Table of Contents
Table of Contents
Table Of Contents
Table Of Contents
Explore 3 real-world use cases for modern data architecture for AI.
To succeed with AI, enterprises need a modern data architecture that prepares, delivers, and governs multi-structured datasets. Such an architecture optimizes bleeding-edge technology while integrating with traditional elements.
But building this architecture is easier said than done:
More than a third (36%) of business, data, and AI leaders say they lack a defined data architecture and roadmap. Another 29% cite cloud modernization and platform selection as a barrier.
Source: BARC, Upgrade Your Data Architecture for AI Breakthroughs
To overcome these obstacles, leaders need detailed guidance and scenario-specific clarity. For example, what does a modern data architecture look like when you’re trying to reduce customer churn with AI? How about when you want to automate regulatory compliance or process documents?
A modern data architecture for AI isn’t a single blueprint, it’s a set of incremental, use-case-driven decisions about how to store, manage, and consume multi-structured data to support the AI outcomes the business requires. With this in mind, this article explores architectural elements and AI use cases for customer service, regulatory compliance, and document processing.
Key Takeaways
- More than a third (36%) of business, data, and AI leaders say their lack of a defined data architecture and roadmap is a top challenge; starting with specific business use cases provides clear direction.
- Modernizing customer service architectures requires adding graph functions to lakehouses to map the customer journey and adopting consolidated tools that integrate data pipelines, MDM, and data cataloging.
- Regulatory compliance AI relies on unified pipelines, observability, and cataloging tools to trace data lineage and improve transparency.
- Document processing and agentic AI require a multi-structured data foundation that supports multi-model orchestration.
- BARC's four guiding principles: Successful data modernization requires data leaders to start small and scale, adopt a factory design, adjust roles and skillsets, and extend data governance programs to cover AI risks.
61% of AI adopters add AI-specific technology to their existing architecture to support AI innovation, while fewer than half overhaul the data management or analytics aspects of their architecture.
Source: Informatica, Upgrade Your Data Architecture for AI Breakthroughs
To stay competitive, leaders must adapt product offerings, go-to-market strategies, and back-office processes. But they also must maintain governance standards to minimize risks. These dual needs for innovation and governance drive companies to modernize their data environments while retaining proven processes and elements.
However, taking an incremental approach works only if decisions are connected to specific business outcomes. When they aren't, three failure modes crop up:
Technical debt and platform sprawl
IT and data teams take shortcuts that meet short-term goals but hurt long-term efficiency by accumulating incompatible platforms, tools, and applications. Adding tools without proper integration creates data silos that directly undermine the AI inputs those tools were meant to support.
Governance gaps
Companies struggle to maintain trustworthy data, thanks in part to their technical debt. Upgrading storage and compute power without extending governance poses serious risks to compliance, security, and decision-making, leaving AI outputs untrustworthy.
Primitive data culture
This occurs when organizations lack the values, norms, and training needed to communicate and collaborate effectively with data, leaving teams unequipped to transition responsibilities and skillsets toward AI workloads.
AI initiatives that overcome these challenges deliver significant business benefits. They increase customer satisfaction and loyalty by anticipating needs and streamlining interactions, which contributes to higher revenue. They also streamline operations by automating processes and optimizing performance, reducing overall costs.
The key to achieving these benefits? Begin with the business problem first and work backward to the architecture. The following examples demonstrate data modernization led by the business to support three use cases: customer service, regulatory compliance, and document processing.
Use case 1: Reduce customer service churn with AI
The business problem
Suppose an online bank wants to reduce high churn rates among customers aged 24–35. Its president and CDO form a tiger team of business managers, analysts, data scientists, and data engineers to tackle the problem.
The architecture decisions required
First, the tiger team integrates and analyzes a range of data, including sales and financial records from relational databases, customer service tickets from the customer relationship management (CRM) application, and user clickstreams from the website and mobile application. The team then makes incremental changes across the architectural layers:
Storage layer: The team adds graph functions to their data lakehouse so they can connect data points across the consideration, purchase, and renewal stages to map the customer journey.
Management layer: They replace separate pipeline, master data management (MDM), and catalog tools with a consolidated tool that integrates all those functions to manage diverse data efficiently.
Consumption layer: Finally, the tiger team upgrades their AI platform to include GenAI language models in addition to machine learning (ML) and business intelligence (BI) functions.
