Meta continues its aggressive push into artificial intelligence with the global rollout of its new Opportunity Score system, part of a broader effort to assist advertisers in improving the performance of their campaigns across Facebook and Instagram. For business users, especially small and medium-sized enterprises (SMEs), this development represents both an opportunity and a challenge. Meta AI ad recommendations now influence how campaigns are judged and adjusted, potentially altering the entire process by which ads are optimised and evaluated. While Meta promotes this feature as a way to deliver better outcomes, advertisers are rightfully asking what this means in practice and whether these automated insights truly deliver on their promise or simply add another opaque layer to Meta’s already convoluted advertising ecosystem.

Opportunity Score is designed to evaluate how well an advertiser is following Meta’s AI-driven best practices when setting up ad campaigns. It offers a numeric score, ranging from 0 to 100, alongside specific suggestions for how advertisers can improve their ads in line with what Meta believes will drive results. These suggestions could include widening audience parameters, adding more creative variations, or switching to Advantage+ placements, all of which are elements Meta’s AI deems optimal based on aggregated campaign data. According to Meta, advertisers who implement these AI suggestions are more likely to see positive campaign outcomes, such as increased conversions, lower cost-per-result, and better reach. However, the reality for many business users is more complex, particularly when they do not have large budgets or access to dedicated ad managers.

One of the main concerns around Meta AI ad recommendations is transparency. While Meta insists that Opportunity Score is not a measure of ad quality or a direct factor in how ads are auctioned or delivered, the simple fact that a score exists often creates confusion. Business users are likely to interpret the score as a rating that influences how their ads perform within Meta’s ecosystem, even if the company says otherwise. This perceived ambiguity can have serious implications for trust, especially among advertisers who have previously been burned by changes to Meta’s algorithm or opaque policies that seem to shift without notice. For those managing their own campaigns, a poor Opportunity Score might be seen as a reason to doubt their own strategy, even when it’s based on clear goals and solid targeting.

Moreover, the AI recommendations provided by Meta often push advertisers towards adopting broader campaign settings, such as Advantage+ placements or expanded targeting. These settings typically give Meta’s machine learning systems more control over where and to whom ads are shown. While that might work well for large-scale advertisers with flexible budgets and wide audience appeal, smaller businesses often operate within much tighter constraints. Local targeting, niche demographics, and specific conversion goals don’t always benefit from AI-driven generalisations. This creates a tension between following Meta’s advice and maintaining control over campaign specifics. Many business users may find themselves wondering whether they are optimising for their own business goals or simply complying with what Meta’s AI considers best practice for the sake of achieving a high score.

Another layer of complexity comes from how these recommendations integrate with Meta’s existing ad tools. Advertisers already have access to tools like Ads Manager, Campaign Budget Optimisation, and the Learning Phase, each of which generates its own data and suggestions. Now, with the addition of Opportunity Score, business users are being asked to interpret yet another metric. This can be overwhelming for those without a strong background in digital marketing, especially since conflicting recommendations may arise between different tools. For example, Opportunity Score might suggest a broader audience while Ads Manager flags low relevance for that same audience. When faced with contradictory advice from the platform’s own systems, business users are left to make difficult choices, often without sufficient guidance.

Furthermore, the introduction of Opportunity Score must be understood within the broader context of Meta’s ongoing automation of ad processes. From Advantage+ campaigns to automated creative generation, Meta is steadily reducing the level of manual control advertisers have over their campaigns. On one hand, this reflects a broader trend in digital marketing towards AI-managed optimisation, which can reduce friction and improve efficiency for advertisers with limited time or expertise. On the other hand, it risks sidelining strategic decision-making in favour of a one-size-fits-all approach. For businesses with unique brand identities or nuanced customer journeys, relying too heavily on automated recommendations may dilute the impact of their messaging or result in wasted spend on less relevant audiences.

There is also a growing sense that Meta AI ad recommendations may prioritise Meta’s own commercial objectives as much as, if not more than, the success of individual advertisers. When these recommendations encourage broader placements or more varied creative formats, it often results in increased ad inventory consumption—something that benefits Meta’s bottom line. This has led to questions around the neutrality and objectivity of these AI-driven insights. Are Meta AI ad recommendations truly based on unbiased performance data, or are they subtly nudging advertisers toward choices that ultimately serve the platform’s financial goals? For business users working with limited marketing budgets, this distinction is critical. Understanding the potential for platform-driven bias is essential when deciding how much trust to place in automated suggestions and how they align with genuine campaign objectives.

From a practical standpoint, business users should treat the Opportunity Score and related AI ad recommendations as helpful indicators rather than strict mandates. The score can highlight areas where campaigns may be underperforming due to overly narrow targeting, insufficient creative assets, or missed opportunities in placement. However, it should not override the advertiser’s own understanding of their target audience, brand goals, and performance metrics. A low score is not necessarily a sign of failure, and a high score does not guarantee success. Instead, it’s one piece of a larger puzzle that should be evaluated in conjunction with campaign data, customer insights, and business objectives.

Advertisers who wish to make the most of Opportunity Score should consider experimenting incrementally with the suggested changes rather than applying them wholesale. For instance, running A/B tests with AI-recommended settings versus existing setups can reveal whether the AI is truly improving outcomes. Businesses can also track performance over time to see whether implementing suggestions actually leads to the promised improvements. This approach allows advertisers to maintain strategic control while still benefiting from Meta’s machine learning insights. In time, these insights may even help businesses develop their own internal benchmarks for what works and what doesn’t within their specific sector or audience.

It is also important for businesses to advocate for clearer communication and support from Meta, particularly as the platform leans more heavily on automation. As AI continues to shape how ads are delivered and evaluated, transparency and education must be prioritised. Meta should be held accountable for explaining how these systems work, what data they use, and what their limitations are. This would empower advertisers to use the tools more effectively and make informed decisions. At present, however, Meta’s support infrastructure often falls short, particularly for SMEs and individual business users who do not have account managers or access to dedicated training.

In conclusion, Meta AI ad recommendations and the Opportunity Score system reflect a significant shift in how ad performance is managed on Facebook and Instagram. While these tools have the potential to streamline optimisation and improve results, they also introduce new challenges around transparency, control, and relevance—especially for business users without extensive advertising experience. Success will depend on the ability of businesses to critically engage with these recommendations, test them against real-world outcomes, and retain strategic oversight of their campaigns. As Meta’s advertising environment becomes increasingly automated, advertisers must adapt while remaining vigilant, ensuring that they leverage AI tools to support—not substitute—their marketing expertise. The Opportunity Score may indeed open doors for better performance, but only if it is used wisely and in balance with the unique needs of each business.