FinTech CMO reveals how AI-native ops doubled pipeline with 25% less budget

Jul 26, 2026 · Topline
🎧 PodShort 82 min squeezed to 3 AI SprinklerAS Sales Tech
Episode artwork
Tim Rutten
CMO at Backbase
Sam Jacobs
Host, Founder & CEO at Revenue Collective
Asad Zaman
Co-host, CEO at Sales Talent Agency
AJ Bruno
Co-host, CEO at QuotaPath
Topline
82 min squeezed to 3
Full episode from Topline
Quotable Moments

You don't need Fable. It's way too expensive. You don't need that level of intelligence. You could actually probably do better with a small language model which is specifically trained for financial conversations or financial type of transactions.

Tim RuttenCMO at Backbase
02:53

It's not the bank's, as I understand what Tim's saying, they're not building their own, they're not vibe-coding their own internal customer support software, they're not vibe-coding their own internal operational software. What they have is an application layer that really understands them as customers, which is Backbase.

Sam JacobsHost, Founder & CEO at Revenue Collective
02:10

It seems like we're or the frontier labs are competing around electricity. Eventually, right? Because all these models are getting incredibly intelligent, and I can tell you already for banking, there's sufficient intelligence. We don't need more intelligent models. We need smaller models, more efficient models, and we need better infrastructure around these models to have the lowest error rates.

Tim RuttenCMO at Backbase
02:51
Key Insights
  • Tim Rutten's team at Backbase achieved a 10x increase in pipeline by leveraging AI tooling, specifically through automating and making agentic certain workloads, allowing them to double the pipeline with 25% less budget.
  • For banks, AI's initial impact is predominantly in internal operations and customer care centers, where significant efficiency gains (up to 90%) are being realized by automating repetitive tasks, particularly those under the COO's purview.
  • The key to successful AI implementation in highly regulated industries like banking is not to use large language models (LLMs) for full workload execution, but rather to apply them in a hyper-deterministic, heavily guard-railed manner for specific reasoning tasks, achieving high accuracy with minimal error rates.
  • European financial institutions are generally more innovative and advanced in adopting new banking technologies compared to their American counterparts, with regulation often being a key driver, pushing for clarity on AI frameworks faster.
  • The AI bubble faces a significant risk if the investments in CapEx and R&D do not translate into substantial, recurring revenue generation in the near term, suggesting a temporal disconnect between spend and measurable impact.
  • The business models of leading AI labs (like OpenAI, Anthropic) are built on selling 'tokens,' implying that if open-weight or open-source models become extremely cheap, the economic viability of these labs and the broader AI asset bubble could be called into question.
  • AI, like electricity or the internet before it, will eventually become a commodity, making it crucial for companies to focus on building AI-native operations and custom-trained, smaller models for specific use cases rather than relying on the largest, most expensive frontier models.
  • Backbase is implementing an 'AI-native GTMOS' (Go-To-Market Operating System) which standardizes and automates marketing, sales, and operations workflows across the entire revenue organization, significantly boosting efficiency and output.
Metrics Mentioned
  • 2.5 billion Euro (Backbase's valuation as a Fintech company)
  • 120+ (Number of banks worldwide powered by Backbase)
  • 25% less budget (Budget reduction achieved while doubling pipeline using AI tooling)
  • 12 years (Tim Rutten's tenure at Backbase across various roles)
  • 23 years (Backbase's operational history)
  • 90% efficiency gain (Observed in customer care call centers using AI)
  • Hundreds of millions of savings (Potential savings for larger players by achieving 90% efficiency gain in customer care)
  • 3-5% error rate (Typical error rate for reasonable compute in AI models, which is unacceptable for critical banking processes)
  • 99% deterministic (Actual implementations of AI in banking are almost 99% deterministic, with AI used for a small 1% gain in reasoning.)
  • 50 million pounds (Lloyds Banking Group's actual ROI from AI last year)
  • 100-200 million pounds (Lloyds Banking Group's projected ROI from AI by end of this year)
  • 0.1% (The 100-200 million pounds ROI represents maybe 0.1% of the bank's total operational use cases.)
  • 10X (Reported increase in pipeline achieved by Tim Rutten's team at Backbase through AI.)

RevBots.ai View:

  • AI Sprinkler teams see biggest wins automating COO-owned workflows first.
  • ARM-stage companies build AI-native GTMOS like Backbase's for compound efficiency.
  • Tab Hoppers mistakenly chase frontier models when smaller, trained models deliver more.
  • SaaS Hoarders face reckoning as AI commoditization exposes bloated tool stacks.
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