Published by Intent Amplify, delivering research-driven insights for legal knowledge leaders, AI leaders, risk leaders, architecture teams, legal operations leaders, innovation teams, and professional services decision-makers navigating trusted AI adoption, explainability, traceability, governance, client trust, and regulatory expectations.
Legal and professional services AI has entered its trust, explainability, and governance era.
The first wave of generative AI impressed teams because it could answer quickly. The next wave must earn confidence because it can explain where the answer came from.
That difference matters deeply in legal, accounting, compliance, risk, and professional services environments. A polished answer is not enough. Legal and risk professionals need to know which source was retrieved, whether the user was authorized to access it, why it was selected, how the response was grounded, and whether a human expert can verify the output before it reaches a client, regulator, court, or board.
This is why explainable Agentic RAG is becoming one of the most important legal-grade AI architectures for knowledge-heavy, risk-sensitive organizations.
Retrieval-augmented generation gave organizations a way to connect large language models to enterprise knowledge. Agentic RAG goes further. It adds task planning, retrieval strategy, source evaluation, permission-aware access, workflow logic, quality checks, auditability, and traceability. In simple terms, it helps AI behave less like a black-box text generator and more like a governed knowledge assistant.
The timing is urgent. Thomson Reuters' professional services research found that 40% of organizations now use GenAI, up from 22% the previous year, while 87% of professionals expect GenAI to become central to workflow within five years.¹
Adoption alone does not create trust. Trust depends on explainability, traceability, and governance.
That is the strategic value behind Progress Agentic RAG and its work with a leading European law firm. The firm needed AI-powered answers for legal and accounting research, but it also needed source visibility, governance, auditability, and confidence. Progress Agentic RAG helped the firm build an AI assistant that delivers trusted, traceable answers grounded in internal knowledge and selected authoritative sources.
View the Progress Agentic RAG case study
Key Figures for Legal Knowledge, AI, Risk, and Architecture Leaders
Only 18% of professional services organizations measure AI return on investment, while 40% do not know whether AI ROI is being tracked. Agentic AI adoption remains early, with 15% currently using it, 53% planning or considering it, and 77% expecting it to become central to workflow by 2030. ¹
Legal professionals expect AI to free up nearly 240 hours per year, up from 200 hours in the prior year, creating about $19,000 in annual value per professional. Thomson Reuters estimates AI-related time savings could create $32 billion in combined annual impact across the U.S. legal and tax/accounting sectors.²
Progress reports up to 95% faster AI-readiness and more than 80% cost savings compared with building similar RAG capabilities internally.³
Traditional enterprise search can require employees to open 8-12 documents to answer a single question.⁴
The Progress law firm case study supports approximately 300 legal and accounting professionals. The same firm serves more than 20,000 public and private clients. The firm now handles thousands of legal and accounting questions per month through its AI assistant.⁵
Microsoft analyzed 31,000 workers across 31 countries, and found 81% of leaders expect AI agents to be moderately or extensively integrated into company AI strategy within 12-18 months.⁶
IBM reports AI-enabled workflows are expected to expand from 3% in 2024 to 25% by 2026, an 8x increase.⁷
Google Cloud's grounding framework returns a support score from 0 to 1, supports up to 200 facts, and allows 10,000 characters per fact for grounding checks.⁸
McKinsey estimates agent-enabled process reinvention can reduce time to resolution by 60-90% in selected workflows.⁹
PwC analyzed close to one billion job ads and found that industries more exposed to AI saw 3x higher growth in revenue per worker.¹⁰
Why Explainability Is Becoming the Legal AI Trust Standard
Generating answers is relatively straightforward. Producing answers that are trusted, explainable, and defensible requires stronger controls.
In legal and professional services, the real value is not just speed. It is confidence, source authority, defensibility, and risk control. A lawyer, accountant, consultant, or compliance leader must be able to defend an answer, especially when that answer shapes a client's decision.
Explainable Agentic RAG changes the conversation because it makes the answer inspectable, source-backed, auditable, and reviewable by a professional. It shows the sources. It supports traceability. It gives professionals a path to validate what the AI produced instead of simply accepting a confident paragraph.
That is the missing layer in many enterprise AI pilots.
Organizations do not struggle because AI cannot generate content. They struggle because generated content often lacks operational accountability. Enterprise teams need AI that can connect to real knowledge, understand retrieval context, cite sources, and make quality review part of the workflow.
