In their article about how banks can extract value from a new generation of AI technology, notable strategy and management consulting firm McKinsey identified AI-enabled data pipelines as an essential part of the ‘Core Technology and Data Layer’. They found this infrastructure to be necessary for AI transformation, as an important intermediary step in the evolution banks and financial institutions will have to make for them to see tangible results from their investments in AI.

The technology stack for the AI-powered banking of the future relies greatly on an increased focus on managing enterprise data better. McKinsey’s Financial Services Practice forecasts that with these tools, banks will have the capacity to harness AI and “… become more intelligent, efficient, and better able to achieve stronger financial performance.”
What McKinsey says
The promise of AI in banking
The authors point to increased adoption of AI across industries and organizations, but the depth of the adoption remains low and experimental. They express their vision of an AI-first bank, which -
- Reimagines the customer experience through personalization and streamlined, frictionless use across devices, for bank-owned platforms and partner ecosystems
- Leverages AI for decision-making, by building the architecture to generate real-time insights and translating them into output which addresses precise customer needs. (They could be talking about Reef)
- Modernizes core technology with automation and streamlined architecture to enable continuous, secure data exchange (and now, Cruz)
They recommend that banks and financial service enterprises set a bold vision for AI-powered transformation, and root the transformation in business value.
AI stack powered by multiagent systems
The true potential of AI will require banks of the future to tread beyond just AI models, the authors claim. With embedding AI into four capability layers as the goal, they identify ‘data and core tech’ as one of those four critical components. They have augmented an earlier AI capability stack, specifically adding data preprocessing, vector databases, and data post-processing to create an ‘enterprise data’ part of the ‘core technology and data layer’. They indicate that this layer would build a data-driven foundation for multiple AI agents to deliver customer engagement and enable AI-powered decision-making across various facets of a bank’s functioning.
Our perspective
Data quality is the single greatest predictor of LLM effectiveness today, and our current generation of AI tools are fundamentally wired to convert large volumes of data into patterns, insights, and intelligence. We believe the true value of enterprise AI lies in depth, where Agentic AI modules can speak and interact with each other while automating repetitive tasks and completing specific and niche workstreams and workflows. This is only possible when the AI modules have access to purposeful, meaningful, and contextual data to rely on.
We are already working with multiple banks and financial services institutions to enable data processing (pre and post), and our Cruz and Reef products are deployed in many financial institutions to become the backbone of their transformation into AI-first organizations.
Are you curious to see how you can come closer to building the data infrastructure of the future? Set up a call with our experts to see what’s possible when data is managed with intelligence.