There’s a blind spot in most boardrooms today, and it’s costing enterprises their competitive edge in the AI era. Despite decades of digital transformation, most organizations still fundamentally misunderstand the strategic value of business knowledge.

The Knowledge Paradox

Walk into any Fortune 500 company and ask who owns customer data. You’ll get a clear answer—likely the Chief Data Officer or Chief Analytics Officer. Ask about transaction data, employee records, or financial information. Again, clear ownership, robust governance, sophisticated management systems.

Now ask who owns business knowledge—the procedures, policies, best practices, and operational expertise that actually drive how work gets done. You’ll likely be met with blank stares or finger-pointing across silos.

This isn’t just an organizational oversight. It reflects a fundamental misunderstanding of what knowledge represents in the modern enterprise.

How We Got Here: The Great Knowledge Undervaluation

Historically, enterprises have fallen into two traps when it comes to business knowledge:

Trap #1: Treating Knowledge as Low-Value Content

Because knowledge is less structured than traditional data, companies have relegated it to second-class status. Customer data gets C-suite attention and million-dollar investments. Business knowledge gets a SharePoint site and a junior content manager.

Trap #2: Treating Knowledge as Compliance Overhead

When organizations do pay attention to knowledge, it’s often purely defensive—policies to control, procedures to audit, documentation to satisfy regulators. Knowledge becomes a cost center focused on risk mitigation, not a strategic asset for value creation.

The result? Critical business knowledge ends up fragmented across departmental silos—customer service has their knowledge base, sales has their playbooks, marketing has their guidelines. Each function creates, manages, and hoards their own content, leading to duplication, inconsistency, and massive inefficiency. Even worse, this knowledge rarely drives actual operations in systematic ways.

The AI Inflection Point: Why Everything Just Changed

The explosion of generative AI and agentic systems has fundamentally altered the economics and strategic importance of business knowledge.

Here’s the inconvenient truth: AI is only as good as the knowledge you feed it.

Want your AI agents to deliver accurate, consistent, compliant customer experiences? They need trusted knowledge. Want to automate complex business processes? You need well-structured procedural knowledge. Want to scale expertise across your organization? You need to capture and systematize the knowledge in your experts’ heads.

The quality of your knowledge directly determines the quality of your AI outcomes. Garbage in, garbage out—but now at the speed and scale of automation.

This means business knowledge has suddenly become your most critical AI fuel. Yet most organizations are trying to power their AI future with knowledge management practices designed for the 1990s intranet era.

The Opportunity: Knowledge as Competitive Advantage

Here’s what forward-thinking leaders are beginning to recognize: business knowledge can be a far more defensible source of competitive advantage than most other assets.

Why? Because knowledge that captures your unique business processes, hard-won expertise, and operational best practices is:

• Difficult to replicate – Your competitors can’t simply buy it or copy it
• Continuously evolving – With AI, you can capture expert knowledge and keep it fresh in ways that were never possible before
• Multiplicative in value – When leveraged by AI, knowledge scales across every operation, every customer interaction, every decision point

The companies that figure out how to systematically capture, curate, and deploy their business knowledge through AI will outperform those that continue to treat it as an afterthought.

The Leadership Gap: Where’s Your Chief Knowledge Officer?

Here’s a question every board should be asking: Who on your executive team is responsible for business knowledge?

Almost every large enterprise now has a Chief Data Officer or Chief Analytics Officer. Many have Chief Digital Officers. Some are adding Chief AI Officers.

But how many have a Chief Knowledge Officer? Or even a C-suite executive with knowledge management clearly in their remit?

This gap is stunning when you consider that knowledge—not data—is the real bottleneck to AI effectiveness. You can have pristine data warehouses and cutting-edge AI models, but without trusted, well-managed knowledge to guide them, you’ll automate your way to inconsistent, unreliable, or even dangerous outcomes.

The CEO Imperative: Three Questions to Ask Tomorrow

If you’re a CEO or board member, here are the questions you should be asking your executive team this week:

1. Who owns business knowledge in our organization? Not content management, not document control—actual strategic ownership of knowledge as a corporate asset.
2. How are we capturing and systematizing the expertise in our people’s heads? With AI tools, this is now technically feasible at scale. Are you doing it?
3. How is our knowledge architecture enabling or limiting our AI ambitions? If you’re investing millions in AI but haven’t modernized your approach to knowledge management, you’re building on quicksand.

The Path Forward

The organizations that will win in the AI era will be those that recognize this moment for what it is: a fundamental revaluation of business knowledge from operational overhead to strategic asset.

This starts with leadership. CEOs and boards must elevate knowledge stewardship to a strategic priority, with clear executive ownership, appropriate investment, and integration into AI initiatives from day one.

The opportunity is enormous. The window won’t stay open forever. Your competitors are likely just as blind to this as you’ve been—but the first movers who crack the code on knowledge-powered AI will build advantages that are very difficult to overcome.

The question isn’t whether to treat business knowledge as invaluable IP. The question is whether you’ll recognize it before or after your competition does.

Ashu Roy is CEO of eGain, a leader in AI knowledge solutions for customer experience and enterprise automation.

As organizations rush to implement AI agents and chatbots, many are learning a hard truth: AI is only as good as the source knowledge that powers it. This evolution makes the quality of underlying content mission-critical.

When Knowledge Fails, AI Fails

The promise of AI-powered customer service quickly turns into a liability when the knowledge foundation is weak. Customers receive inconsistent answers, AI systems provide outdated information, or bots confidently deliver incorrect guidance that creates compliance risks.

Real-world failures illustrate the problem: A UK shipping company’s chatbot showed a customer photo evidence of their package delivered to the wrong address with no option to escalate, while another shipping company had to disable its AI chatbot after it used profanity with users.

These failures share a common root cause: insufficient knowledge management. Gartner found that 61% of customer service leaders have a backlog of articles to edit, and over one-third have no formal process for revising outdated content—yet many are deploying conversational AI that depends entirely on this content.

The Pillars of a Strong Knowledge Foundation

What enterprise knowledge features separate successful AI implementations from failures? Five critical characteristics:

Unified Content Management: Content scattered across SharePoint, CRM systems, and legacy platforms produces inconsistent AI results. A unified approach creates a single source of truth.

Intelligent Content Synthesis: Modern knowledge systems synthesize content from multiple sources, apply reasoning to match context with solutions, and deliver precise answers.

AI-Assisted Content Creation: Advanced systems leverage generative AI to accelerate content creation while maintaining editorial workflows for consistency, quality, and compliance.

Personalized Delivery: Knowledge systems should adapt content based on factors like user role, experience level, region, language, and interaction context.

Comprehensive Delivery: Making knowledge available everywhere requires the ability to connect the knowledge base to all channels and platforms.

The eGain AI Knowledge Hub^TM: Knowledge Management Built for AI

The eGain AI Knowledge Hub delivers on these principles with a comprehensive platform designed for AI-driven customer service at scale.

The AI Knowledge Hub unifies disparate content through pre-built connectors to existing content repositories like Sharepoint, Confluence, Box, OneDrive and other platforms.

Hybrid AI delivers detailed answers needed for important step-by-step processes and captures contextual information to guide users through question-and-answer sessions, matching customer situations with relevant solutions for both self-service and agent-assisted interactions.

AssistGPT automates ongoing knowledge management tasks while maintaining business controls and compliance. Authors create multilingual content through an intuitive console with flexible editorial workflows balancing speed and quality.

Personalization adapts delivery to each user—concise information for experienced agents, detailed guidance for newcomers—automatically adjusting for role, region, language, and customer interaction. The AI Knowledge Hub delivers trusted answers across all touchpoints while providing analytics to continuously improve knowledge effectiveness.

AI Knowledge Hub integrations deliver unified knowledge content and trusted answers to any customer experience platform, including: Salesforce, Microsoft Dynamics, ServiceNow, SAP, Genesys, Amazon Connect, and Cisco Webex, allowing organizations to maintain existing systems while presenting unified knowledge to AI agents and human advisors.

Real-World Transformation: The Proof in Performance

eGain customers across industries have achieved remarkable outcomes:

Telecommunications: A major telecom provider serving 10,000+ agents and 600 retail stores improved First Contact Resolution by 37%, increased Net Promoter Score by 30 points, and doubled agent time-to-competency while cutting training time by 50%. The guided help capability enabled any agent to handle any call.

