This article is Part 2 of our GenAI Ready series and continues the discussion from our recently published piece on building data readiness foundations for GenAI in financial services.
Five Workstreams to Make Your Data GenAI-Ready
You don’t fix the basement with inspirational townhalls. You fix it with a clear, practical framework and disciplined execution.
A useful way to think about GenAI data readiness is through five workstreams.
Workstream 1 – Inventory & Prioritize
Start by accepting you cannot fix everything at once.
- Baseline the landscape for a handful of high-value domains – customer, credit, product, risk, compliance. Identify systems, key datasets, and major unstructured repositories (policy sites, shared drives, content systems).
- Tie this inventory to GenAI use cases. Shortlist three to five realistic, high-value use cases: underwriter copilot, policy Q&A, advisor assistant, operations triage.
For each, ask:
- What data and documents does this depend on?
- How fragmented and messy are they today?
- Who owns them?
That gives you a map of where to start.
Workstream 2 – Standardize & Connect
Once you know your mess, you can begin to impose order.
- Define a shared language. Align business and data teams on core domain models and definitions. Decide, explicitly, what a customer, exposure, facility, or product is – and how it should be represented.
- Build canonical data products. Instead of letting GenAI talk directly to source systems, create well-governed data products like:
- Customer 360
- Credit Application & Decision
- Exposure & Performance
- Policy & Procedure Library
These become the controlled interfaces through which GenAI gets its facts.
Workstream 3 – Govern & Protect
No GenAI program will scale in financial services without serious attention to governance and protection.
- Classify and control access. Label data by sensitivity (public, internal, confidential, highly sensitive) and enforce role-based access with masking/tokenization rules.
- Define policies for GenAI usage. Make it explicit:
- Which classes of data can go into prompts and logs
- What can be used for fine-tuning or evaluation datasets
- How data is retained, audited, and deleted
Give risk, legal, and compliance something concrete to review and improve, not a black box.
Workstream 4 – Operationalize for GenAI (RAG & Beyond)
With standards and governance in place, you can build the actual GenAI plumbing.
- Stand up GenAI-ready stores. For prioritized domains, implement:
- Document stores for policies, memos, research, etc.
- Vector databases for semantic search over those documents
- Feature/data stores for key structured entities
Ensure they are populated through repeatable pipelines, not manual uploads.
2. Build robust RAG pipelines. Don’t just index PDFs and hope for the best. Design pipelines that:-
- Chunk documents intelligently and preserve context
- Enrich them with metadata (product, region, risk type, date, owner)
- Support evaluation: test questions, expected answers, and guardrails
- Capture feedback from users to refine retrieval and prompts
3. Think APIs and events, not just tables. Expose data products via APIs and event streams that GenAI components can reliably call, with authentication and throttling built in.
Workstream 5 – Measure, Monitor, Improve
GenAI makes your data problems very visible, very quickly. Use that to your advantage.
- Track data and GenAI metrics together. For each use case, monitor:
- Data quality, coverage, and latency
- GenAI answer quality, hallucination rates, response times
- Business impact: time saved, cases handled, error reductions
- Feed insights back into the data roadmap. If the underwriter copilot consistently struggles with certain products or geographies, that’s a hint your underlying data there needs attention. Prioritize accordingly.
A Simple 4-Level Data Readiness Maturity Ladder
Leaders often ask, “Where are we, realistically?” You don’t need a 50-page model to answer that. A simple four-level ladder is enough.
Level 1 – PoC Theater
- Pilots built on exported data and manual prep
- No governance, no integration into core systems
- Impressive demos, zero production impact
Level 2 – Structured Experimentation
- A small central team coordinating experiments
- Some integration into 1–2 systems or domains
- Basic access controls and logging
- Each new use case still feels like a small one-off project
Level 3 – Platform-Ready
- Clear domain models and standardized data products for key areas
- Governed document and vector stores in place
- Reusable RAG components and APIs
- 5–10 GenAI use cases live in production with metrics and monitoring
Level 4 – AI-Native FS Firm
- GenAI and AI agents embedded into daily workflows for RMs, underwriters, ops, collections, compliance
- Data platform and governance continuously refined based on usage and risk feedback
- Business sees GenAI as “how we work”, not as a separate innovation stream
For most institutions, the real journey is from Level 1–2 to Level 3. That’s where cleaning up the basement happens.
