The State of RAG in 2025: Bridging Knowledge and Generative AI

Retrieval Augmented Generation (RAG) Comes of Age: : Addressing Enterprise AI Challenges

In the race for AI-driven transformation, many enterprises find themselves grappling with a critical paradox: how to unleash the power of LLMs without compromising data accuracy, regulatory compliance, or spiraling operational costs. Outdated insights, hallucinated outputs, and the prohibitive expense of continuous model retraining are no longer just technical hurdles – they're quickly morphing into direct threats to ROI and competitive advantage.

In 2025, retrieval augmented generation (RAG) is not just a solution; it's the strategic imperative addressing these core enterprise challenges head-on. At its core, RAG bridges the gap between large language models (LLMs) and the ever-expanding corpus of organizational knowledge. How? By retrieving verified, contextually relevant data at the moment of generation, ensuring AI outputs are both informed and trustworthy.

Unlike generative AI powered by pre-trained and fine-tuned LLMs, which generate answers based on static training data, retrieval augmented generation grounds responses in real-time, curated, proprietary information. This enables enterprises to build systems that are not only intelligent but also compliant, secure, and scalable. In a landscape where outdated knowledge or misinformation can carry significant operational and legal risks, RAG provides the confidence layer that businesses need. 

Inside the RAG Search Process Flow

How does RAG actually work? The process begins when a user enters a prompt. Based on this prompt, the system queries an enterprise’s knowledge bases, retrieves the most relevant information, and feeds it alongside the prompt into an LLM. The LLM then uses the enhanced prompt to generate an accurate output. 

Several preparatory steps are crucial to ensure the knowledge base is rich, relevant, and ready. 

• Data Ingestion: Brings in structured and unstructured information from across the organization – documents, emails, databases, and more.
• Data Preprocessing: Cleans and normalizes the data, removing duplicates and handling inconsistencies to ensure integrity.
• Chunking: Breaks large documents into smaller, manageable pieces, enabling the system to search and retrieve only the most relevant portions rather than full documents.
• Vectorization: Transforms each chunk into a numerical representation using embeddings, making it possible to accurately compare the semantic meaning of content.
• Indexing: Organizes these vectors into a searchable structure, optimizing for speed and precision when matching user queries.
• Metadata Enrichment: Adds crucial context – such as author, source, date, or other forms of data classification, further refining the retrieval process.
• Quality Assurance: Ensures every step functions properly, confirming that the right data is being retrieved and used effectively.

Each of these upstream processes is critical in determining the performance of a RAG-powered application. From reducing noise to increasing precision, they collectively ensure that enterprise Generative AI delivers reliable, traceable, and contextually grounded answers.

While each step may be simple in theory, scaling them to full-scale enterprise deployments requires deep expertise. 

What’s New in RAG: Key Wins Driving Performance

While RAG excels at surfacing relevant information quickly and reducing hallucinations by anchoring answers to trusted sources, traditional RAG systems still face limitations. Their performance depends heavily on the quality and structure of the retrieved data, the LLM's context window size, and the system’s ability to validate generated outputs. 

When workflows require more than just answering static queries – such as reasoning through multistep tasks, complying strictly with regulatory requirements, integrating real-time operational data, or retrieving data with deterministic accuracy – RAG alone often falls short. 

In 2025, advanced RAG systems address these and other limitations with a variety of innovations and architectural considerations. These enhancements push RAG from useful to indispensable – positioning it not just as a search enhancer, but as a core enabler of enterprise AI that can operate safely, contextually, and at scale.

Knowledge Graphs (GraphRAG)

GraphRAG combines vector search with structured taxonomies and ontologies to bring context and logic into the retrieval process. Using knowledge graphs to interpret relationships between terms has paved the way for deterministic AI accuracy – boosting search precision to as high as 99%. A prerequisite for effective and highly accurate GraphRAG is a carefully curated taxonomy and ontology. 

For enterprises, this translates directly into confidence for high-stakes decisions, reduced human review cycles, and a dramatic decrease in the risk of misinformation impacting critical operations (e.g., financial reporting, legal discovery).

AI Guardrails

Embedding AI guardrails directly into the generation process elevates the trustworthiness of Generative AI outputs to the next level. These may include aligning Generative AI to user roles, ensuring policy and legal compliance, brand tone enforcement, and enhancing efficiency and quality. Guardrails enrich prompts with contextual and role-specific information and validate outputs to meet regulatory and organizational standards – ensuring consistent, safe, and on-brand results.