Financial Services: A global bank improved FCR by 36% while reducing Average Handle Time by 67% and cutting onboarding time by 40%. They climbed from #3 to #1 in NPS rankings across 11 countries.

Government: A government agency serving 25 million citizens deflected 70% of calls to AI-powered virtual assistance, reduced case handling time by 25%, and boosted agent engagement to 92% versus a 67% industry benchmark. Their Forrester CX Index position improved 33% in one year.

Healthcare: A health insurance firm reduced training time by 33% for 2,000+ remote agents during COVID while achieving all 30 operational objectives and reaching the top 5 in Forrester’s CX Index.

Manufacturing & Utilities: Companies saved millions annually—one utilities firm saved $5M by reducing unnecessary engineer callouts while improving FCR by 30%.

Technology: A fast-growing SaaS company boosted agent confidence by 60% and self-service utilization by 30%, contributing to improved profit margins over three consecutive years.

The Path Forward

More than 75% of customer service leaders feel pressure from executives to implement AI. But success requires establishing a solid knowledge foundation first.

As customers increasingly leverage AI-powered agents to manage service requests, organizations must embrace automation as the dominant strategy. Those that invest in unified, AI-optimized knowledge management today will capitalize on the AI revolution. Those that don’t risk failed implementations, damaged customer trust, and missed opportunities.

The question isn’t whether AI will transform customer service—with 85% of leaders exploring AI chatbots—the question is whether your knowledge foundation is ready. For organizations investing in comprehensive knowledge management, the rewards are clear: dramatic improvements in customer satisfaction, operational efficiency, and business outcomes that extend far beyond the contact center.

The AI revolution in customer service is here. The winners will be those who recognize that exceptional AI ROI starts with exceptional knowledge.

Research Spotlight: This article draws on findings from “The Great Retirement: Knowledge Loss, AI and The Workforce Shift,” a survey of 1,000 global organizations conducted by APQC in partnership with eGain in August 2025.

While 85% of C-suite leaders and 81% of business unit leaders recognize knowledge loss as at least a moderate concern, awareness hasn’t translated to action. Only 30% of organizations consistently capture knowledge from departing employees, and 41% rarely or never attempt it at all.

The Hidden Costs of Inaction

When critical information about equipment maintenance, production processes, quality standards, and troubleshooting procedures exists only in employees’ heads or scattered across disconnected systems, manufacturers face severe consequences. Production lines slow when experienced operators retire. Quality varies across shifts when standardized procedures aren’t accessible. Compliance risks escalate when documentation is fragmented. New hires take months to reach competency, reducing workforce agility. Customer satisfaction plummets when service teams can’t access product specifications and troubleshooting guides.

APQC research reveals why knowledge capture fails despite widespread recognition of the problem:

• 52% cite lack of time
• 45% report insufficient resources
• 38% say it’s not a priority
• 35% point to unsupportive culture

The result? Decades of manufacturing expertise simply walks out the door.

The AI Opportunity Gap

Despite these challenges, a massive opportunity exists. While 79% of organizations express interest in AI technology for capturing departing expertise, only 21% actually leverage AI automation to mine conversations and capture knowledge. Most still rely on manual methods: 74% use people-to-people transfer, 62% manually document knowledge, and 48% conduct exit interviews.

This 58-point gap between interest and implementation represents a competitive advantage for manufacturers who act decisively.

What organizations want from AI-powered knowledge management:

• 41% aim to reduce knowledge management cycle-time
• 36% want better decision-making
• 29% seek to streamline processes
• 28% focus on internal customer experience

Current AI deployment shows untapped potential:

• 61% use AI for discovery (finding knowledge)
• 41% for analytics (insights)
• 40% for curation (organizing)
• 33% for creation (generating content)
• Only 24% for delivery (publishing)

Proven Returns: Real Manufacturing Outcomes

Forward-thinking manufacturers implementing eGain’s AI-powered knowledge platform are achieving remarkable results. One manufacturer migrated 5,000+ content articles and 6,000 SharePoint documents into a unified AI-driven knowledge base. A dental equipment manufacturer doubled dealer self-service adoption while boosting sales. A pioneering e-bike company deployed knowledge across 21 languages, improving self-service search success by 85% and increasing conversational AI usage by 18X.

The measurable outcomes are compelling:

• Up to 90% deflection of service requests to digital self-service
• 36% improvement in First Contact Resolution
• 40% reduction in training time
• 33% faster competency for complex queries
• 50% reduction in agent time-to-competency
• 70% of calls deflected to AI-powered virtual assistance
• 25% reduction in case handling time

The eGain Solution: Three Integrated Pillars

Understanding that 46% of organizations worry about AI accuracy, 38% about data privacy, and 36% about compliance, eGain has built a platform specifically designed to address these concerns while delivering transformational value.

1. AI Knowledge Hub^TM: The Unified Foundation

The AI Knowledge Hub eliminates content silos by creating a single source of truth that unifies SharePoint sites, CRM systems, product documentation, and legacy systems. It combines AI reasoning, machine learning, natural language processing, and analytics to deliver knowledge across all touchpoints—from shop floor tablets to customer service portals. Advanced capabilities include federated search, conversation guidance, and role-based access ensuring operators, technicians, and service representatives get precisely the knowledge they need.

2. AI Agent: Expert-Level Performance for Everyone

AI Agent monitors conversations and interactions in real-time, establishing intent and presenting guided knowledge in the flow of work. This transforms variable agent performance, enabling all representatives to assist like experts. Extending beyond customer service to production support and field service, AI Agent has helped organizations deflect 70% of calls to virtual assistance and reduce case handling time by 25%.

3. Composer: Enterprise-Grade Integration

Recently launched, eGain Composer^TM provides a modular platform for developers with robust APIs, Model Context Protocol servers, and SDKs in Python and TypeScript. This composable architecture enables seamless integration into ERP systems, MES platforms, and IoT dashboards—critical for manufacturers with complex technology stacks and custom applications.  Composer allows manufacturers to deploy eGain’s AI Knowledge Hub and AI Agents in the specific workflows they want, configured for their specific tech stack.

The Bottom Line

The data is unambiguous: 51% workforce turnover within five years, 85% C-suite concern, but only 30% consistent knowledge capture. The top barriers—time (52%), resources (45%), and prioritization (38%)—are organizational choices, not technological limitations.

AI-powered platforms provide the solution. Organizations using eGain have achieved 90% self-service deflection, 36% FCR improvement, and 50% faster time-to-competency. These aren’t projections—they’re proven outcomes.

The competitive advantage belongs to manufacturers who act now. With 79% interested but only 21% implementing AI-enabled knowledge capture, the window is open. But with the Great Retirement accelerating, that window is closing rapidly. Organizations that act today will preserve institutional knowledge, accelerate workforce development, and build sustainable competitive advantages—while their competitors watch expertise disappear.

The expertise is walking out. The technology is ready. The ROI is proven. The question isn’t whether to invest in knowledge management—it’s whether you can afford not to.

Explore how eGain can transform your manufacturing operation through the Innovation in 30 Days program—a no-cost production pilot demonstrating real-world value. Download the full APQC research report to learn more about “The Great Retirement” findings.

The Knowledge Crisis in Telecommunications

The telecommunications industry faces unique customer experience challenges, particularly when companies operate with fragmented knowledge bases following mergers or rapid growth. Agents struggle to find accurate information, leading to customers receiving different answers depending on who they speak to, driving millions of repeat contacts and escalating service costs.

Consider the typical scenario: a customer calls about a complex billing issue involving bundled services, device financing, and network troubleshooting. The agent must navigate multiple systems, search through disparate knowledge repositories, and somehow deliver a coherent answer while the customer waits on hold. According to industry research, 65% of agents say finding answers to customer queries is their biggest issue, and 84% of agents hate their desktop tools. This isn’t just an agent productivity problem—it’s a customer experience crisis that directly impacts Net Promoter Scores, first contact resolution rates, and ultimately, customer churn.

Without a robust knowledge management system, telecommunications companies face several critical challenges:

Inconsistent Customer Service: When knowledge is scattered across legacy systems, SharePoint sites, product documentation, and departmental silos, every agent becomes an island. New agents lack the expertise of tenured representatives, leading to wildly inconsistent customer experiences. One major UK telecommunications provider found that after merging multiple brands, they had four separate knowledge bases serving 10,000 contact center agents handling over 3.5 million calls per month, resulting in 19 million instances where customers had to call back to resolve their issues.

