Rumblings Launches AI Decision Engine for Marketing Teams

Rumblings has launched an AI-powered decision engine that aims to bridge the gap between cultural intelligence and actionable marketing strategy. The Australian startup positions itself as a reasoning layer rather than a simple content generation or data retrieval tool.

The platform was founded by four executives with backgrounds spanning data science, journalism, and marketing. Tom Crawford, former head of advanced analytics and data science at Woolworths Group, partnered with Annabelle Jones and Lori Susko, co-founders of insights consultancy We Scout, and Jenny Ringland, founder of communications agency G+S and former News Corp journalist.

According to the IT Brief Australia announcement, Rumblings combines real-time cultural signals with internal business context including audience behaviour, category dynamics, commercial objectives, brand positioning and user psychographics. The goal is generating tailored recommendations for decision-makers across marketing, strategy, communications, innovation and product teams.

The founders argue the current AI marketing stack leaves a critical gap. Most existing tools either surface information or create content, but stop short of telling companies what action to take. Crawford described the product as a reasoning layer designed to interpret early behavioural signals against deep business context and generate decision pathways, not just insights.

"A lot of the current AI stack is built around productivity. We built Rumblings to combine productivity with judgement, credibility and human collaboration," Crawford said. "The challenge wasn't finding more data. It was building a system capable of understanding a business deeply enough to know which signals are commercially actionable."

This positioning responds to a fragmented market where businesses rely on separate systems for trend forecasting, social listening and content production. Teams end up with more inputs but less clarity on what actually matters for their specific brand context.

Jones highlighted another concern driving the platform's development: the "flattening effect" visible across marketing as generative AI adoption accelerates. "A lot of AI is creating optimisation, but not necessarily originality and that's a real problem," she said. "You can already see the flattening effect happening across marketing, the same aesthetics, same campaign structures, same language."

The platform interprets signals through the lens of each brand's unique context, positioning and permission to play. Two companies in the same category might need completely different responses to the same cultural shift. The future isn't brands using AI to copy each other faster, it's brands using AI to better understand themselves, their audiences and where they can lead.

Market timing appears deliberate. Grand View Research estimates the AI marketing industry will reach US$82 billion in annual revenue by 2030, representing a 25% compound annual growth rate from 2025. A PwC survey found 88% of executives planned to increase AI-related budgets over the next 12 months, with agentic AI among the biggest priorities.

AdNews Australia reporting confirms the platform is currently in pilot conversations across brands, agencies and innovation teams, with a broader release planned for later in 2026.

The launch comes as investors examine a new group of AI software companies promising to improve decision-making rather than simply automate execution. Dominic Matthews, Managing Partner at Trampoline, noted the category was attracting broad attention, although many products remained superficial. "Most are an LLM over a dashboard. If a team has spent years inside the workflow and can now automate it, that's interesting," Matthews said.

Whether Rumblings can actually deliver on its reasoning layer promise remains to be seen. The difference between a sophisticated dashboard and genuine strategic guidance is often thinner than vendors admit (and users would prefer not to discover during a quarterly planning cycle).

The real test won't be whether the platform identifies cultural shifts. It will be whether marketing teams actually trust its recommendations enough to act on them when the data conflicts with internal assumptions. That's where most AI tools quietly fail.

Artūras Malašauskas is an AI Systems Integrator with 20+ years of production-grade web engineering experience. He has designed, shipped, and scaled enterprise Python/PHP systems for logistics, SaaS, and public-sector clients. For the past year, he has focused exclusively on AI integrations: deploying open-source LLMs, building generative media pipelines (image, audio, video), and engineering multi-agent workflows for real production environments. His standard: reproducibility, security, cost-efficient inference—no vaporware. He documents and evaluates emerging AI tooling, separating verified capabilities from marketing noise. Technical editor at: muza-ai.eu, ai-verslas.lt, ai-naujinos.lt Connect on LinkedIn