This is heightened alignment is essential for mitigating legal and reputational risk, ensuring consistent customer experience, and maintaining regulatory adherence across all AI-driven interactions, especially in regulated industries.

Operational Data Integration

Unlike traditional ETL processes, advanced RAG platforms are now able to connect directly to structured data sources via API. This real-time access allows GenAI to incorporate operational insights from both structured (databases, spreadsheets) and unstructured (emails, chats) data,  enhancing the quality of outputs and enabling faster, more informed decision-making and paving the way for a variety of previously unavailable use cases.

Access to operational data empowers real-time, data-driven decisions that are grounded in your freshest operational reality, unlocking new use cases for actionable insights that were previously impossible.

LLM Agnostic Architecture 

The most future-proof retrieval augmented generation systems are LLM-agnostic by design, allowing seamless integration with a variety of large language models. The flexibility provided by LLM-agnostic RAG platforms empowers organizations to select models that best align with their specific needs, security requirements, and cost considerations. By avoiding vendor lock-in, LLM-agnosticity enables businesses to adapt swiftly to the evolving AI landscape, ensuring long-term adaptability and control over their AI strategies.

For businesses, this provides essential strategic agility and cost control, allowing your enterprise to adapt to rapid shifts in the AI landscape, avoid vendor lock-in, and select the optimal LLM for specific needs without re-architecting.

Flexible Deployment Options

Offering multiple GenAI deployment options, including on-premises, private cloud, and hybrid setups ensures that organizations can maintain data sovereignty, comply with regulatory requirements, and integrate seamlessly with existing systems. By providing tailored deployment strategies, forward-looking GenAI platform vendors support enterprises, including in harshly regulated industries, in achieving optimal performance and security in their AI initiatives.

This degree of deployment flexibility ensures full data sovereignty and regulatory compliance, offering the flexibility required for secure integration within any existing IT infrastructure, which is especially critical for highly regulated sectors.

Massively Scalable Data Access Control Enforcement

Security and compliance are paramount in enterprise settings, making granular, fully scalable data access control mechanisms, including real-time access control lists (ACLs), role-based permissions, and comprehensive audit trails a prerequisite for production deployments. These features ensure that sensitive data is accessible only to authorized personnel, aligning with stringent regulatory standards and safeguarding against unauthorized access.

For banks, financial service providers, and other organizations dealing with sensitive information, protecting sensitive data with granular access controls guarantees secure and compliant AI deployments, essential for meeting stringent enterprise security and privacy mandates.

Cost-Effective Scalability

The entry ticket to real-world enterprise-wide impact is the capability of handling extensive data volumes in the terabytes and user bases in the tens of thousands, while still ensuring accurate, privacy-enabled, secure, and cost-effective low-latency performance. Fortunately, a small number of leading enterprise GenAI platform providers have demonstrated their ability to deliver on the promise of scaling deployments. 

With organizations across industries struggling to scale their GenAI deployments to production, proven, cost-effective scalability ensuring that AI initiatives can handle massive data volumes and user demands efficiently, minimizing compute costs while maximizing performance and privacy at scale.

Where RAG is Delivering Value in 2025: Real-World Enterprise Success

Let's be honest: RAG often fails in endless proof-of-concepts that never scale. Why? Because many pilots overlook what matters to a CSO (privacy), CFO (risk and ROI), CTO (scale), and COO (operations). Ultimately, it all boils down to scalability: To unlock full value, RAG needs to prove itself in enterprise-scale deployments – handling millions of documents, thousands of user roles, and complex entitlements.

At Squirro, we don’t just talk AI – we deliver it at enterprise scale. From 10,000-user deployments and 15 million+ document rollouts to complex privacy and governance requirements in highly regulated environments, we’ve delivered GenAI deployments that have stood the test of time. Building a small-scale RAG may be straightforward. The true test is scaling it securely, cost-effectively, and in full compliance with enterprise-grade data governance — and that’s exactly where Squirro excels.

Enterprise AI Use Cases Enabled by RAG

From customer support to internal research, market intelligence, and employee knowledge portals, our RAG-based enterprise GenAI platform is turning structured and unstructured data into operational insight. Sales teams gain faster access to competitive intelligence; service reps get real-time responses; executives get contextual summaries that drive better decisions.

Semantic Enterprise Search for Manufacturing: Henkel's Success Story

Henkel, a global innovation leader, partnered with Squirro to transform internal knowledge sharing. By streamlining over 300,000 search results from 45+ data sources, Henkel fostered collaboration, reduced redundancy, and fueled innovation among its technical teams. This strategic partnership enhanced Henkel’s market leadership and set a new standard for sustainable growth.

