As of December 2025, the ‘accuracy’ of AI in marketing isn’t about perfect prediction, but about increasingly sophisticated probabilistic modelling – meaning AI systems estimate the likelihood of outcomes based on vast datasets, and continually refine those estimations with new data. This is particularly evident in platforms like Google Marketing Platform and Meta Business Suite, now widely used by Australian SMBs.
Currently, these platforms utilise Generative AI for tasks like ad copy generation and image creation. Accuracy here is measured by metrics like click-through rate (CTR) and conversion rate, and is driven by the quality and relevance of the training data. In 2026, we’re seeing increased integration of Customer Data Platforms (CDPs) – like Tealium, available in Australia – which feed first-party data into AI models. This improves accuracy in audience segmentation and personalisation. For example, a CDP can identify customers likely to respond to a specific promotion based on their purchase history and website behaviour. Meta’s Advantage+ campaign budget, now standard, uses AI to distribute budget across ad sets, aiming for the highest volume of conversions within a set cost-per-acquisition. Compliance with Australian Privacy Principles (APPs) remains crucial when using these systems, requiring transparent data handling practices. Announced for 2027, Google intends to roll out more advanced AI-powered attribution modelling, moving beyond last-click attribution.
Ultimately, AI marketing systems function by analysing data patterns to predict and optimise marketing performance, rather than providing definitive answers.
