Message house
Privacy as a practical outcome
- Control: confidential work stays in the business workflow.
- Confidence: AI assistance without a vague data-handling story.
- Continuity: a familiar device ecosystem for daily work.
Independent PMM case study · GTM strategy
Apple On-Device AI for SMBs
A product marketing strategy for positioning Apple Intelligence as a privacy-led advantage for small businesses handling confidential work.
~36MU.S. SMB opportunity
3message pillars
1north-star workflow metric
Context & role
Context: Cloud-first AI tools are growing fast, but privacy-sensitive businesses need a clearer answer to where their confidential work goes. My role: Independent product marketer responsible for the segmentation, positioning, GTM approach, and measurement framework.
The problem / decision
Could on-device processing create a credible reason for privacy-sensitive SMBs to choose Apple Intelligence over cloud-first AI alternatives - and where should the launch begin?
Method & artifacts
GTM choices
Beachhead: small law firms
Sized the SMB opportunity, selected solo and small law firms as the initial segment, then built competitive battlecards against ChatGPT, Claude, Gemini, Copilot, and local LLMs.
Recommendation & outcome
Lead with privacy as an operational benefit, not an abstract feature. The launch story should demonstrate how sensitive workflows remain under a business's control, then back it with a simple proof-based onboarding path.
What I would measure next
Track acquisition, activation, and expansion - with weekly active confidential workflows per business as the north-star metric. I would also test message comprehension, setup completion, and retained weekly use by segment.
PositioningSegmentationGTM strategyCompetitive intelligenceLifecycle metrics