Meta’s acquisition of Manus AI in late 2025 and its integration with Meta Business Manager and Ads Manager created immediate curiosity across the industry. The question on most people’s minds is simple: “How will this benefit me and impact how we approach Meta?
So, I decided to test it. Here are my initial findings from a first look and first experiment.
Connecting Manus to Business Manager was straightforward. Once the Meta app is linked and permissions are in place, the integration pulls account data with minimal friction. The process felt almost too simple, which prompted me to dig deeper into what information was being accessed.
The first limitation I noticed (and caveat, this may be due to the lite version’s constraints) is that Manus pulls data for 25 ad accounts at a time. It allows selection of specific accounts, but in environments like ours, where Business Managers house 100+ accounts, manual selection becomes necessary.
When Manus pulls ad account data, it does a strong job of structuring insights into an Account Summary.
Overall, the summaries are useful. They provide a fast overview and highlight areas worth exploring. In many cases, the recommendations align with best practices we already apply internally, which reinforced confidence in our existing approach.
However, when I pushed deeper and asked for optimization recommendations on specific campaigns, limitations became clear.
Firstly, it seems to have overlooked campaign settings and objectives. For example, it suggested frequency capping optimizations on campaigns using conversion objectives where frequency capping is not an available option. It also focused heavily on CTR and CPC as primary performance signals, even when the campaign’s primary KPI was CPA and lower-funnel conversions. This isn’t necessarily wrong insight; CTR and CPC are useful diagnostic metrics. But optimization recommendations should align with the primary KPI, not secondary signals.
Some suggestions also lacked contextual awareness. For instance, it recommended creative changes every two weeks despite industry best practices favouring 45–60 day creative cycles to avoid premature fatigue and learning disruption.
There were also benchmarking discrepancies. In one case, Manus estimated a CPA target that was materially lower than our actual performance baseline, highlighting the importance of human validation when interpreting AI-driven recommendations.
That said, there were meaningful wins.
Manus provided step-by-step guidance on advanced measurement configurations and offline tracking implementation within Meta, saving significant time that would be otherwise spent researching Meta’s business documentation.
It also demonstrated value in surfacing account summaries and potential optimization ideas. While not all recommendations were applicable, the exercise validated our own strategic thinking. Many of the insights mirrored approaches we already recommend internally through our campaign checks and pacing analyses.
Perhaps the most interesting element was watching the tool’s reasoning process in real time. The screen share functionality that displays search activity and data analysis builds transparency and helps users understand how outputs are generated. That level of visibility adds credibility and reduces the black box perception often associated with AI tools.
So where does this leave us?
I see meaningful potential as Manus evolves. Use cases that stand out include:
- Support with Detailed Account Audits
- Support with Pacing, Creative Fatigue and Flags
- Support in finding new creative opportunities and impact of enabling features (Advantage + creative for instance)
- Support in curating A/B testing methodologies
- Support in finding tailored optimizations, articles, and troubleshooting settings for easier access and convenience.
- Support in implementing bulk campaign changes and setting adjustments on live campaigns (Future)
The key word here? Support. AI can accelerate analysis and surface insights, but context, judgment, and creative problem solving remain irreplaceable. Campaigns succeed because of nuanced decision making and understanding human behaviour, not automation alone.
In many ways, the most valuable takeaway from this experiment is validation. The optimizations Manus recommended are the same principles we apply internally: strong creative strategy, disciplined pacing, and data-informed decision making. If anything, it reinforced that we are already thinking at the level these tools aspire to reach.
Our Social Team is continuing to explore Manus and sharing findings as we push its capabilities within available constraints. Stay tuned, this is only the beginning.
