We’ve been testing AI-driven budget allocation for a few weeks now, and I’m genuinely uncertain whether we should be leaning into it or pulling back.
Here’s the situation: we’re running a campaign with the same product across Russian and US markets. Our AI system is recommending a 70/30 budget split favoring the US market, with specific creator tier recommendations for each region. The reasoning is sound—higher predicted ROI in US, better audience overlap with our customer base, existing creator relationships there.
But I have this nagging feeling. The US market is more saturated with influencer campaigns, and our AI might be optimizing for short-term ROI metrics while missing longer-term brand building opportunities in the Russian market where we’re newer. Also, what if the model’s training data is just biased toward US performance because more campaigns have been run there historically?
Manual override would mean our strategists spend weeks building alternative scenarios, but at least we’d catch regional nuances our model might miss—things like seasonal trends in Russian markets, specific cultural moments, creator availability fluctuations.
I’ve read that some teams use AI recommendations as a starting point but adjust based on strategic goals. Others commit fully to the AI and treat human input as exceptions only. Both approaches have burned people I know.
How much are you actually relying on AI budget distribution across different markets, and at what point do you decide to overrule it based on strategy or gut feeling?
Here’s my honest take: never let AI own budget allocation alone, especially across markets with different dynamics. What you should do instead is use AI to recommend allocation based on predicted performance, then layer in strategic context that the model can’t see.
We start with AI predictions, but we always adjust for what I call ‘strategic intent’—things like market penetration goals, brand positioning, long-term partner relationships, and seasonal factors. The 70/30 split your model recommends might be mathematically optimal for next quarter’s ROI, but if your strategic goal is to build authority in the Russian market this year, that math changes significantly.
What’s worked well for us is a ‘confidence-based override’ approach: if the AI’s confidence level on a recommendation is above 85%, we treat it seriously but still review it. Below that, we’re faster to override based on strategy. This middle ground respects the model’s insights while keeping humans in control of decisions that affect long-term business positioning.
Also—and this is critical—audit the AI model’s training data. If it was trained heavily on US campaigns (which is likely), it’s inherently biased toward US-market dynamics. Ask your analytics team specifically: what’s the geographic breakdown of training data? Is Russian market data proportionally represented? That matters enormously for cross-market recommendations.
I’d push back slightly on pure AI allocation, but for a different reason: creator relationships matter. We’ve built trust with specific Russian creators over time, and they’ve delivered consistently. If the AI recommends rotating those relationships out for marginal ROI gains, we don’t do it. We know those creators will go the extra mile for us, negotiate fair terms, and maintain quality.
What we do is use AI to optimize within our committed creator roster. So we lock in relationships with key creators across both markets, then let AI optimize budget and content direction among them. That’s the hybrid approach.
And honestly? Manual oversight isn’t just a safety net for us—it’s a feature. Our strategists catch contextual stuff algorithms don’t: competing campaigns in market, creator conflicts, timing issues. That human layer saves us way more than it costs.
From my perspective, I’d say: please don’t let AI make budget decisions that affect which creators get hired. What I’ve noticed is that algorithms optimize for predictability and past performance, which means established creators with proven metrics keep getting more budget while newer creators (even if they’re really talented) can’t break in.
For my own career, I’ve benefited enormously when brands made intuitive bets on collaborating with me even when my numbers didn’t perfectly match their model. Those risks led to my best performing campaigns. If everything was algorithmically allocated, I’d probably still be stuck at micro-influencer tier because the math always seemed to favor established names.