Enterprise Knowledge Management for Artificial Intelligence Market: Building the “Brain” of the Modern Enterprise

The Enterprise Knowledge Management (EKM) for Artificial Intelligence market is rapidly becoming foundational infrastructure for digital organizations. Valued at USD 4.2 billion in 2025 and projected to reach USD 10.45 billion by 2030, the market is expected to grow at a remarkable 20% CAGR from 2026 to 2030. This surge reflects a profound shift: knowledge systems are no longer passive repositories—they are active intelligence layers powering AI-driven decisions, automation, and enterprise workflows.

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From Static Knowledge Bases to Active Intelligence

Traditional knowledge management systems relied on manual tagging and keyword search. Modern AI-enabled platforms instead use machine learning, natural language processing, and neural search to automatically ingest, structure, and retrieve unstructured enterprise data.

These systems function as the context engine for AI, ensuring large language models (LLMs) can access accurate internal information when generating responses. In 2025, organizations are moving these solutions from pilot projects to mission-critical infrastructure because the reliability of AI outputs depends directly on the quality of enterprise knowledge inputs.

Key Market Insights

• 88% of organizations now use AI in at least one business function, though most remain in pilot phases.

• Large enterprises hold 69.2% of market revenue due to their massive data volumes and legacy complexity.

• Natural Language Processing (38.3%) is the leading technology segment powering unstructured data interpretation.

• The BFSI sector leads adoption (26.7%) because of regulatory compliance and risk-management needs.

• Global enterprise data generation reached ~402 million terabytes daily in 2025.

• 80% of Fortune 500 companies now use GenAI-based knowledge retrieval internally.

• AI knowledge tools reduce employee search time by about 40%, boosting productivity and billable output.

Market Drivers

1. Retrieval-Augmented Generation (RAG)

The biggest catalyst is enterprise adoption of RAG architectures, which combine generative AI with real-time proprietary data sources. Companies discovered that generic AI models lack domain context and can hallucinate incorrect answers. Robust knowledge pipelines solve this by supplying verified, up-to-date information directly to AI systems.

2. Rise of Agentic AI

Autonomous software agents capable of performing multi-step tasks require access to fragmented corporate information across emails, databases, chat logs, and cloud storage. This need is driving demand for:

• Neural search platforms

• Knowledge graphs

• Unified data fabrics

Together, these technologies create a single “enterprise memory layer” enabling AI to reason across systems.

Challenges Restraining Growth

Despite rapid expansion, the market faces significant hurdles:

• Data Quality Problems: AI cannot compensate for incomplete or outdated legacy data.

• Privacy and Sovereignty Concerns: Organizations hesitate to store sensitive information in AI-accessible systems.

• Permission Complexity: Ensuring AI respects access controls at granular levels is technically demanding.

These factors can delay deployments and increase implementation costs.

Emerging Opportunities

Autonomous Knowledge Curation

Future systems will automatically maintain corporate knowledge bases—archiving obsolete files, flagging contradictions, and prompting updates from subject-matter experts.

Multi-Modal Knowledge Retrieval

A major untapped opportunity lies in unlocking “dark data” stored in video and audio. Platforms that transcribe, index, and vectorize multimedia content will gain a competitive edge as enterprises increasingly rely on recorded meetings and training sessions.

Governance-First Architectures

Vendors are embedding permission-aware indexing directly into knowledge platforms, ensuring AI systems never expose confidential information to unauthorized users.

Segment Analysis

By Type

• Intelligent Document Processing (IDP): Dominant segment due to enterprise demand for digitizing and structuring documents.

• Vector Databases: Fastest-growing segment because they are the native storage architecture for generative AI.

By Distribution Channel

• Direct B2B Sales: Largest share due to enterprise preference for customized deployments.

• Cloud Marketplaces: Fastest growth, enabling engineers to instantly deploy AI knowledge tools via existing cloud agreements.

By Organization Size

• Large Enterprises: Dominant buyers due to complex data ecosystems.

• SMEs: Fastest growth thanks to SaaS-based AI knowledge solutions lowering entry barriers.

By Application

• Customer Support: Largest use case because AI-powered knowledge bases reduce support tickets.

• R&D: Fastest-growing segment, accelerating discovery by analyzing decades of historical research data.

Regional Landscape

• North America leads with 38.9% market share, driven by strong adoption among technology giants and early enterprise AI deployment.

• Asia-Pacific is the fastest-growing region, fueled by digital transformation initiatives in Japan, South Korea, and China and the massive scale of regional data generation.

Industry Developments

Recent announcements highlight how rapidly the ecosystem is evolving:

• NTT DATA launched a multi-agent “Smart AI Agent” platform for manufacturing and automotive sectors.

• Nomura Research Institute partnered with Microsoft Japan to deploy GenAI knowledge systems across 100 enterprise projects.

• Amazon introduced “DeepFleet,” a generative AI model designed to manage robotic knowledge interactions in logistics environments.

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Competitive Landscape

Leading vendors in this market include:

• OpenText

• ServiceNow

• SAP

• Salesforce

• Atlassian

• Microsoft

• IBM

• Amazon Web Services

• Google

• Coveo

• Lucidworks

• Sinequa

• NICE

• Verint

Competition is intense as hyperscalers and specialized vendors race to build scalable, secure knowledge infrastructures for AI.

Lasting Impact of COVID-19

The pandemic fundamentally reshaped knowledge management priorities. Remote work eliminated informal office knowledge sharing, forcing organizations to digitize institutional knowledge. This shift permanently established knowledge management as a budgeted operational necessity rather than a discretionary IT investment.

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Outlook

The future of enterprise AI hinges on knowledge quality. As companies scale automation, deploy AI agents, and embed copilots into workflows, knowledge platforms will evolve into the central nervous system of the digital enterprise.

Organizations that build robust, governed, and intelligent knowledge layers today will define tomorrow’s competitive advantage.