A strategic conversation guide for Mohammad Shah — Marketing Analytics
"I solve the attribution problem using AI — without asking for new tools or DevOps resources."
This positions you as a problem-solver, not a tool-requester. You're removing friction, not adding it.
Appspace is excited about AI but allergic to new tool integrations. Your path to becoming "the AI guy" is:
Your pain point is perfect. Attribution is the white whale of marketing analytics. Everyone wants it, nobody has it, and it's genuinely hard. If you crack this — even partially — you become indispensable.
You don't need Salesforce API access. You need export access. Most orgs let analysts export data — they just block tool integrations. Work within the system.
Use this when first proposing the AI attribution project.
"I've been thinking about our attribution problem. We spend [X hours/week] manually trying to connect Salesforce to GA4 to our ad platforms, and we still can't confidently tell the CMO which campaigns actually drove pipeline.
I want to run an experiment. Using just Gemini and our existing data exports — no new tools, no DevOps involvement — I think I can build a working attribution model in 2-3 weeks.
If it works, we finally have real answers. If it doesn't, I've only used free tools on my own time. Zero downside."
"I know — and those attempts required tool integrations that DevOps blocked. This is different. I'm not asking to connect anything to Salesforce. I'm taking data we already export and using AI to do the matching and modeling that used to require a data engineer."
"Good question. I'll anonymize everything first — hash email addresses, use company IDs instead of names. The AI only sees patterns, not PII. I can show you the sanitization process before I start."
After you have a working prototype (even rough).
"I built something I want to show you. For the first time, we can see which campaigns actually influenced closed-won deals — across every touchpoint, every platform.
[Show the dashboard/report]
This took me 3 weeks using only Gemini and our existing data exports. No new tools. No DevOps involvement. No budget."
"I'd like to expand this. Right now it's manual — I run it weekly. With a few hours a week dedicated to this, I could automate the pipeline and add predictive modeling: 'Based on current pipeline, here's where we should shift budget next quarter.'
Would you support me making this an official part of my role?"
Once word spreads that you "do AI stuff."
"Happy to help. Let me understand the problem first — what's the specific pain point, and what does 'solved' look like for you?
[Listen]
Got it. Let me think about whether AI is even the right tool here. Sometimes it is, sometimes a simple automation is better. I'll come back with a recommendation."
You're not the "AI hammer looking for nails" guy. You're the "I solve problems, sometimes with AI" guy. This builds trust and makes you the go-to for anything complex.
Track and document everything. These are your receipts.
| Metric | Why It Matters |
|---|---|
| Hours saved per week | Translate to $ using your loaded cost (salary × 1.3) |
| Report turnaround time | "Attribution report went from 2 days to 2 hours" |
| Budget reallocation decisions influenced | Direct CMO impact — "We shifted $50K based on this insight" |
| Accuracy improvement | "Model predicted Q4 pipeline within 8% vs. 25% with old method" |
| Teams/people asking for help | Social proof of internal demand |
❌ "I want to use AI to improve our analytics"
✅ "I want to solve the attribution problem — AI is how I'll do it"
❌ "We need Claude/GPT-4 to do this right"
✅ "I've maxed out what Gemini can do — here are the results — here's what we could do with better tools"
❌ "AI can replace manual reporting"
✅ "AI handles the grunt work so we can focus on strategy"
❌ "I'll have full attribution in 2 weeks"
✅ "I'll have a working prototype that shows whether this approach works"
In 6-12 months, if you execute this well:
"The best way to get permission is to not need it. Build something that works, then show them."