AI chat / Content design
2026 / Case study draft
AI chat entry point for Ads Manager
I helped turn static mid-flight recommendations into an AI chat entry point that explained why guidance appeared, what signals supported it, and what an advertiser could do next.
Visual mockups
The entry point had to earn the click.
Recreated product visuals showing the entry point, the answer structure, and the prompt/spec thinking behind the experience.
Mid-flight guidance
Recommendation card
Prevent budget-capped delivery
Current spend
$4.8K
Delivery pace
Early cap
Confidence
Medium
Signals behind the guidance
7 day viewAI entry point
Ask why this budget change is recommended
Turns static guidance into a conversation about the signals, tradeoffs, and next step.
Response anatomy
A useful answer had to explain the recommendation.
Name the trigger
Which mid-flight signal caused the recommendation to appear.
Personalize the why
How the signal applies to this campaign, audience, and objective.
Give one next move
A realistic action, with caveats when confidence is conditional.
Prompt spec sample
Static guidance became reusable chat instructions.
Marketing science
Explain budget guidance through pacing, missed delivery opportunity, and campaign objective.
Conversation rule
Start with the observable signal. Use could when the recommendation is conditional.
Engineering hook
Requires recommendation type, campaign objective, spend pacing, active date range, and confidence state.
Overview
What changed
This case frames AI chat as a content-design system, not a novelty layer. The work connected entry-point language, response structure, prompt guidance, and quality review so static recommendations could become explainable product guidance.
Role
My part
- Defined entry-point language for mid-flight recommendation cards so advertisers knew what the AI chat could explain.
- Translated marketing science expert input into prompt guidance, example responses, and user-facing explanation patterns.
- Partnered with product design and engineering on recommendation triggers, chat context, response quality, and implementation details.
Problem
The product moment was unclear
Ads Manager could surface mid-flight guidance while a campaign was running, but the recommendation itself was static. The copy could say what to do, but it could not open up why the recommendation mattered, which signals informed it, how it applied to that advertiser's setup, or what tradeoffs to consider before acting.
Constraints
Rules of the work
- AI responses needed to personalize the explanation without overstating certainty or sounding like unsupported marketing advice.
- Marketing science guidance had to be translated into language advertisers could act on while a campaign was already in flight.
- Engineering needed prompt patterns, recommendation context, response states, and guardrails that could scale across campaign types.
Users
Who needed clarity
- Advertisers reviewing active campaigns and deciding whether to act on a recommendation.
- Marketing science experts whose guidance needed to survive translation into product and AI behavior.
- Engineers and product partners connecting recommendation data to the chat prompt, response structure, and quality checks.
Before / after
Examples in context, with the reason for each change
AI chat entry point
Entry point shown on a mid-flight recommendation card.
Before
Increase budget to improve results.
After
Ask why this budget change is recommended
Chat response
AI response explaining the recommendation in context.
Before
Increasing your budget may improve results.
After
Your campaign is reaching its daily budget before its highest-converting hours. Increasing it could keep delivery active when this audience is more likely to respond.
Content decisions
The writing system underneath
Move from recommendation to explanation
The entry point could not feel like a generic assistant pasted onto the page. It had to extend the recommendation by making the reason behind it available.
Ask why this budget change is recommended
Design responses around evidence, not authority
The chat response had to show its work: what signal the system noticed, why it mattered for this campaign, and what the advertiser could do next.
Your campaign is pacing out around 3 PM, before the hours that usually drive the most conversions.
Turn expert heuristics into promptable patterns
Marketing science expertise became reusable guidance for prompts, examples, and quality review instead of sitting in meeting notes.
When recommending a budget change, name the observed signal, explain the likely tradeoff, and offer one decision path.
Process
How I got there
- Mapped mid-flight recommendation cards by trigger, advertiser question, data context, and likely follow-up.
- Worked with marketing science experts to capture the reasoning behind budget, delivery, audience, and creative recommendations.
- Wrote conversational patterns for entry prompts, answer structure, follow-up suggestions, and transparent caveats.
- Partnered with engineers to align recommendation context, prompt inputs, response states, and quality checks.
Outcome
Impact signals
- Defined a repeatable pattern for turning static product guidance into explainable AI entry points.
- Created response standards for signal, rationale, tradeoff, caveat, and next action.
- Impact to add: advertiser comprehension, chat engagement, and recommendation follow-through.
Learnings
What I would carry forward
- The best AI entry point starts with the user's next question, not the product's feature label.
- Transparency is a writing problem and a systems problem.
- Static guidance becomes more valuable when users can inspect the reasoning before they act.
Next project