Prolonged Agent Training: The complexity of telecommunications products and services means new agents can take months to reach full productivity. Without guided, contextual knowledge delivery, training programs become expensive, time-consuming burdens that struggle to keep pace with product launches and policy changes.

Low First Contact Resolution: When agents can’t quickly access accurate information, they escalate cases, transfer calls, or provide incomplete solutions. This drives up operational costs while destroying customer satisfaction.

Compliance Risks: Telecommunications is a heavily regulated industry. Without controlled, compliant knowledge pathways, agents may inadvertently violate regulations or fail to follow required procedures, exposing companies to penalties and legal risks.

The eGain Solution: Knowledge-Powered Customer Engagement

eGain addresses these challenges through an integrated platform built on three foundational capabilities: the AI Knowledge Hub^TM, AI Agents, and the Conversation Hub^TM. Together, these components transform how telecommunications companies manage knowledge and deliver customer experiences.

AI Knowledge Hub: The Foundation of Trusted Knowledge

The eGain AI Knowledge Hub connects fragmented content silos across SharePoint sites, CRM knowledge bases, product documentation, policies, intranets, and legacy systems into a unified knowledge foundation. Rather than forcing agents to search multiple systems, the Knowledge Hub federates content and delivers contextually relevant answers directly within the agent’s workflow.

What sets eGain’s approach apart is its hybrid AI architecture. The platform combines probabilistic reasoning from large language models for natural conversation with deterministic reasoning for specific, multi-step workflows where precision is critical—especially important in compliance and high-risk use cases common in telecommunications.

The AI Knowledge Hub personalizes content and guidance tailored to the interaction context, the agent’s role and experience level, region, and language, recognizing that tenured agents need less step-by-step guidance than novice agents. This intelligent personalization accelerates time-to-competence while ensuring consistent, compliant service delivery.

For telecommunications companies, the impact is measurable. BT Consumer transformed omnichannel customer service for over 20 million customers with eGain’s AI Knowledge Hub, improving NPS by 20 points and reducing service costs across tens of thousands of agents and store associates. The solution distilled 20,000 articles into AI-powered process guidance flows that provide agents with guided help across almost unlimited scenarios.

AI Agents: Automating with Assured Actions^TM

eGain AI Agent 2 delivers what the company calls Assured Actions—a breakthrough approach that addresses the reliability and consistency challenges many organizations face when deploying AI agents for customer experience. Unlike chatbots that frustrate customers with limited capabilities, eGain’s AI Agents leverage the trusted knowledge foundation to deliver accurate, consistent responses across channels.

The platform’s unique quality assurance mechanism, PrismEval^TM Service, continuously optimizes the match between the knowledge base and AI-generated answers, reducing the risk of hallucinations or inaccurate guidance—a critical requirement for telecommunications companies where incorrect information can lead to service outages, billing errors, or compliance violations.

Conversation Hub: Omnichannel Engagement

The eGain Conversation Hub enables telecommunications companies to manage customer conversations across messaging apps, chat, email, social media, and voice channels from a unified platform. This omnichannel capability ensures knowledge consistency whether a customer reaches out via Twitter, web chat, or phone call.

The Conversation Hub integrates seamlessly with the AI Knowledge Hub, providing agents with contextual guidance and relevant knowledge articles as conversations unfold. This integration means agents spend less time searching and more time solving customer problems.

Seamless Integration with Existing Platforms

One of eGain’s most compelling advantages is its ability to work alongside existing customer experience infrastructure. Telecommunications companies have made significant investments in platforms like Genesys and Salesforce, and eGain doesn’t require ripping and replacing these systems.

The eGain Knowledge Hub for Genesys is available through the Genesys AppFoundry and embeds directly into the Genesys desktop, delivering personalized answers and conversational guidance during customer conversations while slashing training needs and ensuring compliance. Similarly, eGain Knowledge Hub for Salesforce Service Cloud is embedded directly in the Salesforce workspace and is rated number one by analysts and clients.

These pre-built integrations enable bidirectional data flow. Agents on eGain’s digital-first desktop can view customer profiles and context from Genesys or Salesforce, while omnichannel interactions are recorded end-to-end in both systems for a complete 360-degree customer view.

Proven Results in Telecommunications

The statistics from eGain telecommunications customers speak for themselves. Companies have achieved:

• Up to 50% reduction in agent training time
• Up to 37% boost in First Contact Resolution
• NPS improvements of up to 30 points
• Up to 60% deflection of agent-assisted service requests through improved self-service

One telecommunications company unified 15 global contact centers with eGain, achieving 95% successful searches and streamlined service using AI-powered knowledge. Another utility company using eGain reported a 5X improvement in knowledge building speed and 6X improvement in finding answers, reaching 98% success in helping agents locate the right information quickly.

The Path Forward

For telecommunications companies struggling with fragmented knowledge and inconsistent customer experiences, the path forward is clear. By implementing a comprehensive knowledge management platform like eGain—with its AI Knowledge Hub as the foundation, AI Agents for intelligent automation, and Conversation Hub for omnichannel engagement—telecom providers can transform agent productivity, accelerate training, improve customer satisfaction, and reduce operational costs.

The platform’s ability to integrate seamlessly with existing systems like Genesys and Salesforce means companies can achieve these benefits without disrupting current operations. In an industry where customer experience is increasingly the primary competitive differentiator, investing in knowledge-powered engagement isn’t just a technology decision—it’s a strategic imperative for survival and growth.

eGain’s AI Knowledge Hub and AI Agent solutions create transformational value—purpose-built to address the external-facing intelligence layer completing the CPG AI ecosystem. While internal generative AI tools provide employees access to company knowledge, eGain specializes in connecting customer intelligence, operational expertise, compliance requirements, quality standards, and market insights into a unified platform serving every critical function.

Elevating Brand Management Through Connected Customer Intelligence

Leading CPG brands engage billions of consumers globally, yet customer insights remain fragmented across service logs, social monitoring, sales reports, and research databases. P&G’s AI Factory deploys machine learning models 10x faster, while Coca-Cola’s AI-driven analytics power personalized marketing across 100+ markets. Yet as P&G CIO Vittorio Cretella notes, “The true impact of AI can only be felt when it’s used pervasively across the entire organization.”

eGain’s platform extends these capabilities by specializing in the external-facing ecosystem where brands truly live. Brand managers can gain real-time 360-degree views synthesizing all customer touchpoints—service interactions, social signals, product feedback, competitive intelligence. When quality issues emerge, trends are be identified within hours, matching the speed Nestlé achieved reducing product ideation from six months to six weeks through AI-powered consumer insights.

eGain’s AI Agents handle routine inquiries autonomously while capturing insights flowing back to brand management, creating closed loops between customer experience and strategy. Leading companies report 60-70% faster time-to-insight through eGain’s unified knowledge platforms.

Powering Operational Excellence with Intelligent Knowledge Flow

CPG supply chains represent extraordinary complexity. Unilever achieved 6 consecutive years as Gartner Supply Chain Master and 98% on-shelf availability through AI-driven customer connectivity. P&G’s Supply Chain 3.0 earned 11 consecutive years of recognition. Yet operational knowledge challenges persist when operators must navigate multiple systems for solutions.

eGain complements these advanced systems by connecting manufacturing knowledge, supplier data, logistics information, and historical resolutions. Quality variances can automatically trigger relevant specifications, past solutions, and expert contacts. For organizations like Unilever, which improved ice cream supply chain forecast accuracy by 10% through AI-enabled weather analysis, eGain can extend this precision to the knowledge layer – supporting 35 factories across 60 countries.

On eGain’s platform continuously learning systems capture every resolved incident and create self-improving operational intelligence. Organizations leveraging eGain’s solutions report 40-50% reductions in resolution times through better knowledge access.

Democratizing HR Expertise Across Global Operations

CPG companies managing 100,000+ employees globally face HR complexity spanning dozens of jurisdictions and thousands of policy permutations. Nestlé made NesGPT part of standard onboarding across sales, innovation, marketing, and legal functions. P&G requires all 107,000 employees to complete governance training before AI tool access.

eGain’s AI Agents can provide specialized HR knowledge orchestration complementing internal tools. Employees can receive precise, personalized answers accounting for location, role, tenure, and benefits—interpreting policy contextually rather than just retrieving documents. The system ensures governance through access tracking and flag interpretation issues before they become problems.

