A new Australian platform is launching with a different proposition: helping brands understand what’s changing in culture, and what to actually do about it.
Rumblings, is an AI-powered decision engine that spots early shifts in human behaviour and translates them into clear strategic recommendations for marketers, agencies, communications teams and innovation leaders.
The business was founded by We Scout founders Annabelle Jones and Lori Susko, alongside G+S founder and former News Corp journalist Jenny Ringland and former Woolworths Group head of advanced analytics & data science, Tom Crawford.
The founders say Rumblings was built in response to a growing problem, more information than ever, but less clarity on what matters.
“Marketing teams today are drowning in signals and tools. Social listening tells you what has already happened. Trend forecasting tells you what might matter in 18 months. Generative AI helps you produce more content. But nobody is solving the gap between seeing a cultural shift emerge and knowing what your business should actually do next and why,” Jones said.
“Think of it like your personal insights partner, someone incredibly well read, who’s already consumed everything, spots cultural shifts early, and tells you why they matter to your role and your brand.”
Rumblings combines real-time cultural signals with deep brand intelligence, including audience behaviour, category dynamics, commercial goals and brand positioning, as well as user psychographics, to generate tailored recommendations for decision-makers across marketing, strategy, communications, innovation and product teams.
According to the founders, the current market remains highly fragmented, with businesses relying on disconnected AI tools and teams to piece together strategic direction.
“Right now, companies have trend agencies, internal research teams, social listening platforms, consultants and generative AI tools all operating separately. But very little exists to connect those dots into one clear commercial recommendation. That’s the opportunity we saw,” said Crawford.
Unlike traditional trend-forecasting or social-listening platforms, Rumblings is designed to function less like a reporting tool and more like a strategic collaboration partner, helping organisations pressure-test ideas, identify whitespace opportunities and understand where shifts in culture intersect with genuine commercial relevance.
‘Sea of sameness’
The founders also say the platform has been intentionally designed to push against the growing “sea of sameness” emerging across marketing as AI adoption accelerates.
“A lot of AI is creating optimisation, but not originality, and that’s a real problem. You can already see the flattening effect happening across marketing, same aesthetics, same campaign structures, same language. We built Rumblings to help brands think more clearly, not more generically,” Jones said.
Rather than recommending identical actions to every business, Rumblings interprets signals through the lens of each brand’s unique context, positioning and permission to play.
“Two brands could see the exact same cultural shift, and we see it as Rumblings’ role to filter what is important to the end user, and explain what they should do with it, and why. That’s intentional. 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,” she added.
The launch comes as businesses globally ramp up investment into AI-powered marketing tools, with many organisations now grappling with how to use AI to empower teams, not just operationally.
In a recent PwC survey, 88 per cent of executives said they planned to increase AI-related budgets over the next 12 months, with agentic AI emerging as one of the biggest priorities. According to Grand View Research, by 2030, the AI marketing industry is expected to grow to US$82 billion in annual revenue, a 25% compound annual growth rate from 2025, making it one of the fastest-growing industries in the world.
The founders believe this next phase of AI adoption will be defined less by speed and automation, and more by decision-making quality and collaboration.