BBI in Action – Three Snapshots
Across several client engagements, we have observed the following recurring patterns.
Digital Lender Cleaning Up for Credit Ops Copilots
A fast-growing digital lender wanted an underwriter copilot to summarize complex credit files and highlight key risk factors.
Under the hood, they had multiple LOS and LMS systems, bureau and alternative data scattered across platforms, and credit memos sitting in email and shared drives.
The first step was not a chatbot. It was:
- Designing a unified credit data model across systems
- Building data products for applications, exposures, and performance
- Setting up a document pipeline for KYCs, memos, and supporting docs
- Implementing a governed RAG layer for summarization and Q&A
Only once this foundation was in place did the underwriter copilot become safe and genuinely useful.
Wealth/Asset Manager Preparing for Advisor Copilots
A wealth platform wanted to give advisors an assistant that could answer client questions, explain product options, and reference relevant research instantly.
Advisors were currently juggling client data in one portal, product data in another, and PDFs of research and product notes emailed or saved locally.
We helped them:
- Consolidate client and product data into a canonical layer with clear ownership
- Index product documentation, research, and policy content with rich metadata
- Implement fine-grained access control and logging to keep compliance comfortable
The result: the path is now clear for a copilot that can safely draw from all three worlds – client, product, and research – without creating new risk.
Banking GCC Owning the Basement
A large bank’s GCC was told to “drive GenAI” across the group. They had talent and energy but sat on top of a fragmented data estate owned by global teams.
The breakthrough came when the GCC stopped chasing use cases in isolation and instead:
- Ran a focused data readiness assessment across a few high-value domains
- Defined a pattern: what a GenAI-ready data platform looks like (data products, document & vector stores, governance stack)
- Piloted this pattern in one domain and then offered it as a reusable foundation to other teams
They moved from “innovation showcase” to owning and improving the basement for everyone.
A 90-Day GenAI Basement Clean-Up Plan
You may already be executing a broader AI data roadmap – assessments, board buy-in, governance councils. This 90-day plan zooms in on one GenAI use case and the minimum data, document and governance work needed to ship something real without waiting for a multi-year transformation.
Weeks 1–4: Discover & Prioritize
- Identify 1–2 GenAI use cases that matter (e.g., credit memo summarization, policy Q&A)
- Map the data and documents they depend on
- Assess current maturity: definitions, quality, access, governance
- Align with business, risk, and tech on one flagship use case
Weeks 5–8: Fix the Foundations for One Domain
- Define the shared data model and relevant data products for that domain
- Catalog key datasets and repositories, classify sensitivity, implement basic access controls
- Set up a minimal document + vector store and RAG pipeline for the chosen use case
Weeks 9–12: Ship a Narrow but Real Use Case
- Deploy the GenAI use case to a limited user group
- Measure time saved, error reduction, and user satisfaction
- Document the data and governance issues revealed, and feed them into the next phase of the roadmap
The goal of these 90 days is not perfection. It is to move from abstract strategy to a live example of what “data readiness for GenAI” really looks like in your own environment.
About BBI – and an Invitation
At BBI, we sit squarely in the data basement.
We work with lenders, banks, wealth and asset managers, and GCCs to modernize their data platforms and get them GenAI-ready – from domain modeling and data products to document pipelines, RAG architectures, and the governance that keeps regulators and boards comfortable.
If you’re under pressure to “do something with GenAI” but have a nagging feeling that your data basement will hold you back, that’s exactly the conversation we like to have. If you’re still earlier on the curve and need to get your core data estate AI-ready before you even touch GenAI, start with our article on optimizing data readiness for AI modeling in financial services – and then use this piece as your next step.
If you’d like a pragmatic view of where you are on the data readiness ladder – and what a 90-day path forward could look like – reach out. We’re happy to share the questions we use and the patterns we’ve seen, so you can invite GenAI in only when the basement is ready.