Organizations implementing AI-powered HR knowledge systems report 60-70% reductions in tier-1 inquiries while improving satisfaction—enabling focus on building “empowered, agile, accountable organizations” CPG leaders identify as essential for competitive advantage.

Transforming Quality Assurance from Reactive to Predictive

Quality failures can trigger recalls, damage brands, and create regulatory consequences. P&G’s Microsoft partnership targets “predictive quality”—real-time checks on production lines. Unilever uses AI and IoT to reduce machine cleaning times by 20% and cut utility use by 10%. Yet QA knowledge remains fragmented across systems.

A unified quality intelligence platform leveraging eGain’s AI knowledge management platform can connect the entire ecosystem. QA teams immediately access specifications, protocols, supplier history, past incidents, and regulatory requirements—contextualized to specific situations. Systems proactively surface emerging risk patterns before reaching customers. When one facility resolves a challenge, that knowledge becomes immediately available enterprise-wide.

This supports objectives like P&G’s Supply Chain 3.0 vision of “end-to-end synchronized, sustainable, and resilient supply chain, amplified by data and analytics,” or Unilever’s 99.995% IT uptime across 3 billion weekly transactions.

Accelerating Marketing Research Impact

CPG marketing research teams generate valuable insights, but ROI depends on making them discoverable and actionable. Nestlé’s ChatGPT-powered innovation tool analyzes market trends and consumer data from 20+ brands, generating product concepts in minutes. Coca-Cola’s AI analyzes millions of touchpoints to deliver hyper-personalized content. Yet for companies operating across 120+ countries, research fragmentation represents massive lost opportunity.

eGain enables intelligent surfacing where research findings, consumer panels, competitive analyses, and trends surface through natural language queries. Brand managers can instantly access relevant research, competitive studies, and historical launch data—synthesized and contextualized. Organizations leveraging AI-powered knowledge platforms like eGain’s report 50-60% increases in research utilization.

Enabling Enterprise Intelligence with IT Governance

CPG IT organizations balance accessibility demands against security and compliance. P&G built ChatPG with safeguards ensuring prompts wouldn’t train external models. Nestlé requires governance training for NesGPT’s 80,000+ users. Coca-Cola’s $1.1 billion Microsoft partnership emphasizes secure, compliant AI deployment.

eGain resolves the accessibility-versus-governance paradox—the balance organizations seek with multi-billion-dollar ICT investments. The platform integrates with existing systems without replacement. IT can define controls once; the eGain AI-Knowledge Hub enforces them consistently.

Completing the Connected Intelligence Vision

CPG leaders have made substantial progress: internal AI tools serving tens of thousands, AI Factories deploying models faster, supply chains earning consecutive industry recognition. Yet the critical knowledge gap remains.

eGain’s platform orchestration addresses the external-facing intelligence layer—customer interactions, support conversations, market signals, competitive intelligence—connecting it to internal operations. A customer service agent handling a product query can instantly access similar historical queries, quality specifications, recent manufacturing changes, related feedback patterns, and marketing positioning—all contextualized to deliver optimal experience while capturing insights flowing back to brand management, R&D, and operations.

eGain’s AI Agents autonomously handle tier-1 inquiries while intelligently escalating complex issues with full context, ensuring seamless handoffs. This creates the “one supply chain” integration Unilever achieved with retailers—extended to the knowledge domain, connecting every customer touchpoint with every operational function.

For CPG leaders pursuing “agentic AI and autonomous operations,” this represents a fundamental capability advantage: learning faster, adapting more quickly, leveraging institutional knowledge strategically. The philosophy of combining decades of expertise with startup agility requires knowledge systems amplifying both—preserving institutional wisdom while enabling rapid experimentation.

In markets where competitive advantage depends on organizational agility and customer insight, AI-powered knowledge orchestration isn’t optional—it’s the foundation for sustained excellence. For companies ready to extend AI initiatives from internal efficiency tools to comprehensive intelligence platforms connecting every touchpoint, function, and data source, eGain’s AI Knowledge Hub and AI Agent solutions offer the proven architecture to complete their transformation vision.

A patient calls their insurance carrier asking if they’re covered for an ordered procedure. The representative checks their system and answers. Days later, a different agent provides contradictory information. This isn’t hypothetical—65% of patients report receiving inconsistent answers from the same healthcare organization, a terrifying statistic when those answers involve life-and-death decisions.

For hospitals, medical centers, and other providers, fragmented knowledge management isn’t just inefficient—it’s a patient safety risk, compliance liability, and financial drain affecting every department from the ED to billing.

The Staggering Cost of Information Chaos

76% of healthcare employees spend over 30% of their workday searching for information they need to do their jobs. For a 3,000-employee hospital, that’s 900 full-time equivalents doing nothing but hunting for answers. Contact center agents place patients on hold. Nurses interrupt physicians mid-round. Administrative staff email multiple departments for billing codes. Every minute spent searching is a minute not spent on patient care.

Medical knowledge doubles every 73 days, making it impossible for practitioners to stay current without robust knowledge systems. Yet many organizations rely on scattered information: outdated protocol binders, individual notes, disparate systems that don’t communicate, and informal knowledge sharing.

Compliance violations carry severe penalties. Over 176 million patients have been affected by PHI breaches—most from employee negligence rather than hacking. HIPAA violations can cost up to $1.5 million per category annually. When staff can’t easily find current privacy protocols or regulatory requirements, violations become inevitable.

What Healthcare Needs: eGain’s Knowledge Management Solution

eGain’s AI Knowledge Hub^TM addresses healthcare’s unique challenges with purpose-built capabilities:

Unified Knowledge Foundation – All content flows from one authoritative source. When a billing policy changes, it updates everywhere instantly—patient portal, contact center, registration workflow, self-service FAQ. No version control nightmares or contradictory information.

AI-Powered Search – Staff shouldn’t need to know where information lives. eGain’s AI understands natural language queries and synthesizes answers from multiple sources. A query about “Medicare Advantage PT coverage for hip replacement” might span three different documents—intelligent search delivers one complete answer.

Conversational Guidance – Rather than expecting staff to memorize complex processes, eGain walks them through step-by-step, asking relevant questions and delivering next-best-action recommendations based on responses.

Omnichannel Consistency – Knowledge remains consistent whether accessed by patients through self-service portals, mobile apps, or contact center agents. Complete context follows patients across touchpoints.

Compliance Built-In – Role-based access, complete audit trails, version control, and approved-content-only mechanisms ensure HIPAA compliance and regulatory readiness.

Integration – Pre-built connectors link to EHRs, billing systems, and contact center platforms, delivering contextual knowledge without requiring staff to leave their primary systems.

Proven Results From eGain Healthcare Customers

Dramatic Training Time Reduction

A premier health insurance organization serving millions of members consolidated 17 separate legacy systems into eGain’s Knowledge Hub. The impact was immediate: 33% reduction in agent training time for complex health insurance queries—even as 2,000+ agents transitioned to remote work overnight during COVID-19. The organization met all 30 business objectives, including substantial reductions in Average Handle Time and improvements in First Contact Resolution. Knowledge-powered service earned them recognition among the top 5 health insurers in the Forrester US CX Index for 2020 and 2021.

Exceptional Patient Experience at Scale

A large government healthcare agency serving 25 million patients experienced “phenomenal success” with eGain AI Knowledge Hub. The platform empowers 128,000 contact center agents with consistent, accurate, compliant information and guided processes. Their Forrester CX Index position improved by 33% in one year—remarkable for an organization of that scale.

Search Accuracy That Transforms Operations

One leading health plan consolidated knowledge systems into eGain, serving 2,000 agent users with over 1,000 knowledge articles. Search accuracy increased to 96% in less than a year—agents found the right answer on the first try virtually every time. This directly impacts escalations, handle time, first contact resolution, and compliance risk.

Proactive Patient Engagement

A national retail healthcare chain leverages eGain’s knowledge-powered notifications to serve over 100 million consumers with proactive communications across SMS, email, and voice about prescriptions, appointments, preventive care, and test results. This reduces inbound inquiries, improves medication adherence, and demonstrates active engagement in patient wellbeing.