=> Read our case study on semantic enterprise search for manufacturing

High-Stakes, Regulated Environments: Banking and Financial Services

Financial institutions, law firms, and healthcare providers now trust RAG to augment workflows where accuracy, auditability, and explainability are non-negotiable. With structured oversight and permission-aware access, RAG systems can operate safely even in the most regulated sectors.

Empowering Client Advisors with AI Workflow Automation

A wealth management firm partnered with Squirro to equip client advisors with GenAI Employee Agents. This enabled faster, data-driven decisions, improved regulatory compliance, AI workflow automation, process optimization, and client management, ultimately enhancing client service and relationships.

=> Read our case study on empowering client advisors with AI workflow automation

Saving Millions in OPEX with AI-Driven Ticket Classification

A multinational bank partnered with Squirro to use AI ticketing for faster, more accurate handling of millions of cross-border payment exceptions annually. This significantly reduced manual processing time and costs, saving millions in OPEX and freeing up capital for revenue generation.

=> Read our case study on saving millions in OPEX with AI-driven ticket classification 

Streamlining audit and compliance workflows

A major European bank used the Squirro Insights Engine to automate audit and compliance, saving over EUR 20 million in three years. This automation of risk detection, operational efficiency, and document analysis freed up the time equivalent of 36 full-time employees and achieved ROI in just two months post deployment.

=> Read our case study on streamlining audit and compliance workflows 

From RAG to ROI: Why RAG Matters More Than Ever

Beyond Chat: A Strategic Decision-Making Asset

RAG is no longer just an enhancement for AI chatbots – it’s the strategic backbone of enterprise knowledge management and knowledge access. As AI moves from novelty to necessity, RAG offers a repeatable, scalable way to bring intelligence to the point of work, for example by streamlining investment analysis. 

With real-time, role-aware augmentation, executives, analysts, and frontline teams are empowered to make better decisions, faster. No more digging through SharePoint or outdated PDFs – just the right answer, right now.

• Reduced operational overhead
• Fewer errors
• Enhanced customer service quality
• Shorter search-to-decision cycles.

A Competitive Edge in the Knowledge Economy

With the current wave of retirements in sectors including banking and financial services, decades of expertise in compliance, risk management, client relationships, and complex workflows are walking out the door. Meanwhile, new hires, while capable, often lack the time and structure to learn at the same pace. 

RAG enables organizations to face this generational change by mining their own data assets at scale. It democratizes expertise, reduces silos, and allows any employee to operate like a domain expert, offering a powerful competitive edge in the knowledge economy. 

• Increased productivity
• Faster onboarding
• Reduced dependency on subject matter experts
• Elevated performance and customer experiences

Measuring the ROI of RAG in the Enterprise: Five High-Impact Drivers

While RAG is a powerful architectural innovation, its business value extends far beyond model performance. As enterprises scale GenAI initiatives, the real differentiator becomes how well these systems drive efficiency, insights, and growth. Here are five high-impact ROI drivers of RAG in real-world enterprise environments:

1. Time-to-Insight Reduction

RAG systems reduce the need for manual information search by surfacing contextual answers instantly — grounded in proprietary enterprise data. This dramatically speeds up research cycles in legal, compliance, and support use cases.

2. Lower Model Maintenance Costs

With RAG, organizations can continuously update content in their information retrieval layer without having to undergo the costly process of retraining fine-tuned large language models. In addition to drastically cutting compute and DevOps overhead, this speeds up iteration cycles and reduces AI infrastructure spend.

3. Accelerated Onboarding & Shorter Time-to-Value

New hires no longer need to learn where knowledge is stored — they can query RAG systems from day one and get domain-specific insights. This reduced ramp-up time in knowledge-intensive roles and faster path to revenue generation for sales, consultants, and analysts puts new joiners on a fast track to revenue contribution.

4. Increased Coverage of Investment Opportunities

In banking and asset management, RAG can surface overlooked or emerging signals from unstructured data — like research notes, market sentiment, or historical filings, enabling banks to assess more investment opportunities and prioritize deals with high return potential.

5. Stronger Risk & Compliance Alignment

RAG delivers traceable, source-backed outputs, which is critical in regulated industries like finance and healthcare, reducing legal exposure, improving audit-readiness, and paving the way for enhanced model explainability.

