AI from start to scale: How financial services firms can use AI to get their data ready for AI

• Financial services firms do not need perfect data to accelerate AI adoption. Organisations can use AI itself to improve data quality, strengthen governance, automate data management, and prepare their foundations for scalable AI deployment.
• Strong data foundations remain critical to AI success. Firms that address fragmented, unstructured, and inconsistently governed data are better positioned to move beyond pilots, scale AI initiatives, and realise measurable business value faster.
• The greatest AI value comes from embedding intelligence across the enterprise. By combining AI-enabled data readiness with strategic, high-impact use cases, organisations can improve decision-making, enhance customer experiences, optimise risk management, and create lasting competitive advantage.

Generative and agentic AI capabilities are advancing at extraordinary speed. Organisations are investing more in AI than ever before, with NVIDIA reporting that 83% of financial institutions plan to increase their AI investments in 2026.

Yet, many financial services firms are discovering that their pace of execution is failing to keep up with their ambitions. They have been unable to deploy AI at the pace stakeholders demand or scale beyond a handful of use cases, and measurable results remain elusive. 

So, how can organisations accelerate their AI efforts and start realising value faster? Many assume that progress requires perfect foundations, including pristine data, exhaustive governance frameworks and precisely planned pilot programmes. At Baringa, we believe this idea is unrealistic, and often leads to delays that compromise competitiveness. Speed is of the essence, and one way to meaningfully accelerate progress is using AI itself to get data ready for AI. 

The readiness gap

At its core, AI is only as effective as the data underpinning it. Without high-quality, well-structured and accessible data, AI systems tend to produce outcomes that are disappointing at best, and inaccurate, biased or a business risk at worst.

Financial institutions have spent decades amassing vast reserves of customer and market data. The majority of this information, however, was created to serve operational or compliance purposes rather than power AI systems. It’s no wonder then that many are finding legacy data landscapes to be a major structural barrier to effective AI adoption.

In large organisations, data typically lies scattered across siloed systems, governed inconsistently and managed by teams working to different standards. Much of this data is unstructured. This means that it lacks the predefined format and consistency that AI models require to function reliably, and companies must take extra steps to access, prepare and use this data effectively.

Without strong data foundations, it is almost inevitable that AI programmes will stall, struggle to scale, and fail to deliver their promised value. Gartner estimates that 30% of generative AI projects will be abandoned due to poor data quality, inadequate risk controls, escalating costs or unclear business value.

Organisations can’t afford to ignore these issues. AI demands better data and a different approach to managing it. Financial institutions that fail to evolve their data practices will find it impossible to unlock the full potential of AI.

Moving into the fast lane with AI

Organisations have access to a broad spectrum of AI capabilities, each suited to different dimensions of data management.

• Analytical AI such as machine learning, predictive analytics and anomaly detection finds patterns, forecasts outcomes and flags risks in structured data
• Language and document AI including natural language processing (NLP), intelligent document processing (IDP) and GenAI unlocks value from unstructured data like emails and PDFs, turning text into actionable insight
• Data quality and automation AI cleans, enriches and connects data across systems, ensuring downstream information is reliable, consistent and understandable

Leaders who are serious about accelerating AI adoption must first rethink how they manage their data. The old playbook says: clean the data first, then build the models. The new playbook says: use AI to clean and analyse data, automate processes and prepare platforms for scale.

The range of applications is substantial. AI and machine learning can optimise data quality management across the information lifecycle, from data retrieval and cleansing through to data source integration. They can also support stronger data governance, providing continuous, automated tracking of data lineage and dependencies, data classification and policy enforcement, compliance monitoring and more.

GenAI can produce synthetic data to address information gaps, and combine with other forms of AI to build the data pipelines needed to train and deploy proprietary AI models. At the frontier, agentic AI makes it possible to orchestrate vast networks of agents that enforce data standards and ensure consistency across entire data architectures.

Finding your best fit

The AI landscape is vast, and the tools within it vary enormously in quality, maturity and strategic relevance. For financial services firms, the challenge is distinguishing between solutions that deliver incremental improvement and those capable of driving truly transformational outcomes. The greatest success will come to companies that look beyond the hype and identify where AI can create enduring value.

This is precisely where Baringa brings vital clarity. We work with financial services firms to pinpoint the highest-value opportunities for applying AI across their data foundations, then deploy AI-driven automation across the data management lifecycle to act on them. Our approach empowers organisations to move beyond competent adoption towards competitive advantage.

Good, better, best: The journey to transformational AI in banking and insurance

Good: Automating repetitive data and operational tasks to accelerate processing times.

• In banking, this means confirming that client documentation meets baseline requirements before progressing to a human agent for know your customer (KYC) validation.
• In insurance, it looks like using AI to review claims submissions against predefined policy criteria before triaging to a handler.

Better: Using machine learning to predict and prevent quality issues before they impact operations, and to surface patterns and risks earlier.

• In banking, this involves reviewing existing client data alongside newly provided documentation to build a fuller understanding of their position, then flagging potential risks against thresholds aligned to KYC and risk management frameworks.
• In insurance, it means combining historical claims data with real-time submissions to identify emerging trends or flag potential instances of fraud.

Best: Embedding AI into the fabric of the business, so that every decision, process and client interaction is informed by deeper, connected intelligence.

• In banking, this includes unifying the end-to-end onboarding journey, from initial engagement through to full system integration, which gives both clients and operational teams richer insights, faster processing and stronger risk mitigation.
• In insurance, it involves integrating underwriting, claims and customer data to enable real-time portfolio-level decision-making and highly personalised engagement throughout the customer lifecycle.

From readiness to value

Our approach helps compress AI readiness timelines from years to months. We have helped financial services clients save thousands of hours of manual work, accelerate deployment and boost accuracy rates by harnessing AI to detect anomalies, resolve quality issues and implement governance protocols in real time.

Once data has been properly prepared for AI use, the opportunities truly start to multiply, allowing organisations to unlock benefits across multiple areas. Examples include:

• Real-time treasury insights that optimise liquidity and pricing, driving sharper decision-making, improved financial performance and reduced risk. AI can significantly accelerate analysis of complex financial information and support more sophisticated modelling and scenario analysis, enhancing cash forecasting accuracy, risk management in foreign exchange, and hedging strategies
• Amazon-like client experiences in capital markets that foster deeper engagement and loyalty. AI can analyse transactional, market and behavioural data to create investment strategies and services that are tailored to each client’s size, position and risk appetite, and adjust these dynamically as market conditions evolve
• AI-powered customer service that resolves complaints faster and strengthens brand loyalty. AI can analyse customer interactions, sentiment and historical resolution patterns to anticipate needs, personalise responses and route complex cases to the right specialist, turning service moments into opportunities to deepen trust.
• Real-time risk and pricing insights that sharpen underwriting and reserving decisions. AI can analyse vast volumes of structured and unstructured data, including claims patterns, loss drivers, and market trends, to identify emerging risk trends, optimise portfolio composition and improve reserving accuracy, whilst feeding valuable insights back into underwriting and risk management.

Building fast, building to last

As pressure mounts for greater AI adoption, investment and results, financial services leaders find themselves facing a paradox. Organisations that move too slowly risk falling behind as technology, competitors and market expectations advance. Yet, launching AI initiatives with weak foundations will only lead to spiralling costs and stalled progress. The financial institutions that will sustain long-term success are those that find the right balance between pace and preparation, building delivery momentum without sacrificing strategic rigour.

In a market awash with AI hype, Baringa helps you distinguish genuine transformation from empty promises. Get in touch to learn how we can work with you to identify the right opportunities, tools and use cases to start unlocking the full value of AI, faster.