For years we have asked business analysts to do the impossible: navigate sprawling repositories of documents, interpret inconsistent information, and turn scattered knowledge into operational clarity. Inside most organizations, more than 80 per cent of data is unstructured—emails, reports, manuals, notes, scans, images—and it grows at more than 50 per cent each year. Hidden in that volume is insight that could strengthen decisions, speed up processes, and reduce risk. Yet much of it remains inaccessible.
This is why the role of the business analyst is changing. Their mission is no longer to extract meaning from chaos alone. Their mission is to design better discovery—to make the organization’s knowledge usable, governable and ready for transformation. And to do that, analysts need new tools, new methods, and a new mindset.
Retrieval-Augmented Generation (RAG) and the SAS Retrieval Agent Manager (RAM) provide analysts with exactly that shift. They offer a path beyond manual search, tribal knowledge, and scattered systems. They allow analysts to build a culture in which information finds the user and decisions flow faster.
From our customer conversations, we can see the evolution of this role, especially with the possibilities of agentic AI capabilities.
Seeing the organization through Its knowledge
Internal analysts sit at a critical junction. They translate business needs into data questions, and data back into operational insight. But when information is dispersed across hundreds of folders, tools and legacy systems, the analyst becomes a detective rather than a problem-solver.
This is where RAG changes the job. Instead of relying on the model’s “memory”, RAG retrieves relevant internal documents at the moment of the question. It anchors answers to sources the business already trusts. As our work shows, this shift moves AI from guesswork into evidence-based reasoning.
For analysts, this means:
no more searching through ten systems for one paragraph
no more dependency on senior experts to interpret legacy content
no more risk of relying on incomplete or outdated information
By focusing on discovery—how people find what they need—analysts become stewards of organisational intelligence.
Tackling the real productivity blocker: unstructured knowledge
Most analysts know the pain of working with PDFs, scans, tables and inconsistent document formats. Traditional approaches require heavy code or specialist teams to build pipelines. Each project becomes bespoke and reinvented.
RAM was designed to break this bottleneck. It industrialises the entire RAG process—ingestion, OCR, vectorisation, retrieval, generation—without heavy code. Everything is configured through an interface with flags and sliders. Analysts can test different embedding models, evaluate OCR performance, and compare retrieval quality through simple experiments.
This shifts the analyst’s work from “How do I build the pipeline?” to “Which knowledge do we need, and how should we structure it?”
That is transformation. It makes analysts architects of knowledge, not just consumers of it.
Security, privacy and the analyst’s mandate
For many business analysts, especially those in regulated sectors, the biggest fear is accidental exposure of sensitive data. Most cloud-based LLMs are trained on publicly available data and cannot handle confidential material securely.
RAM is built with a security-by-design philosophy. It never trains on your documents. It processes content only at query time and immediately discards it. It can run fully on-premise with no internet connection, supporting the strictest privacy and compliance rules.
This matters deeply to analysts because they are often the first line of defence in risk-aware processes. RAM’s concept of collections—document sets with independent permissions—adds another layer of control. If a user cannot access a collection, the system returns an empty result. No leakage, no partial hints, no surprises.
For analysts, this allows safe exploration of high-value knowledge without compromising governance.
Use cases that have inspired us
RAM’s flexibility and security make it relevant across many domains. Analysts can use it to solve problems that previously required manual review or specialist intervention.
Here are real examples drawn from our implementations.
In maintenance and operations, linking IoT data with technical manuals
Many teams want to move from reactive maintenance to intelligent service. Analysts can connect machine sensor data with the organisation’s maintenance manuals—many of which are scanned PDFs, handwritten notes or legacy documents.
RAM lets analysts query faults, retrieve repair instructions, and generate step-by-step guidance in context. It turns unstructured documentation into actionable intelligence.
In healthcare, instant discovery of clinical history and protocols
Clinicians often need rapid access to patient histories and standard protocols. Analysts can build an RAG pipeline that uses AI to retrieve clinical information stored behind the firewall and summarise it safely.
This accelerates care while keeping sensitive data inside the organisation’s perimeter.
In financial services, compliance, regulations and fraud investigation
Financial analysts face mountains of regulations, policies and model documentation. RAM allows them to query these materials in seconds and retrieve exactly the clauses they need.
This reduces review time, strengthens compliance and speeds investigations.
In the public sector, assisting citizens with consistent, reliable answers
Service teams often depend on large archives of historical tickets and departmental records. RAM makes this knowledge searchable and accessible through natural language.
Analysts can design workflows that reduce case-handling time and improve public service consistency.
In insurance, interpreting contract clauses for claims adjusters
Claims teams need quick access to previous claims and current contract clauses. Analysts can build retrieval flows that surface relevant passages instantly. This leads to faster, more accurate decisions at scale.
A new blueprint for the analyst profession
The internal business analyst is no longer the person who simply interprets data. They are becoming the designer of the organisation’s knowledge framework—the person who turns unstructured information into operational clarity.
RAM supports this shift by providing:
a no-code way to build powerful RAG systems
a secure, on-premise architecture for sensitive knowledge
a governed approach that respects permissions and privacy
a scalable model that removes the burden of bespoke engineering
This is the moment for analysts to step forward and shape what discovery, efficiency and transformation look like inside their organisations. AI will not replace them. But analysts who learn to harness their organisation’s hidden knowledge will define the next wave of value creation.
Getting started is easy – no coding, just insights. Learn more at www.sas.com/ram
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