Scaling AI in Financial Services: Beyond the Hype

In this guest blog, we’re pleased to welcome insights from Simon Axon, Global Financial Services Strategist at Teradata. Simon explores the challenges financial institutions face in scaling AI beyond proof-of-concept. He examines how banks can move from traditional data management to real-time, signal-oriented AI applications that support fraud prevention, personalisation, risk management, and regulatory compliance.

As AI begins to deliver measurable economic impact – with 42% of firms reporting cost reductions and 59% seeing revenue growth – a critical challenge remains: scaling beyond proof-of-concept. Many organisations find themselves in what’s often called “AI theatre” – impressive demos that never reach production.

The core challenge isn’t the AI technology itself. It’s the data foundation underneath, which often acts like “AI on quicksand.”

The Real Barrier to AI at Scale

Gartner predicts that 70% of generative AI projects will be abandoned after proof of concept. The reasons are familiar: poor data quality, inadequate risk controls, escalating costs, and unclear business value. These aren’t technology failures – they’re strategic misalignments.

In financial services, where precision, trust, and speed are paramount, the foundation for AI must be more than technically sound. It must be intelligence-ready.

From Data Management to Intelligence Activation

Historically, data maturity was measured by how efficiently organisations could store, govern, and query information. But in the AI era, value creation is defined by intelligence activation – the ability to transform data into contextual, autonomous action.

Working with banks globally, a clear pattern emerges: those leading the market aren’t just collecting data – they’re acting on AI-driven intelligence in real time. This shift marks the beginning of what I call the Intelligence Activation Era.

Signals Over Static Systems

Consider customer personalisation. Traditional systems might offer segment-based offers based on geodemographics. But AI can detect nuanced behavioural signals – such as browsing Rightmove or Zoopla listings, initiating a conveyancing process, applying for a Help to Buy ISA, engaging with mortgage calculators, or scheduling viewings via estate agent apps – and proactively offer tailored solutions at the exact moment of need.

This isn’t just smarter targeting. It’s a fundamental shift from demographic assumptions to intent recognition, dramatically improving conversion rates and customer experience.

The Architecture of Scalable AI Intelligence

To unlock AI’s full potential, financial institutions must move beyond traditional data platforms and embrace an AI-native, signal-oriented architecture that activates intelligence across three integrated layers:

Data Layer: AI agents construct signal-ready data products that are reusable, discoverable, and trustworthy outputs – designed and maintained with the rigor of software products – that deliver value to users and systems by feeding AI models and autonomous decisioning.
Application Layer: AI-driven systems consume intelligence and act in real time – enabling contextual decisions across fraud detection, marketing, and risk management.
Interface Layer: AI-powered natural language interfaces allow business users to interact with the system through conversation, turning intent into action without technical barriers.

This layered approach ensures that intelligence flows seamlessly across the enterprise – from data creation to decision execution to user engagement. To bring this life, consider a customer browsing mortgage calculators online. The Data Layer captures this behavioural signal alongside their transaction patterns and financial profile, creating enriched intelligence products. The Application Layer instantly processes this intelligence to trigger personalised mortgage pre-approval offers at the optimal moment. Meanwhile, the Interface Layer allows the customer to simply ask “What mortgage options suit my situation?” and receive tailored recommendations through natural conversation, turning complex financial analysis into an effortless experience.

What This Means for Financial Services

Fraud Prevention That Anticipates: AI models predict and prevent fraud before it occurs, not just flag it after the fact.
Personalisation That Resonates: Deep insights enable truly individualised customer experiences across every touchpoint.
Risk Management That Sees Ahead: AI uncovers emerging risks and enables new approaches to credit scoring, enhancing financial inclusion.
Regulatory Confidence by Design: Every model and query remains auditable, explainable, and defensible – even as systems become more autonomous.

The Competitive Reality

Organisations that activate AI-driven intelligence at scale achieve unprecedented operating leverage. Decisions accelerate, automation compounds, and competitive advantage becomes self-reinforcing.

Success no longer hinges on storing data – it depends on acting on AI-powered intelligence.

Partnering for Scalable AI Success

At Teradata, we’re helping financial institutions build the AI foundations for signal-oriented transformation. From infrastructure to expertise, we enable banks to activate intelligence across their operations – securely, scalable, and strategically.

The future of banking belongs to those who can harness the power of AI-driven signals. The journey starts with understanding your current capabilities, identifying the gaps, and taking decisive steps toward integration and scalability.

What’s your experience with scaling AI in financial services?

At Teradata, we’re keen to hear about your experience with scaling AI in Financial Services. Are you seeing similar patterns in your organisation – or breaking through them? I’d love to hear your thoughts, successes, and roadblocks. Connect with me on LinkedIn here, or drop me an email at Simon.Axon@Teradata.com.

Finance Month at The Data Lab Community

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