Published by Jessica Weisman-Pitts
Posted on August 12, 2024
5 min readLast updated: January 29, 2026
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Vikas Krishan Altimetrik
By Vikas Krishan, Chief Digital Business Officer at Altimetrik
Artificial intelligence holds huge potential for transforming the Financial Services sector. Recent news from both Goldman Sachs and JP Morgan on the intended use cases for AI powered LLM’s potentially augmenting, and even supplanting research teams has bought both the revenue earning and business efficiency capabilities of AI to the fore. And yet, despite early adoption of AI technologies, many firms are still grappling with where to start or how to fully leverage their capabilities to drive tangible business outcomes. The key to unlocking AI’s true power requires a fundamental shift in how organisations manage and integrate their data.
The Data Imperative
At the heart of every successful AI implementation is high-quality, accurate data. This cannot be stressed enough: AI is driven by data. The more accurate data an AI system has, the better it can learn and make trustworthy predictions. This simple yet profound reality underscores the critical importance of data integrity in the AI ecosystem. Many Financial Institutions are falling short in this crucial area – both on their ability to utilise internal data but to also provide accurate, timely external data. As companies grow, so does the complexity of their data environment.
This expansion often leads to the creation of data silos, degradation of data quality and the proliferation of disconnected data repositories. The result is a fragmented data ecosystem that hampers AI’s ability to deliver meaningful insights and drive process improvements. Here, Financial Institutions should look to develop a Single Source of Truth (SSOT). A SSOT provides a unified and consistent view of data across an organisation. This authoritative source of core data helps identify operational inefficiencies, monitor customer behaviour and execute strategies to drive much needed growth.
The key benefit of a SSOT for AI applications lies in its ability to generate valuable data insights that can uncover patterns and trends more quickly than traditional methods. This enables companies to react faster, avoiding adverse impacts on financial performance or capitalising on market opportunities. By continuously monitoring operations and vital information in real-time, Financial Services firms can pinpoint areas for improvement and make proactive suggestions to customers regarding their asset holdings or loans.
Breaking Down Silos
To ensure businesses are accessing the full potential of AI, Financial Services firms must break down these data silos and foster a culture of collaboration across departments. This means moving away from the traditional model where teams work in isolation. Instead, Financial Services firms should move towards a more integrated approach to data management. By creating a unified data strategy, organisations can ensure that AI platforms have access to a comprehensive, accurate and up-to-date view of the business. This holistic approach not only enhances the quality of AI-driven insights, but also promotes cross-functional collaboration and innovation. Through this change in cultural practice, the business takes ownership in delivering outcomes, ensuring teams follow a sprint agile process for better outcomes.
The Incremental Path to Success
While the promise of AI is enormous, it is important to recognise that successful implementation is not an overnight process. It’s clear that a big bang approach is both ineffective and expensive. Therefore, emphasis should be placed on solutions that involve business ownership, the selection of high ROI use cases, and the engagement of all stakeholders.
A much more holistic, incremental approach to data management as part of a unified strategy is vital. AI’s potential to transform the financial industry rests on how quickly firms can adopt a Digital Business Methodology (DBM). Through this holistic approach a better understanding of the customer’s key challenges, maturity level, and the relationship between IT and the business can be understood, leading to a more effective roadmap. The DBM enables companies to adopt and implement digital business. It provides a defined path that orchestrates and converges data, technology and people, delivering an outcome-driven approach that drives results with speed, consistency and scale. DBM aims to break down complex projects into manageable bite-sized components through collaboration across all stakeholders.
This measured approach allows organisations to build a robust data foundation step by step, ensuring that each phase of the AI journey is built on solid ground. Through this underpinning of DBM, there is more of an emphasis on data and AI engineering rigour, including governance, security, and compliance. Centralised cloud platforms such as Snowflake and Databricks offer the essential infrastructure to create and manage data assets efficiently, ensuring consistency, speed, and scalability. Integrating a DBM with this platform is essential for businesses to effectively engage with AI. This integration operates within the customer’s environment, while remaining independent of their complex and siloed systems.
By adopting this incremental methodology, Financial Services players can identify and prioritise high-impact areas for AI implementation. Firms can build confidence in AI-driven processes through quick wins. Businesses can then continuously refine and expand AI capabilities based on lessons learned during the staged roll-out. The future belongs to organisations that can effectively harness AI to drive innovation, improve efficiency, and deliver superior customer experiences. By investing in the necessary data infrastructure and talent development, Financial Institutions can not only participate in the AI revolution but lead it, securing their place at the forefront of the industry for years to come.
Artificial Intelligence (AI) refers to the simulation of human intelligence in machines programmed to think and learn. It encompasses various technologies, including machine learning and natural language processing.
Data management involves the practices of collecting, storing, organizing, and maintaining data to ensure its accuracy, accessibility, and security for effective decision-making.
A Single Source of Truth (SSOT) is a data management concept that ensures all stakeholders access a unified and consistent view of data across an organization, minimizing discrepancies.
Data silos are isolated data repositories that hinder information sharing across departments. They can lead to inefficiencies and a lack of comprehensive insights within an organization.
A Digital Business Methodology (DBM) is a structured approach that integrates data, technology, and people to drive digital transformation and improve business outcomes.
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