legal wants to review claims, brand wants consistent tone, and sales wants alignment. That’s normal. The trick is to avoid email ping-pong.

Example flow (make.com or n8n):

Trigger: new creative request submitted via form (Typeform/Tally/HubSpot form)
Automation: generate a creative brief document (Google Docs/Notion)
Automation: route for approvals (Slack + status fields)
Automation: once approved, store final assets and metadata (Drive + Airtable)
Automation: notify owner and log version history

I’ve seen teams save hours per week just by forcing creative requests through a structured form with validation. It feels strict at first, then everyone breathes easier.

Workflow B: UTM builder + link QA (the unglamorous hero)

If you’ve ever tried to analyse a test and discovered five different spellings of the same campaign name, you know the pain.

Trigger: new campaign created in your “campaign registry” table
Automation: generate tracking URLs with locked UTM values
Automation: validate destination URL (status code, redirects, presence of pixel)
Automation: write final URL back to the registry and message the media buyer

Small detail, big payoff. When leadership asks “did it work?”, you won’t need a week of detective work.

Workflow C: Lead routing that respects speed-to-lead

If a new channel performs, your lead volume can spike. If sales follows up slowly, you’ll think the channel is bad, when in fact your response time is.

Trigger: new lead in CRM
Automation: enrich (Clearbit-style providers if you use them, or internal data)
Automation: score and route (territory, segment, intent)
Automation: notify SDR in Slack, create task, start cadence sequence
Automation: if no action in X minutes/hours, escalate

I like to bake in a “polite nag”. It’s not glamorous, but it moves revenue.

Workflow D: Testing journal (so you don’t repeat old mistakes)

Teams often run tests, then forget why they made decisions. Six months later, they repeat the same experiment and call it “new”. I’ve done it myself, and it’s mildly embarrassing.

Trigger: campaign status changes to “launched”
Automation: create a testing log entry (Notion/Confluence)
Automation: capture hypothesis, audience, creative variants, budget, dates
Automation: schedule reminders to record learnings at day 7 / day 14

This gives you institutional memory, even when people move roles.

Creative Guidance: How to Approach an AI-Adjacent Ad Format

We don’t have official creative rules from the source. Still, you can prepare principles that tend to hold in high-trust environments.

Keep the promise plain and measurable

Overstated claims tend to backfire—fast. I recommend writing offers so that a sceptical reader can verify them without gymnastics.

Good: “Book a 15-minute setup call”
Good: “Get the template we use for lead routing in n8n”
Risky: vague claims that imply guaranteed results

Match the user’s pace

In conversational contexts, people often want quick, workable steps. So your creative should feel like help, not a billboard.

Use short sentences
Prefer specific nouns over hype
Avoid shouting punctuation and edgy gimmicks

As my old boss used to say, “Don’t make me think.” It’s still good advice.

Offer a sensible next step

If the ad appears close to a moment of decision, your call to action should respect that moment.

Low friction: checklist, calculator, template, short consult
Medium friction: webinar, comparison guide, case story
High friction: “Talk to sales” (works when intent is high)

Measurement: What You’ll Want to Track from Day One

Because this is described as a test, reporting may be limited early on. You can still build measurement discipline on your end.

Baseline metrics (marketing)

Clicks and landing page views (with bot filtering if you can)
Conversion rate per landing page variant
Cost per lead and cost per qualified lead

Down-funnel metrics (sales)

Speed-to-lead (minutes, not days)
Meeting set rate and meeting held rate
Pipeline created and win rate by source

Operational metrics (the ones people forget)

Time to launch a new variant (idea → live)
Approval cycle time
Data completeness (UTMs present, campaign ID present, owner assigned)

When you can cut time-to-launch in half, you effectively double your learning rate. That’s where small teams punch above their weight.

Risks and Guardrails You Should Put in Place

New ad surfaces tend to create new failure modes. A bit of planning helps you avoid drama.

Brand safety and context control

You’ll want clarity on where your ad can appear and how it’s labelled. Until the platform provides that clarity, you can still set internal guardrails:

Approve only conservative claims
Block sensitive topics in your own creative themes
Maintain a “do not run” list for offers that create compliance risk

Data privacy and consent

If you collect leads, keep your consent language tidy and consistent. Route all leads through systems that respect your retention policies.

I’m careful here because “move fast” is fun until you’re cleaning up a compliance mess on a Friday at 6 pm.

Internal expectation management

When leadership hears “new OpenAI ads”, expectations can inflate overnight. So set a test frame:

Define a fixed test budget
Run a limited set of hypotheses
Commit to a review date and a decision (scale, iterate, stop)

How We’d Run the First 30 Days of Testing (A Realistic Cadence)

If you get access to a new ad product, a calm cadence helps. Here’s a schedule I like because it mixes discipline with enough flexibility to learn.

Days 1–3: Setup and first launch

Finalise tracking and campaign registry
Launch 2–3 creatives max (don’t over-fragment)
Confirm lead routing and follow-up times

Days 4–10: Early signal check

Pause obvious underperformers (if volume supports it)
Fix landing page friction
Interview sales: lead quality notes, objections, fit

Days 11–20: Iteration loop

Swap one variable at a time (offer, audience, message)
Keep one “control” creative running
Log learnings in your testing journal

Days 21–30: Decision and next steps

Review down-funnel metrics (not just top-of-funnel)
Decide: scale, continue testing, or stop
Document what you’ll repeat next time

This avoids the classic trap where a team runs ten variants, learns nothing, and concludes the channel is “mysterious”. Usually it isn’t. Usually the test design was messy.

SEO Notes: How to Capture Demand Around “OpenAI Ad Formats” Without Thin Content

If you’re publishing content around OpenAI ad formats right now, you’ll compete with fast, short news posts. You can still win organic traffic if you provide what those posts often skip: operational guidance.

Suggested keyword themes (use naturally)

OpenAI ad formats
OpenAI ads test
advertising in AI chat products
AI ad inventory marketing strategy
make.com marketing automation and n8n marketing automation

Content angle that tends to rank

Explain what’s confirmed vs unknown
Provide a readiness checklist
Offer automation templates and workflows
Include measurement and governance guidance

That approach earns links, keeps readers on-page, and gives you a reason to exist beyond repeating a tweet.

Readiness Checklist You Can Implement This Week

If you want a short, practical list to hand to your team, use this.

Create a campaign registry (sheet or Airtable) with strict naming rules
Automate UTM creation and URL QA in make.com or n8n
Set up lead routing SLAs and escalation alerts
Build an approval workflow for creative and claims
Define success metrics tied to pipeline, not vibes
Start a testing journal and schedule learning reviews

I’d rather you do six boring things well than build a fancy dashboard that reports nonsense. Boring done properly prints money. Not always quickly, but reliably.

Where Marketing-Ekspercki Fits In (If You Want Help)

We help teams connect marketing, sales, and operations through automation—often with make.com and n8n—so you can test channels without drowning in manual work. If you plan to experiment with new ad formats as they appear, we can:

Design and implement lead routing and follow-up automations
Set up a campaign registry with tracking governance
Build reporting pipelines that connect spend to pipeline
Create approval flows that keep brand and legal aligned

If you tell me what CRM you use and where you track campaigns today (spreadsheet, Airtable, HubSpot, Salesforce, or something else), I can propose a sensible automation blueprint that won’t turn into a spaghetti monster.

Source

OpenAI post on X (16 January 2026): “Here’s an example of what the first ad formats we plan to test could look like.”

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