I’ve been trying to use AI tools to speed up creator discovery and campaign optimization across US and LATAM, but I’m running into a consistent problem: the insights that work great in one market don’t apply to the other, and I’m not sure if I’m using the tools wrong or if they’re just built for a US-centric workflow.
Like, AI-powered creator scoring tools rank creators mostly on follower counts, engagement metrics, and audience demographics. That works fine for the US market where the data is clean and the patterns are established. But LATAM creator data is messier, regional variations are huge (Mexico vs. Brazil vs. Argentina are completely different), and the scoring doesn’t feel like it’s capturing authenticity or local trust the same way.
I started experimenting with using AI differently. Instead of relying on it to rank creators, I’m using it to surface creator patterns I might have missed—like identifying which audience segments respond to which creator archetypes, or finding clusters of creators who have cross-market appeal. But that requires a lot of manual validation, which defeats the purpose of “scaling” with AI.
The bigger challenge: campaign optimization. AI can help identify which creative angles are likely to resonate, but it’s making recommendations based on patterns from previous campaigns, which might not apply across markets. A hook that works on US Instagram might completely miss on Brazilian TikTok.
I’m wondering if the issue is that I just don’t have enough data yet, or if AI tools genuinely need different training for cross-market work. And more practically: how are you actually using AI to scale creator partnerships without losing the local market insight?
Are you using any specific tools, or are you building custom approaches? And what’s the balance between letting AI optimize and keeping human judgment in the loop?
AI is only as good as the data you feed it and the objective you define. Most off-the-shelf tools are built on US-dominant datasets, which is why they underperform in LATAM.
Here’s what works: Use AI for pattern detection, not decision-making. You need the human layer for filtering and validation.
Our process:
- Use AI to generate hypotheses: Identify creator micro-segments, predict content performance, suggest optimal posting times by market.
- Validate manually: Do these segments actually make sense? Do our top creators fall into these AI categories? Do the posting time predictions match what we observe?
- Test: Run A/B tests to confirm AI recommendations before scaling.
For LATAM specifically, we feed AI with market-segmented data. Don’t train one model on US + LATAM mixed together. Train separate models and compare results.
Concrete example: We used AI to analyze sentiment and engagement patterns across 500+ LATAM creators. The algorithm identified that creators with high comment-response rates (who actively replied to audience) had 3x better brand lift than creators with high engagement numbers alone. That’s a pattern humans might miss but AI can quantify.
On campaign optimization, we use AI to A/B test creative variations quickly (headlines, hooks, visuals), then roll out winners. But we always validate manually first—does the winner actually align with what we know about regional preferences?
The honest take: AI at scale requires human validation. Without it, you’re just using AI to scale mistakes. Build in the human loop.
We’ve built a hybrid approach that’s working at scale. Here’s the framework:
AI handles the high-volume, low-decision-threshold work:
- Screening thousands of creators against engagement benchmarks
- Suggesting optimal posting times and formats
- Forecasting campaign performance based on historical data
Humans handle the strategic, high-impact decisions:
- Evaluating creator fit and authenticity
- Making regional adjustments to AI recommendations
- Validating that predictions match market realities
For cross-market scaling specifically:
We trained separate AI models for US and LATAM. Not because we’re being purist, but because market dynamics are genuinely different. US model weights conversion signals heavily. LATAM model weights engagement depth and sentiment signals.
Then we found the overlap. Creators who performed well in both models are our cross-market candidates. Creators who excel in one model but not the other stay market-specific.
On campaign optimization: We use AI to generate 5-10 content variations (different hooks, angles, visuals). We test these with creators to see which feels most authentic to their voice. The creator’s feedback is crucial—they know their audience better than the algorithm.
The scaling question: Yes, AI speeds things up massively once you’ve done the upfront work of defining market-specific parameters and validating a few cycles. But there’s a setup cost. You need solid historical data (50+ campaigns) to train effectively. If you’re starting fresh in a market, humans have to do more work initially.
Biggest learning: Don’t treat AI as a replacement for judgment. Treat it as a multiplier for good judgment. It can help a mediocre operator run more campaigns, but it can’t make a bad strategy work.
I’m honestly skeptical of AI for the relationship side of creator partnerships, which is what I focus on. AI can help you find creators faster, but it can’t tell you if they’re trustworthy partners.
That said, I’ve seen AI work really well for surfacing opportunities. Tools that analyze creator networks—like identifying creators who collaborate frequently or have built strong communities—those are useful. It’s the pattern detection piece.
Where I still insist on human work: validating authenticity. No algorithm can tell you if a creator is genuinely interested in your brand or just chasing money. You have to look at their past work, talk to people who’ve worked with them, see if they’re consistent.
For scaling partnerships across markets, the AI that helps? Tools that track which creators have successfully worked cross-market before. That’s a useful signal. But the decision to work with someone is always relational.
Frankly, from a partnership-building perspective, I’d rather have fewer, higher-quality relationships that I’ve vetted carefully than more relationships sourced by an algorithm. Quality of partnerships matters more than volume.
We use AI for discovery and early screening, but the validation is entirely people-driven.
Our workflow:
- AI generates creator shortlist: Filters by audience demographic, engagement metrics, content category fit
- Team reviews: Quick manual assessment—does this person look like a real fit?
- Data analysis: Pull historical data if they’ve worked with similar brands
- Outreach: Personal, relationship-focused
- Test campaign: Small initial project before bigger investment
- Performance tracking: Feed results back into the AI model to improve future recommendations
For LATAM scaling, we noticed that US-trained models were missing local context entirely. So we built LATAM-specific parameters: regional platform preferences, local creator networks, category-specific engagement norms.
Once that layer was added, the AI became useful. It started recommending creators we probably would have missed otherwise.
On campaign optimization: We use AI for creative testing at volume. AI generates variations, we test with creators, roll out winners. This actually works well and saves tons of time.
The cross-market piece: We don’t try to optimize for both markets simultaneously. We optimize for each market separately, then look for overlap in what worked. That’s where we find the universally strong creative concepts.
Real talk: AI is a tool for people who already know what good looks like. If you’re new to a market, AI will amplify your mistakes. If you have domain knowledge, AI multiplies your output. Choose your AI partner accordingly.
We’ve been experimenting with AI for cross-market creator selection, and we’re seeing both promise and pitfalls.
What’s working: Using AI to identify engagement patterns we don’t see manually. Like, AI flagged that creators with high repost/share rates (not just comments) were better predictors of actual sales for our product than creators with high comment rates. That’s a useful insight we acted on.
What’s not working: AI trying to optimize for both markets simultaneously. We had to split the analysis—run separate models, compare results, then find the overlap manually.
For scaling, we’ve added a validation step: One person on our team (who knows LATAM markets well) reviews and validates all AI recommendations before we reach out. It’s not fully automated, but it’s faster than full manual discovery and it catches AI’s mistakes.
Biggest question we’re wrestling with: Do we trust AI to optimize campaign creative for both markets, or do we optimize separately? Right now, we’re optimizing separately because market dynamics are too different. That defeats some of the scaling advantage.
I think the real answer is hybrid: Use AI for high-volume, low-decision work (screening, pattern detection). Use humans for strategy and validation. The scaling comes from the humans making better decisions faster, not from replacing humans with AI.