Meta’s Andromeda update marks a transition in how the platform handles ad delivery. While Meta platform updates are nothing new, Andromeda represents a shift from advertiser-led audience selection to an AI-driven model that prioritises real-time behavioural signals over static targeting parameters. It signals a deeper shift in how advertising decisions are made, moving further away from manual audience control and towards AI-led delivery at scale. So what exactly is Andromeda, and how should Meta ad strategies change as a result?
What is Meta’s Andromeda AI engine?
Andromeda is Meta’s next-generation ad delivery engine, powered by advanced AI models and supported by upgraded hardware infrastructure. It uses high-performance hardware to process user data at scale, determining ad placements in milliseconds based on immediate engagement signals.
Rather than relying heavily on advertiser-defined audiences, Andromeda evaluates signals such as content engagement, video views, likes, follows, dwell time, and interaction patterns across Meta’s platforms. These signals are processed in real time allowing the system to make delivery decisions in milliseconds. This allows the system to adjust delivery dynamically, favoring current user behavior over historical demographic data.
To align with this logic, several strategic adjustments are necessary. Strictly defined audience segments often limit the system’s ability to gather data, so the current framework favors Broad Targeting and Advantage+ Audiences. These settings provide the engine with a larger pool of users to test and optimise against. In this AI-led environment, the creative asset effectively acts as the targeting mechanism.
Andromeda uses different creative variations to identify which messaging resonates with specific sub-sections of a broad audience. Successful accounts typically deploy six or more distinct ad variations per campaign, focusing on diverse psychological triggers like social proof or educational content rather than minor visual tweaks.
Creative has become a primary performance lever
In an environment where audience parameters are expansive, the creative asset itself functions as the primary tool for targeting. Andromeda utilises creative diversity to identify which specific messaging resonates with different segments of a broad audience.
Performance data suggests that optimal results are achieved when accounts maintain at least six distinct ad variations per campaign. These assets should be strategically varied, moving beyond simple aesthetic changes to include distinct concepts such as product demonstrations, social proof, and educational content.
This variety provides the engine with sufficient range to match individual ads to specific user intent, meaning the diversity of creative inputs now directly dictates the effectiveness of the system’s optimisation.
Broad targeting still needs structure
While broad targeting allows the engine more flexibility, it does not necessitate the removal of all campaign constraints. Basic parameters such as geographic focus and necessary demographic alignments remain useful for providing the system with initial context without hindering its learning capacity.
However, budget allocation is now a more critical factor in performance stability. Andromeda requires a high volume of data to calibrate its delivery, so larger budgets may allow for a faster transition through the learning phase. Testing these automated structures with insufficient spend can lead to inconsistent results, making realistic budget planning essential for effective optimisation.
Implementation and risk mitigation
A data-backed approach to adopting these updates involves running Andromeda-driven campaigns in parallel with established strategies rather than replacing them immediately. This parallel testing allows for a direct performance comparison while protecting core revenue streams.
Utilising controlled A/B testing helps determine where the AI-led model adds measurable value and where manual refinements are still required. Because the system is designed to iterate based on performance feedback, ongoing testing should be viewed as a permanent component of the social media advertising strategy.
What Andromeda signals for the future of Meta advertising
Andromeda reflects a broader shift in paid media. Platforms are moving away from advertiser-led optimisation and towards AI-led interpretation of user behaviour. Success increasingly depends on signal quality, creative diversity, and strategic oversight rather than manual configuration.
This does not remove the need for experience. Instead, it changes where that experience is applied. Strong strategy now sits in how campaigns are framed, how creative is developed, and how performance is interpreted, not in micromanaging audience splits.
Final thoughts
As Andromeda becomes more deeply embedded across Meta’s platforms, the gap will widen between accounts built for AI-led optimisation and those still relying on outdated structures. The opportunity now is to test, learn, and adjust while the system is still evolving. If you need a clear view on how Andromeda could fit into your wider paid media strategy, and what changes will actually move the needle for your business, our team can help you pressure-test your current setup and plan the next steps with confidence. Get in touch with us now to start a conversation.