The Business Case

For healthcare CFOs evaluating knowledge management investments, eGain delivers compelling ROI:

Direct Cost Reduction

• 30-50% reduction in training time
• Lower contact center costs through self-service deflection
• Decreased supervisor time answering staff questions
• Reduced compliance penalties and remediation costs
• Lower claim denial rates through accurate information at point of service

Revenue Protection

• Improved patient satisfaction and ratings
• Reduced patient leakage from administrative frustration
• Better authorization and eligibility verification
• Increased staff capacity without proportional headcount increases

Strategic Value

• Faster implementation of new service lines
• Ability to scale operations without linear cost increases
• Quick, consistent response to regulatory changes
• Better data for quality improvement initiatives

Research shows organizations implementing comprehensive knowledge management see 15-30% improvement in agent productivity, 28% reduction in average handle time, 40-60% reduction in training time, and 30-40% improvement in first contact resolution.

Time to Act

Healthcare organizations cannot afford fragmented knowledge and the chaos it creates. The risks to patient safety, compliance, operational efficiency, and patient experience are too great. Competitive pressures from high-performing health systems, retail healthcare disruptors, and rising patient expectations are too intense.

In healthcare, knowledge is infrastructure—as critical as the EHR or the facility itself. eGain’s healthcare customers—from major health insurance carriers to large government agencies to integrated retail chains —report measurable improvements in patient outcomes, staff experience, financial performance, and regulatory compliance.

Organizations can achieve measurable results with eGain in as little as 30 days through no-cost, properly scoped pilots that demonstrate value before full implementation. The path from fragmented knowledge to unified intelligence is clear—and organizations making this journey with eGain are seeing competitive advantage.

The only question is: how much longer can your healthcare organization afford to operate in knowledge chaos?

This is why the MIT study “The GenAI Divide: State of AI in Business 2025” reveals such a sobering reality: despite $30-40 billion in enterprise AI investment, 95% of organizations are getting zero return. The root cause isn’t the AI technology itself—it’s the foundation on which that AI is built. Organizations are layering sophisticated AI on top of fragmented, inconsistent, and ungoverned knowledge bases, then wondering why their AI initiatives fail to deliver value.

In financial services, where regulatory compliance and customer trust are paramount, the stakes are even higher. AI amplifies whatever you feed it: if you input fragmented knowledge, you get amplified confusion; if you input compliant, unified, trusted knowledge, you get amplified business value. Here are the essential requirements for AI initiatives that actually deliver results in financial services—starting with the knowledge foundation that makes everything else possible.

1. The Trusted Knowledge^TM Foundation: Unified, Intelligent, and Compliant

Financial institutions face a unique challenge: they need AI that’s both powerful and precise, flexible yet compliant. The answer lies in three interconnected capabilities that work together to create truly Trusted Knowledge^TM.

Unified Knowledge Foundation for Compliance

Financial services operate under strict regulatory frameworks where inconsistent or inaccurate information can result in devastating fines and reputational damage. The MIT study emphasizes that “AI is only as good as its input”—organizations need a single source of truth that ensures all AI responses are accurate, compliant, and consistent.

Hybrid AI Architecture That Ensures Accuracy

Pure generative AI systems can hallucinate or provide inconsistent responses—a critical risk in regulated industries. Financial institutions need a hybrid approach that combines the power of GenAI with deterministic, rules-based systems to ensure detailed policies and procedures are provided correctly every time they’re needed.

Compliance-First Architecture

Financial services face unique regulatory requirements including PCI DSS, SOX, GDPR, and industry-specific regulations. AI systems must embed compliance controls directly into their architecture rather than treating compliance as an afterthought.

How eGain Delivers: eGain’s AI Knowledge Hub^TM platform creates a unified hub that consolidates all compliance-critical information from across the enterprise, then applies Hybrid AI architecture to orchestrate multiple AI technologies—generative AI, conversational AI, machine learning, and case-based reasoning—with curated knowledge assets. When compliance-critical information is required, the system delivers exact policies and procedures from verified sources rather than generated approximations. The platform knows when to use GenAI for flexibility and when to deliver precise, deterministic content for regulatory requirements.

Built with compliance standards including PCI, NIST SP 800-53, HIPAA, and FedRAMP, eGain embeds policy and regulatory checks directly into day-to-day workflows with AI-powered version control, audit trails, and approval workflows. This ensures frontline staff deliver trusted responses while alerting knowledge managers to any compliance issues before content reaches customer-facing channels.

2. Learning Systems That Adapt Over Time

The MIT study identifies the “learning gap” as the primary barrier keeping organizations trapped on the wrong side of the GenAI divide. Static AI tools that require constant prompting and don’t retain context fail at scale. Financial institutions need AI systems that learn from feedback, adapt to workflows, and improve continuously.

How eGain Delivers: eGain’s AI Knowledge Hub features persistent memory and iterative learning capabilities. Unlike static systems, eGain’s platform retains context from interactions, learns from user feedback, and adapts to specific financial workflows over time, ensuring continuous improvement and relevance. The system maintains comprehensive customer context across all touchpoints, enabling personalized service that improves with every interaction.

3. Deep Workflow Integration, Not Surface-Level Tools

The MIT research shows that 95% of custom enterprise AI tools fail to reach production, primarily due to poor integration with existing workflows. Financial institutions need AI that embeds seamlessly into their core systems rather than requiring users to switch between platforms.

How eGain Delivers: eGain’s platform integrates directly with existing financial services infrastructure, including CRM systems, core banking platforms, and compliance management tools. This deep integration ensures AI capabilities enhance rather than disrupt established workflows, delivering knowledge precisely when and where employees need it.

4. Focused Use Cases with Rapid Time-to-Value

The MIT study shows that successful AI implementations start with specific, narrow use cases that deliver clear value before expanding, while organizations that achieve deployment within 90 days succeed where those taking nine months or longer fail. Financial institutions need to resist the temptation to solve everything at once and instead focus on high-impact applications that can demonstrate value quickly.

How eGain Delivers: eGain specializes in knowledge-intensive financial services use cases including customer service automation, advisor productivity enhancement and even financial wellness coaching through eGain AI Coach. This focused approach ensures deep domain expertise and proven results. eGain’s Innovation in 30 Days program enables financial institutions to deploy production-ready AI capabilities in weeks, not months, through a proven methodology that includes discovery, design, configuration, and optimization—allowing organizations to realize value quickly while minimizing implementation risk.

5. Measurable ROI with Clear Metrics

Organizations that successfully cross the “GenAI divide” demonstrate concrete business outcomes including cost reduction, productivity improvements, and customer satisfaction gains. Financial institutions need AI solutions that deliver quantifiable returns on investment.

How eGain Delivers: eGain clients in financial services report material measurable results including 36% improvement in First Contact Resolution, 40% reduction in training time, and significant cost savings from reduced BPO spending. The platform provides comprehensive analytics to track and optimize ROI continuously, ensuring that AI investments deliver documented business value.

6. Partnership with Domain Expertise

The MIT study shows that external partnerships achieve twice the success rate of internal builds (66% vs 33%). Financial institutions need AI vendors with deep industry knowledge and proven implementation experience rather than generic technology providers.

How eGain Delivers: With over two decades serving financial services organizations and a customer set including leading global financial institutions, eGain brings deep domain expertise and a track record of success. eGain clients took 4 out of the top 5 spots among multichannel banks in the 2021 US Forrester CX Index, demonstrating sustained competitive advantage through the platform.

The Path Forward

The MIT study’s findings are clear: organizations that successfully leverage AI share common characteristics—they build on a foundation of Trusted Knowledge that unifies enterprise information with hybrid AI architectures balancing flexibility with accuracy, they partner with vendors who understand their industry’s unique challenges, and they focus on rapid deployment of high-value use cases. For financial institutions ready to move beyond pilots to production-scale AI success, these requirements provide a proven roadmap.

One thing is also clear, eGain is the smart choice for financial services companies looking for success in their AI-powered knowledge management.

The New Reality: When Knowledge Gaps Become Compliance Disasters

Dear Chief Compliance Officer,

When TD Bank agreed to pay $3 billion in penalties for AML failures in 2024—the largest penalty ever under the Bank Secrecy Act—it sent a clear message to every compliance executive in financial services: the era of tolerating knowledge management vulnerabilities is over. With global regulatory fines reaching a record-breaking $19.3 billion in 2024 and banks alone facing $3.65 billion in penalties (a 522% increase from the previous year), the question isn’t whether your institution will face scrutiny, but whether your knowledge infrastructure can withstand it.

