01
Plan
Define audience, goals, and channels for upcoming campaigns.
Strategy lives in slides; tactics live in spreadsheets.
Steady
Case Study
Designing AI marketing tools at HubSpot
I led a 9-person design team to ship four AI products inside Marketing Hub. CSAT lifted from 70 to 84, and campaign setup time was cut in half over four months, across all users.
Role
Senior Design Manager
Team
9 designers across 2 product groups
Timeline
4-month rollout, multi-quarter cadence
Shipped
AI Campaigns Assistant · Content Remix · AI Copilot · Social AI Composer
Outcomes
+14 pts CSAT (70 → 84) · 2× faster campaign setup
A platform doubling down on AI
I joined HubSpot's Marketing Hub at a pivotal moment. The platform was doubling down on AI, and design leadership needed to match that ambition. Marketing Hub helps businesses attract, engage, and grow their audience through automation, CRM, and personalized content. My team owned the design experience across every tier, from Starter to Enterprise.
The work that excited me most was shaping how AI showed up for marketers day-to-day. Our AI Campaign Assistant helped customers produce high-quality, targeted content in a fraction of the time. Multi-touch revenue attribution gave them a clear picture of what was actually driving results. These weren't just features. They were the tools that made our customers feel powerful.
Two opposing pressures, one workflow
Marketing teams were stuck between shipping more campaigns, faster, and making every campaign feel personal and on-brand. AI promised to relieve both, but only if it slotted into how marketers already worked, not if it replaced their judgment with a black box.
01
Disconnected orchestration
Campaigns required coordination across email, social, ads, landing pages, and blog. The workflow lived across disconnected tools.
02
Parallel AI surfaces
AI features were arriving from multiple teams in parallel, each with its own UX patterns and trust model.
03
Cautious customers
Marketers were curious but skeptical. We needed to earn trust before asking them to delegate work.
Building the team, then the work
As Senior Design Manager, I led product and content designers across two product groups. My first priority was the team itself: establishing clear ownership, calibrating critique, and aligning on what "good" looked like for AI features specifically.
Product Group 1
Reach & Reputation
AdsSocialAIInsights
Product Group 2
Marketing Orchestration
PlatformCampaignsAI AssistantMeasurement
9
Designers managed
I drove the cross-group AI design strategy, partnered with PM and ENG leadership to sequence releases, and stayed hands-on in critique on the AI surfaces, particularly where multiple agents would surface in the same flow.
Three principles for AI in marketing tools
The team converged on a small set of principles that shaped every AI surface we shipped.
Principle 01
Visible by default, never silent
Marketers see what the agent did, why, and can edit it. No invisible mutations. Every AI action leaves a clear trace.
Principle 02
Editable by structure, not just chat
Outputs land in the same fields and states marketers already know. AI augments the canvas; it doesn't take over a separate one.
Principle 03
Trust earned, not assumed
Each AI surface had a clear handoff: preview, edit, regenerate, approve. Marketers stayed in the driver's seat.
Where marketers got stuck
Those principles came from real research. Across five campaign phases, friction concentrated in the middle, where building and executing fragmented across tools and teams.
02
Build
Brief, draft, review, and approve assets across teams.
Work fragments across email, sheets, and project tools.
Strained
03
Execute
Schedule, publish, and monitor campaigns across channels.
UTM tracking is manual; nothing lives in one place.
Frustrated
04
Measure
Pull performance data and assess what moved the needle.
Attribution means stitching together five reports.
Mixed
05
Inform
Share results with stakeholders and shape the next plan.
Lessons rarely loop back to the next campaign's brief.
Lifted
Mapping AI to where marketers felt friction
From those friction points, each AI surface mapped to a specific moment in the marketer's lifecycle. The five phases collapsed into four where AI could move the most work.
01
Strategy
Starting from a blank page each year. Aligning goals, audiences, and bets across product and brand teams.
02
Calendar
Coordinating channels, dates, and owners. The plan lived across disconnected tools and inboxes.
03
Campaign
Producing high-volume, on-brand assets across email, social, ads, landing pages, and blog. Fast.
04
Performance
Knowing which assets actually moved revenue. Closing the loop back to next year's strategy.
Where AI helped
Templates
Where AI helped
AI Campaigns Assistant
Where AI helped
AI CopilotSocial AI ComposerContent Remix
Where AI helped
Multi-touch attribution
AI agent shipped under my team
Where AI showed up in the marketer's workflow
HubSpot campaigns follow a four-stage lifecycle. We mapped each AI surface to the moment in that lifecycle where it removed the most friction.
01
Plan
Define goals, audience, and channels. Set measurable targets.
Templates
02
Build
Create assets across email, social, ads, landing pages, blog.
AI Campaigns AssistantAI CopilotSocial AI Composer
03
Distribute
Publish across channels using the marketing calendar.
Content Remix
04
Measure
Track collective and asset-level performance, attribute revenue.
Multi-touch attribution
AI agent shipped under my team
Four agents, four jobs
Each AI product targeted a different point of friction. Together they covered the marketer's content lifecycle. The seams between them are what we'd refine next.
AI Campaigns Assistant
Generates campaign briefs, target audiences, and asset drafts from a marketer's prompt. The connective tissue between strategy and execution.
Content Remix
Reformats one piece of content into channel-native versions: a long blog into a tweet thread, an email into social posts. Multiplies one idea.
AI Copilot
In-context assistance inside the editor: draft, edit, summarize, translate. The AI surface marketers reach for hundreds of times a day.
Social AI Composer
Channel-aware social post generation with platform-specific voice, length, and CTAs baked in. Removes the cognitive overhead of channel switching.
Trust earned, not assumed.
A design principle for Marketing Hub AI
From brief to live campaign
A look at the AI Campaigns Assistant in flow: plan, build, and distribute, with measurement closing the loop.
What changed for marketers
+14pts
CSAT lift
From 70 → 84 across all users, 4 months
2×
Faster setup
Campaign workflow time cut in half
4products
Shipped in window
Campaigns Assistant, Remix, Copilot, Social Composer
9designers
Team led
Across 2 product groups, 4 surfaces
The decisions worth naming
Every shipping window forces a choice. Two stand out from this one.
Trade-off 01
Speed vs. cohesion
Chose
Ship four AI products in parallel
Over
Sequencing them behind one unified system
Shipping in parallel earned customer trust by showing momentum, but each agent's UX evolved on its own track. We standardized cross-agent patterns after shipping, not before.
Trade-off 02
Generation vs. control
Chose
Scaffolding around AI output
Over
Letting the model “just do it”
Early prototypes leaned on raw generation. We deliberately added preview, edit, regenerate, and approve, even when the model could have skipped them. We chose marketer agency over magic.
What I'd give more time to
The thing I'd give more time to, in retrospect, is the seam between agents. We shipped Campaigns Assistant, Content Remix, AI Copilot, and Social AI Composer in parallel. Each was strong on its own, but the handoffs deserved more attention than the schedule allowed.
Some of this was the moment in AI: this was before LLMs could do what they can now. We were designing for narrower, more deterministic models, and “agent collaboration” meant something closer to coordinating four well-defined products than orchestrating one fluid assistant.
With current models, I'd revisit how those four surfaces compress into fewer, more capable agents, and how the marketer moves between them feeling like one continuous conversation.