The Case for RAG Over Fine-Tuning LLMs in Evolving Data Environments

Fine-tuning large language models has been a popular approach for tailoring AI to specific tasks or domains, offering strong performance when working with well-defined, static datasets. However, this method comes with notable limitations – high maintenance costs, slower adaptability to new information, and potential data privacy concerns – that can hinder its practicality in enterprise environments.

As organizations increasingly operate in fast-changing, data-rich contexts, these constraints make fine-tuning less suited for scalable, responsive AI solutions. In contrast, RAG offers several key advantages that address these challenges, providing greater flexibility, efficiency, and real-time adaptability. Here's a closer look at how RAG outperforms fine-tuning in dynamic, enterprise settings.

1. Real-Time Data Access and Agility

RAG systems integrate up-to-date information from data sources without the need for retraining, ensuring that AI outputs are always up-to-date – an essential feature in always evolving enterprise environments. Fine-tuned LLMs, in contrast, are typically trained on static datasets and need to be retrained to incorporate new information, leading to costs, delays, and increased resource consumption.

2. Scalability and Efficiency

Retrieval augmented generation systems are inherently scalable, as they retrieve only the most pertinent data for a given query, reducing computational overhead. This selective retrieval improves the effectiveness of processing large and diverse datasets. Fine-tuned models, however, need to process extensive datasets during training and retraining, which can be resource-intensive and less adaptable to growing data volumes.

3. Cost and Maintenance

Maintaining fine-tuned LLMs involves periodic retraining to accommodate new data or changes in the domain, incurring ongoing costs and resource allocation. RAG systems avoid this by accessing external data sources at inference time, reducing the need for retraining and the associated expenses.

4. Security and Data Privacy

RAG frameworks can ensure that only those authorized are given access to sensitive data, for example using access control lists. This helps to minimize the exposure of sensitive data and ensure compliance with data privacy regulations and organizational policies. Fine-tuning requires access to large datasets, which may include sensitive information, posing potential privacy risks if not managed properly.

5. Flexibility and Adaptability

RAG systems offer greater flexibility in adapting to new or evolving information landscapes. They can incorporate new data sources, new large language models, and other emerging technologies without the need for model retraining. Fine-tuned models lack the adaptability provided by LLM agnostic RAG-based GenAI platforms, as they are grounded in a foundational large language model, limiting their responsiveness to change.

While fine-tuned LLMs may perform well in static or narrowly defined domains, RAG approaches provide superior adaptability, efficiency, and compliance capabilities, making them more suitable for dynamic and data-intensive environments.

Agentic AI Will Drive Demands on RAG 

As enterprises lean harder into AI-driven transformation in 2025, RAG systems are facing growing demands – not just to retrieve information, but to enable action, accountability, and adaptation at scale. The expectations go well beyond helpful chat responses; they now sit at the core of enterprise-grade intelligence workflows.

AI Agents: From Insight to Execution

The most urgent pressure on RAG today comes from the rise of AI agents – autonomous or semi-autonomous systems designed to perform multistep processes. These agents don’t just answer questions; they plan, execute, and iterate, interfacing with internal systems, making decisions, and escalating when necessary.

But here’s the catch: these agents only work if they’re grounded in deterministic, accurate knowledge and operate within clearly defined guardrails.

While RAG has made significant strides, it has notable limitations, especially as enterprises move toward more complex AI-driven systems. RAG alone can retrieve relevant information but doesn’t evaluate whether that information is procedurally correct, nor does it have a memory of previous steps in multistep processes.

Meeting the Demands of AI Agents

The emergence of AI Agents isn't just a technical evolution; it's a strategic shift demanding a new level of intelligence and control from your foundational AI infrastructure. Without enhanced RAG, enterprises will be fundamentally limited when capitalizing on autonomous AI workflows – from intelligent automation to next-gen decision support. Squirro's approach ensures your RAG implementation is not just ready for today's needs but architected for the agentic AI future.

Conclusion: The Road Ahead for RAG in Enterprise AI

In 2025, RAG isn’t just a trend – it’s a cornerstone of enterprise AI architecture. It’s what enables companies to harness their data assets responsibly, surface insights in real time, and empower every employee with the context they need to excel.

As enterprises navigate an increasingly data-saturated, risk-sensitive world, RAG offers a path to clarity, compliance, and competitive edge.

Those who adopt early – moving beyond pilots and build structured, scalable RAG architectures – will be the ones who lead in the AI-driven decade ahead.

Ready to move your enterprise AI from pilots to production? Download our white paper to learn more about how to advance GenAI beyond RAG, or stay ahead of the curve by subscribing to our newsletter so you don't miss any upcoming articles on the next stepping stones on the journey to agentic AI!