As you navigate what KPMG aptly calls “The Year of Regulatory Shift” in 2025, your greatest vulnerability may not be in the policies you’ve written or the controls you’ve implemented—it’s in how you manage, secure, and retrieve the knowledge that proves your compliance. The collaboration platforms your teams rely on—SharePoint, Confluence, and similar tools—were never designed for the regulatory gauntlet you face today.

The Hidden Compliance Time Bomb in Your Knowledge Infrastructure

The Documentation Dilemma That Cost Billions

Consider what investigators found at TD Bank: outdated transaction monitoring systems, poor customer due diligence documentation, and systemic lapses in compliance with AML regulations. The bank failed to monitor significant transaction types, including ACH transfers and peer-to-peer platforms, with management oversight failures preventing necessary upgrades. This wasn’t just a technology failure—it was a catastrophic breakdown in knowledge management that allowed over $670 million to be laundered through the institution.

Your institution likely manages thousands of compliance documents across multiple platforms:

• Policy Documents: BSA/AML procedures, sanctions screening protocols, KYC/CDD requirements
• Risk Assessments: Customer risk profiles, transaction monitoring rules, jurisdictional risk analyses
• Audit Trails: Investigation records, SAR filings, regulatory correspondence
• Training Materials: Compliance certifications, procedural updates, regulatory bulletins
• Evidence of Controls: Testing documentation, exception reports, remediation tracking

When these critical documents live in traditional collaboration platforms, you face fundamental security and compliance risks that can transform routine examinations into existential threats.

The SharePoint and Confluence Compliance Nightmare

Active Exploitation: Not a Risk, But a Reality

The vulnerabilities in SharePoint aren’t theoretical—they’re being actively exploited today. In July 2025, Microsoft disclosed critical vulnerabilities (CVE-2025-49706, CVE-2025-49704, and their variants CVE-2025-53770, CVE-2025-53771) that allowed unauthenticated attackers to execute arbitrary code on SharePoint servers. These “ToolShell” vulnerabilities enabled threat actors to:

• Access confidential compliance documentation without authentication
• Modify audit trails and compliance records
• Deploy ransomware that could lock down your entire compliance infrastructure
• Extract sensitive customer data subject to GDPR and CCPA protections

For a financial institution, imagine the regulatory implications if attackers accessed your SAR filings, modified your transaction monitoring rules, or deleted evidence of compliance controls. The resulting penalties would dwarf the cost of any ransomware payment.

The Confluence Compliance Gap

Confluence faces similar challenges with regular high-severity vulnerability disclosures throughout 2025. For compliance teams using Confluence to manage:

• Regulatory change documentation
• Compliance committee minutes
• Investigation case files
• Training and certification records

Each vulnerability represents a potential breach not just of data security, but of regulatory trust. When regulators discover that your compliance documentation platform has known vulnerabilities, it raises questions about your entire risk management framework.

The Regulatory Perfect Storm: 2025’s Compliance Landscape

The Expanding Enforcement Universe

As a CCO in 2025, you’re navigating unprecedented regulatory complexity:

AML/BSA Evolution: Transaction monitoring violations saw penalties exceed $3.3 billion in 2024 (a 100% year-over-year increase). Regulators now expect real-time monitoring capabilities and AI-driven pattern detection—impossible to manage effectively when your knowledge base lacks proper security controls.

ESG Compliance: Global ESG-related fines increased 98% to $37.7 million in 2024. The EU’s Corporate Sustainability Reporting Directive requires disclosure across 84 topics and 1,000 data points—each requiring secure, auditable documentation.

DORA Implementation: The EU’s Digital Operational Resilience Act deadline of January 2025 demands comprehensive ICT risk management and third-party oversight—including your knowledge management vendors.

Privacy Regulations: GDPR, CCPA, and emerging state privacy laws require you to know exactly where sensitive data resides and retrieve it within strict timeframes. Data sprawl across unsecured collaboration platforms makes this nearly impossible.

The Cost of Non-Compliance Is Rising

North American regulators accounted for 95% of global financial penalties in 2024, with U.S. regulators issuing nearly 50 fines. The message is clear: enforcement is intensifying, and the price of failure is catastrophic:

• TD Bank: $3 billion for AML failures
• Nordea Bank: $35 million for inadequate due diligence
• Starling Bank: £29 million for financial crime failings
• City National Bank: $65 million for risk management deficiencies

Each of these cases involved failures in documentation, monitoring, and knowledge management—precisely the areas where traditional collaboration platforms are weakest.

Why Traditional Platforms Fail Compliance Requirements

1. The Audit Trail Disaster

When BCBS 239 data principles require comprehensive audit trails, SharePoint and Confluence offer:

• Limited tracking of document access and modifications
• No real-time monitoring of sensitive content access
• Inability to prove who knew what and when
• Gaps in demonstrating control effectiveness

During regulatory examinations, you need to instantly produce evidence of compliance activities, training completions, and control testing. Traditional platforms turn this into a week-long scavenger hunt that often uncovers gaps you didn’t know existed.

2. The Data Sprawl Catastrophe

Research shows 72% of public PaaS databases lack proper controls. In financial services, this translates to:

• Multiple versions of critical policies across different platforms
• Inconsistent application of compliance procedures
• Inability to ensure all staff have access to current requirements
• Regulatory confusion when different teams reference different versions

3. The Access Control Failure

Financial services compliance demands granular access controls:

• Segregation of duties for dual control processes
• Chinese walls between different business units
• Need-to-know restrictions on investigation files
• Geographic restrictions for data sovereignty

SharePoint and Confluence struggle with these requirements, often forcing you to choose between security and accessibility—a choice that inevitably leads to either compliance gaps or operational inefficiencies.

4. The Integration Impossibility

Modern compliance requires integration across multiple systems:

• Core banking platforms for transaction monitoring
• Case management systems for investigations
• Training platforms for certification tracking
• GRC tools for risk assessment

Traditional collaboration platforms create silos that prevent the unified view regulators expect during examinations.

The eGain Solution: Built for Financial Services Compliance

Security Certifications That Matter

Unlike traditional collaboration platforms, eGain’s AI Knowledge Hub comes with the security certifications financial services demand:

FedRAMP Authorization: While SharePoint and Confluence offer various security features, eGain has achieved FedRAMP authorization—the gold standard for security assessment, requiring adherence to NIST SP 800-53 controls covering access management, encryption, risk assessment, and continuous monitoring.

SOC 2 Type II Compliance: eGain demonstrates ongoing compliance with the five Trust Service Criteria—security, availability, processing integrity, confidentiality, and privacy—through regular independent audits.

HIPAA, PCI, and GDPR Compliance: Critical for financial institutions handling sensitive customer data across multiple regulatory jurisdictions.

These aren’t just checkboxes—they represent a fundamental architectural difference in how knowledge is secured, accessed, and audited.

Purpose-Built for Regulatory Requirements

eGain’s Trusted Knowledge™ approach addresses the specific needs of financial services compliance:

Granular Access Controls: Knowledge is dynamically personalized based on role, region, and compliance requirements. Multiskilled agents can have multiple profiles, ensuring Chinese walls and segregation of duties are maintained programmatically.

Complete Audit Trail: Every access, modification, and interaction is logged and retrievable. When regulators ask who accessed what and when, you have instant, comprehensive answers.

Content Lifecycle Management: From creation through deprecation, every document follows defined workflows with appropriate approvals and version control—essential for demonstrating compliance with regulatory change management requirements.

Unified Knowledge Repository: Instead of scattered documents across multiple platforms, eGain provides a single source of truth that integrates with your existing GRC, case management, and core banking systems.

Real-World Impact for Financial Services

Financial institutions using eGain report transformative results:

• Top-10 Global Bank: Improved advisor productivity by 15% while maintaining complete compliance documentation
• Major Federal Agency: Reduced case handling time by 25% while improving compliance metrics
• 92% Agent Engagement: Versus industry benchmark of 67%, ensuring consistent application of compliance procedures

The Business Case: ROI Beyond Compliance

Quantifiable Benefits

Risk Reduction: With average data breach costs at $3.86 million and AML fines reaching billions, proper knowledge security isn’t a cost—it’s an investment in institutional survival.

Efficiency Gains: Organizations report 60% deflection of routine compliance queries through secure self-service, freeing compliance teams for high-value activities.

Audit Readiness: Transform weeks of audit preparation into hours of report generation with complete, secure documentation trails.