What this architecture enables
This modernized architecture helps the tiger team assess and address their business problem. They use an ML clustering model to identify a sub-segment of customers (high-income women) that change to more traditional banks upon having their first child. They use natural language processing to parse customer service records, which indicates that these new mothers asked for guidance about college savings plans before canceling their service.
Based on these findings, the team spearheads a partnership with a financial services organization to offer 529 planning services.
Use case 2: Automate AI governance for regulatory compliance
The business problem
Consider a global retail chain that must comply with ongoing European Union AI Act requirements. The act demands that companies classify their AI systems’ risk on a scale ranging from “unacceptable” to “minimal” and manage them accordingly.
The architecture decisions required
The CDO engages their cross-functional governance team to execute a three-pronged initiative: adopt a risk assessment framework, extend their policies to address AI, and strengthen data privacy.
To support the compliance initiative, the data team makes two critical upgrades to their architecture:
Transparency infrastructure: Data stewards adopt a unified pipeline, observability, and cataloging tool to trace the lineage of customer data and AI models.
Regional data residency: The team migrates and consolidates customer data onto cloud-based lakehouses. One for Europe and another for North America.
What this architecture enables
The retail chain improves transparency to assess AI risks and enforce new AI policies. By consolidating regional data onto cloud lakehouses, they reduce exposure associated with cross-border personally identifiable information (PII) transfer risks while enabling stewards to maintain auditability.
Use case 3: Modernize document processing
The business problem
As a final example, consider a mortgage provider that aims to attract first-time homeowners. The COO and CDO form a tiger team of business, data, and AI stakeholders.
The architecture decisions required
The team modernizes their architecture by consolidating customer records, demographic data, and external assessments of social media discussions onto a cloud lakehouse.
What this architecture enables
The consolidated lakehouse becomes the multi-structured data foundation for a new agentic application that leverages three types of AI models: a clustering model to define first-time homebuyer types, a recommendation engine to propose next steps for target buyers, and a GenAI language model to converse with customers through the application process. The agentic application orchestrates these models, delegating tasks to each and taking autonomous actions based on their outputs.
BARC’s 4 modernization principles for AI-ready data architecture
Across all three use cases, four principles determine whether data architecture modernization delivers AI outcomes or infrastructure complexity.
Principle 1: Start small and scale
Begin with an achievable project, perhaps customer churn or an operational bottleneck, that delivers near-term results and builds the executive support and budget for larger projects.
Principle 2: Adopt a factory design
Use a modular, governed, product-oriented architecture, like a factory that enforces standardization over artisanship, so analytics teams can confidently consume data products that meet minimum viability standards.
Principle 3: Adjust roles, responsibilities, and skillsets
Take stock of your team's roles, responsibilities, and skillsets, then close AI-readiness gaps with staff changes, hiring, and training.
Principle 4: Extend data governance to cover AI
Since AI risks like inaccuracy, privacy, bias, explainability, and IP mishandling stem from data, adapt your data governance program to cover them, with an eye on regulatory requirements and responsible AI.
The bottom line: Use-case-driven architecture is key to AI success
Modern data architecture for AI isn’t one-size-fits-all. It’s a sequence of use-case-driven decisions including which data to consolidate, which management tools to integrate, and which AI consumption patterns to build for. The three use cases highlight what those decisions look like in practice. Different business problems require different architecture choices, but all of them require the same foundation: trusted, governed, and multi-structured data that AI models can actually use.
Modern Data Architecture for AI FAQs
A modern data architecture for AI comprises platforms, tools, and applications that prepare, deliver, and govern multi-structured datasets across distributed infrastructure. It integrates traditional elements like data warehouses and relational database management systems (RDBMS) with new platforms like vector and graph databases across storage, management, and consumption layers.
Key enterprise use cases include customer service (such as predicting and reducing churn), regulatory compliance (such as meeting EU AI Act standards), and document processing (such as running multi-model agentic applications for mortgage processing).
Most AI adopters take an incremental approach rather than overhauling systems wholesale: 61% add AI-specific technology to their existing architecture. Modernizing architecture involves integrating vector search and graph functions into cloud lakehouses, consolidating management toolsets, and extending governance programs.
Agentic applications require a consolidated, multi-structured data foundation (such as a cloud lakehouse) that integrates structured and unstructured sources. This enables the application to orchestrate multiple AI models, for example clustering, recommendation, and GenAI, and execute automated actions.
The three primary challenges are technical debt (accumulating incompatible platforms and tools that create data silos), governance risk (untrustworthy data that creates compliance and decision-making risks), and a primitive data culture (a lack of values, norms, and training needed to collaborate effectively with data).