Progress Agentic RAG is designed for that environment.
Progress Agentic RAG: From Answer Generation to Governed Legal Knowledge Architecture
The Progress case study shows why Agentic RAG is especially relevant for law firms and professional services organizations.
The leading European law firm was not looking for generic AI assistance. It needed a faster and more reliable way to answer complex legal and accounting questions while maintaining regulatory compliance, client confidentiality, and professional oversight.
The firm evaluated internal development and multiple vendor options. The real issue was not whether AI could generate answers. It was whether the firm could trust the output, trace the source, control retrieval behavior, govern access, and improve answer quality over time.
So, Progress Agentic RAG helped the company create a more governed AI helper. This assistant used internal info like legal and accounting expertise, tapped into vetted external sources, and employed targeted search techniques. Instead of returning a long list of documents, the assistant produces answer drafts linked back to source material.
That is the difference between search and explainable AI search.
Explainable AI search supports interpretation, validation, and professional review in addition to information retrieval.
The Client Benefit: Confidence Clients Can Verify
Clients may never ask whether a firm uses Agentic RAG.
But they will notice the results.
They will notice faster responses, clearer explanations, fewer delays, and advice that is supported by visible sources, stronger context, and professional review.
Explainable AI improves the client experience because it reduces uncertainty.
For lawyers, it removes repetitive research friction. For clients, it creates a more responsive advisory relationship. For the firm, it strengthens brand trust because innovation is paired with professional accountability.
That pairing is important.
A law firm that simply says "we use AI" may sound modern. A law firm that says "we use governed, traceable AI to help our professionals deliver faster, source-backed guidance" sounds credible.
The distinction influences how clients evaluate credibility, accountability, and professional confidence.
Why Agentic RAG Needs Governance, Access Control, and Auditability
Agentic AI can plan, retrieve information, reason through multi-step tasks, and act with greater autonomy than traditional generative AI systems. That increases its usefulness, but it also increases the need for stronger governance, access control, monitoring, and human oversight.
Explainable Agentic RAG gives firms a more practical operating model. It does not remove the professional from the decision. It gives the professional better starting material, clearer evidence, and a stronger review path.
For enterprise leaders, this is the shape of scalable AI adoption:
Source grounding.
Citation visibility.
Permission-aware retrieval.
Retrieval control.
Quality monitoring.
Model flexibility.
Human validation.
Auditability.
Access control.
Risk review.
These capabilities form the governance foundation required for enterprise-scale AI adoption.
Why Progress Is Positioned for Explainable, Legal-Grade AI
Progress is well-positioned because its Agentic RAG story is not about AI novelty. It is about enterprise readiness.
The platform focuses on turning unstructured enterprise knowledge into trusted AI answers that professionals can verify. That matters for organizations where information is scattered across documents, portals, repositories, and internal systems.
For law firms, this means research becomes faster without becoming careless.
For clients, this means expertise becomes more accessible without becoming less accountable.
For enterprise brands, this means AI can become part of the service experience without weakening trust.
The next wave of legal AI adoption will not be won by organizations that generate the most answers. It will be won by those who generate answers professionals can trace, verify, govern, and use with confidence.
That is the rise of explainable Agentic RAG.
Download Now to explore how a leading law firm used Progress Agentic RAG to build explainable, traceable AI research experiences with governed retrieval, source visibility, and professional review.
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References
Thomson Reuters (2026) AI in Professional Services Report 2026. Thomson Reuters, 2026.
Thomson Reuters (2025) Future of Professionals Report 2025. Thomson Reuters, 2025.
Progress (n.d.) RAG-as-a-Service Solution. Progress Software Corporation.
Progress (n.d.) From Search to Answers with Agentic RAG. Progress Software Corporation.
Progress (n.d.) Leading Law Firm Agentic RAG Success Story. Progress Software Corporation.
Microsoft (2025) 2025 Work Trend Index Annual Report. Microsoft Corporation, 2025.
IBM (2025). From AI Projects to Profits. IBM Corporation, 2025.
Google Cloud (n.d.) Check Grounding with RAG. Google Cloud.
McKinsey & Company (2025) Seizing the Agentic AI Advantage. McKinsey & Company, 2025.
PwC (2025) 2025 Global AI Jobs Barometer. PricewaterhouseCoopers (PwC), 2025.