Regulatory Confidence: When examiners see FedRAMP and SOC 2 certifications, it demonstrates a commitment to security that goes beyond minimum requirements.

The Competitive Advantage

In an environment where 60% of small businesses close within six months of a cyber attack, robust knowledge security becomes a competitive differentiator. Clients choosing between financial institutions increasingly consider not just your compliance record, but your compliance infrastructure.

Your Action Plan: From Vulnerability to Resilience

Immediate Steps (Next 30 Days)

1. Conduct a Knowledge Security Audit
   • Inventory all compliance documentation locations
   • Identify which platforms have known vulnerabilities
   • Assess current access controls and audit capabilities
   • Document gaps in your knowledge security posture
2. Evaluate Your Risk Exposure
   • Calculate potential penalties based on recent enforcement actions
   • Assess reputational risk from knowledge security failures
   • Determine cost of current inefficiencies in compliance operations
   • Quantify the investment needed to address vulnerabilities
3. Build Your Business Case
   • Document current compliance costs and inefficiencies
   • Calculate ROI of secure knowledge management
   • Identify quick wins and long-term benefits
   • Present findings to executive leadership and the board

Strategic Implementation (Next 90 Days)

1. Select a Compliance-Grade Solution
   • Require FedRAMP or equivalent security certification
   • Demand comprehensive audit trail capabilities
   • Ensure integration with existing compliance systems
   • Verify vendor’s track record in financial services
2. Pilot with Critical Processes
   • Start with high-risk compliance areas (AML, sanctions screening)
   • Measure improvements in efficiency and security
   • Gather feedback from compliance teams and auditors
   • Document lessons learned and best practices
3. Scale for Enterprise Impact
   • Expand to all compliance documentation
   • Integrate with GRC and case management systems
   • Train all compliance personnel on secure practices
   • Establish ongoing monitoring and improvement processes

The Future of Financial Compliance: AI-Powered, Secure, and Agile

As you look ahead, consider that by 2025, Gartner predicts 100% of generative AI projects lacking integration to modern knowledge management systems will fail to meet their objectives. The financial institutions that thrive will be those that combine:

• AI-Powered Intelligence: Automated monitoring, pattern detection, and anomaly identification
• Unbreakable Security: Military-grade encryption, comprehensive access controls, and complete audit trails
• Regulatory Agility: Ability to adapt quickly to new requirements and demonstrate ongoing compliance

eGain’s AI Knowledge Hub, with its FedRAMP authorization and comprehensive compliance features, represents this future—available today.

Conclusion: The Choice That Defines Your Legacy

As a Chief Compliance Officer, you’ll be remembered for one of two things: the breach that happened on your watch, or the transformation that prevented it. TD Bank’s $3 billion penalty serves as a stark reminder that traditional approaches to knowledge management are no longer sufficient.

The vulnerabilities in SharePoint and Confluence aren’t just IT problems—they’re existential compliance risks. When threat actors can access, modify, or destroy your compliance documentation, when regulators find gaps in your audit trails, when you can’t prove control effectiveness because documentation is scattered across insecure platforms, the resulting penalties and reputational damage can destroy decades

Enterprise knowledge represents one of your organization’s most valuable assets—yet it’s also one of the most vulnerable. As companies increasingly rely on collaboration platforms like SharePoint and Confluence to manage their intellectual capital, they’re unknowingly exposing themselves to significant security risks that could cost millions in breaches, compliance failures, and lost competitive advantage.

With 72% of public PaaS databases lacking proper controls and threat actors causing more than 290 million data leaks in 2023, the time for complacency has passed. This article examines why traditional knowledge management approaches are failing and how modern solutions like eGain’s AI Knowledge Hub are transforming enterprise knowledge security.

The $31 Billion Problem: When Knowledge Becomes a Liability

Knowledge chaos costs organizations an estimated $31 billion annually, but the true price extends far beyond direct financial losses. Your enterprise knowledge—from customer data and proprietary processes to compliance documentation and strategic insights—forms the backbone of your competitive advantage. Yet most organizations are managing this critical asset with tools that weren’t designed for today’s security landscape.

Research shows that 82% of data breaches involve a human element, such as misconfiguring a database or making mistakes that allow criminals to access systems. When this human factor combines with the inherent vulnerabilities of traditional collaboration platforms, the result is a perfect storm of security risks.

The Security Vulnerabilities of SharePoint and Confluence

SharePoint: A Target for Active Exploitation

Recent events have highlighted just how vulnerable SharePoint installations can be. In July 2025, Microsoft disclosed active attacks against on-premises SharePoint servers exploiting critical vulnerabilities CVE-2025-49706 and CVE-2025-49704, with new variants CVE-2025-53770 and CVE-2025-53771 bypassing initial patches. These aren’t theoretical risks—they’re active threats being exploited in the wild.

The SharePoint vulnerability chain, known as “ToolShell,” demonstrates the cascading nature of enterprise knowledge security failures:

• Unauthenticated Access: The vulnerabilities allow unauthenticated threat actors to access functionality that’s normally restricted, enabling them to run arbitrary commands on vulnerable SharePoint instances
• Ransomware Deployment: Threat actors have been observed modifying Group Policy Objects to distribute Warlock ransomware in compromised environments
• Persistent Threats: Malware deployed via .dll payloads are particularly difficult to detect and can be used to obtain machine keys

Confluence: Ongoing Security Challenges

Confluence faces similar security challenges with regular vulnerability disclosures. Throughout 2025, Atlassian has released multiple security bulletins addressing high-severity vulnerabilities, including remote code execution risks. These recurring security issues highlight a fundamental problem: traditional collaboration platforms weren’t built with security as a core architectural principle.

The Root Causes: Why Traditional Platforms Fall Short

1. Data Sprawl and Loss of Control

Data sprawl refers to the dramatic proliferation of enterprise data across IT environments, leading to management challenges and security risks that make it increasingly difficult for IT and security teams to track, manage, and secure data. Traditional collaboration platforms often exacerbate this problem by:

• Creating multiple copies and versions across different storage systems
• Lacking centralized visibility into who accesses what information
• Operating in silos that prevent comprehensive security monitoring

2. Inadequate Access Controls

Without correct access control and user permission settings, attackers can exploit knowledge management systems to enumerate users—often the first step in sophisticated attacks that lead to privilege escalation. SharePoint and Confluence installations frequently suffer from:

• Over-permissioned user accounts
• Insufficient granularity in access controls
• Difficulty in managing permissions across large, complex environments

3. Architectural Limitations

Both SharePoint and Confluence were designed primarily for collaboration, not security. This results in:

• Application-layer vulnerabilities: While firewalls provide network-level defense, vulnerabilities in these platforms exist at the application layer where attackers with basic network access can exploit misconfigurations
• Patch management challenges: Organizations struggle to keep up with the constant stream of security updates
• Limited security features: Native security capabilities often fall short of enterprise requirements

4. Compliance and Governance Gaps

Stringent data privacy laws like GDPR require companies to know exactly where sensitive data lives and be able to retrieve it in a timely manner, with organizations facing costly fines for non-compliance. Traditional platforms make it difficult to:

• Track data lineage and access patterns
• Ensure consistent security policies across all content
• Provide audit trails for compliance reporting
• Respond to data subject requests within regulatory timeframes

The eGain Solution: Security-First Knowledge Management

eGain’s AI Knowledge Hub represents a paradigm shift in enterprise knowledge security, addressing the fundamental vulnerabilities that plague traditional collaboration platforms.

Advanced Compliance and Security Architecture

eGain Composer adheres to top-tier security and authentication standards, including OAuth 2.0, HTTPS, SOC 2, HIPAA, GDPR, and FedRAMP. This comprehensive security framework isn’t an afterthought—it’s built into the platform’s core architecture.

Trusted Knowledge™ Foundation

Unlike traditional platforms that treat all content equally, eGain’s Trusted Knowledge approach provides unified, correct, and compliant content that enables AI to deliver reliable, accurate answers. This means:

• Content integrity: Every piece of knowledge is validated and verified
• Compliance by design: Built-in controls ensure regulatory adherence
• Granular permissions: Knowledge presented to agents is dynamically personalized based on factors like role, region, and more, with multiskilled agents able to have multiple profiles and switch between them

AI-Powered Security and Governance

eGain’s AI Knowledge Hub embeds best practices in knowledge management and AI orchestration through the eGain AI Knowledge Method, focusing on discovering “the knowledge you need, not the knowledge you have”. This approach includes:

• Automated content lifecycle management: Granular API controls throughout the content management lifecycle—from authoring to deprecation
• Intelligent monitoring: Real-time detection of anomalies and potential security threats
• Automated compliance: AI-driven tools ensure content meets security and regulatory requirements

Breaking Down Silos While Maintaining Security

By 2025, Gartner predicts 100% of generative AI virtual customer assistant projects lacking integration to modern knowledge management systems will fail to meet their customer experience and operational cost-reduction goals. eGain addresses this by:

• Providing secure, unified access across all channels and touchpoints
• Pre-integrating with platforms like Microsoft Teams while maintaining enterprise-grade security
• Enabling federated search while preserving access controls

The Business Case: Why Security-First Knowledge Management Matters

Quantifiable Risk Reduction

A single data breach can cost an organization up to $3.86 million on average, or $148 for every stolen record containing confidential information. By implementing proper knowledge security:

• Reduce breach probability by eliminating common attack vectors
• Minimize potential damage through granular access controls
• Accelerate incident response with comprehensive audit trails

Operational Excellence

Organizations leveraging eGain’s AI Knowledge Hub report reduced Average Handle Time (AHT), improved First-Contact Resolution (FCR), and elevated customer experience metrics including CSAT and NPS. Security doesn’t come at the cost of efficiency—it enables it.

Competitive Advantage

In an era where over 60% of small businesses close within six months of a cyber attack, robust knowledge security isn’t just about protection—it’s about survival and competitive differentiation.

Implementation Best Practices

1. Assess Your Current Risk Profile

• Inventory all knowledge repositories across your organization
• Identify sensitive data and its current security posture
• Evaluate existing access controls and governance policies

2. Adopt a Zero-Trust Approach

• Implement least-privilege access principles
• Require authentication for all knowledge access
• Continuously verify and validate user permissions

3. Embrace Continuous Improvement

Security configurations should not be a “one-time” setup but should be validated periodically and after any system update, patch, or major change. Implement:

• Regular security audits
• Automated compliance monitoring
• Continuous threat detection and response

4. Invest in the Right Technology

Choose knowledge management solutions that:

• Prioritize security in their architecture
• Provide comprehensive compliance features
• Offer granular access controls and audit capabilities
• Support modern authentication and encryption standards

The Path Forward: From Vulnerability to Resilience

The security challenges facing enterprise knowledge management are real and growing. As businesses undergo radical digital transformation, they collect and generate immense volumes of data from heterogeneous sources, with rising remote work trends contributing to data sprawl through additional digital identities, personal devices, and collaboration software.

Traditional collaboration platforms like SharePoint and Confluence, while valuable for their collaboration features, simply weren’t designed to handle today’s security requirements. Their architectural limitations, combined with the complexity of modern enterprise environments, create unacceptable risks for organizations that depend on their knowledge assets.

eGain’s AI Knowledge Hub represents a new generation of knowledge management—one where security, compliance, and governance are foundational rather than bolted on. By adopting a security-first approach to knowledge management, organizations can:

• Transform knowledge from a liability into a strategic asset
• Reduce security risks while improving operational efficiency
• Ensure compliance with evolving regulatory requirements
• Build trust with customers, partners, and stakeholders

Conclusion: The Time for Action is Now

Enterprise knowledge security isn’t just an IT issue—it’s a business imperative that affects every aspect of your organization. The vulnerabilities in traditional collaboration platforms aren’t theoretical risks; they’re active threats being exploited today. The question isn’t whether your organization will face a knowledge security challenge, but when and how prepared you’ll be to handle it.

The choice is clear: continue relying on vulnerable collaboration platforms that put your enterprise knowledge at risk, or embrace modern, security-first solutions like eGain that protect your most valuable assets while enabling innovation and growth.

Your enterprise knowledge is too valuable to leave unprotected. The time to act is now, before your organization becomes another statistic in the growing list of knowledge security failures.

To learn more about how eGain can transform your enterprise knowledge security, visit www.egain.com or request a demonstration of the eGain AI Knowledge Hub.

The 1980s: Extraordinary Investment and Grand Visions

The 1980s saw extraordinary corporate investment in AI, particularly expert systems and knowledge-based reasoning. Companies believed they could capture expert knowledge in rule-based systems. GE developed DELTA for locomotive repair diagnostics, reportedly saving millions annually. Digital Equipment Corporation built XCON to configure VAX computer systems, processing thousands of orders and becoming one of the most successful early expert systems. American Express created expert systems for credit authorization.

Case-Based Reasoning (CBR) emerged as a promising alternative—solving new problems by adapting solutions from similar past cases. Inference Corporation, Cognitive Systems Inc., and others built commercial CBR platforms for help desk support, legal research, medical diagnosis, and design assistance.

The vision was intoxicating: capture retiring experts’ knowledge, standardize decision-making, reduce training costs, and scale expertise globally. AI would fundamentally re-engineer corporate operations.

The Knowledge Management Movement (Late 1980s-1990s)

Knowledge management (KM) emerged with broader ambitions than AI, aiming to capture all organizational knowledge—documents, processes, lessons learned, and tacit knowledge. Companies like Lotus (Notes/Domino), Microsoft, and specialized vendors built platforms for knowledge repositories and collaboration.

KM recognized technology alone wasn’t enough, emphasizing communities of practice and knowledge-sharing cultures. Firms like McKinsey, Ernst & Young, and Accenture built massive internal KM systems to leverage knowledge across global practices.

The reality proved messy. Knowledge repositories became overstuffed “knowledge graveyards” with primitive search. People didn’t naturally document knowledge, and systems felt like extra work rather than enablers.

What Went Wrong: The AI Winter Returns

Technical Limitations: Expert systems were brittle—working well in narrow domains but failing catastrophically outside them. Knowledge acquisition took far longer and cost more than anticipated. As business rules changed, updating thousands of interconnected rules became unmanageable. CBR systems struggled with retrieval at scale and adapting cases to different situations. Symbolic AI couldn’t handle uncertainty or learn from data well.

Economic Reality: Development costs were astronomical—often millions per system—with hard-to-prove ROI. Specialized LISP machines became obsolete as PCs grew powerful. Many systems never left pilot projects or were abandoned when key champions departed.

The Hype Cycle: Vendors overpromised dramatically. When systems couldn’t deliver transformative results, disillusionment hit hard. Funding dried up in the late 1980s/early 1990s as companies recognized the gap between promise and reality.

Knowledge Management Challenges: The “if you build it, they will come” approach failed. Tacit knowledge proved much harder to capture than explicit knowledge. Knowledge quickly became outdated without good validation mechanisms. Search was too primitive for large repositories. Cultural resistance—knowledge hoarding for job security, “not invented here” syndrome, and lack of time—undermined adoption.

Changing Technology Landscape: The internet and web browsers in the mid-1990s shifted attention and resources. Data warehousing, business intelligence, and ERP systems offered more immediate, measurable value. The PC revolution made expensive, specialized AI systems seem anachronistic.

What Actually Worked

Not everything failed. Specific, narrow expert systems like XCON saved real money. Credit card fraud detection evolved from rule-based to hybrid approaches. Manufacturing diagnostics and scheduling systems succeeded in controlled environments. Cultural lessons about knowledge sharing influenced later collaboration tools. CBR found lasting niches in help desk systems and design reuse.

Legacy and Lessons

The 1980s-90s AI and KM wave left important legacies. Companies learned that technology without process change and cultural buy-in fails—lessons that informed later enterprise software implementations. Much of today’s AI renaissance builds on symbolic AI research from that era, now combined with machine learning and neural networks that learn patterns from data rather than requiring explicit programming.

The oversell created skepticism that persisted for decades. When modern AI emerged in the 2010s, there was initial wariness about “AI hype” precisely because of this history.

The goal of capturing and leveraging organizational knowledge remains valid. Today’s approaches—using machine learning, natural language processing, better search, and sophisticated knowledge graphs—are finally delivering on those old promises with fundamentally different technical approaches.

The early excitement faded because the gap between vision and capability was too large given 1980s-90s technology. Symbolic AI hit fundamental limits, knowledge engineering didn’t scale, and the economics didn’t work. But the problems those pioneers identified were real, and we’re now revisiting them with dramatically more powerful tools.