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 Customers receive inconsistent answers, AI systems provide outdated information, or bots confidently deliver incorrect guidance that creates compliance risks.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of customer service leaders have a backlog of articles to edit, and over one-third have no formal process for revising outdated content—yet many are deploying conversational AI that depends entirely on this content.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.

Customer service is in crisis. Despite US companies spending a staggering $102 billion on training in 2023 alone, the industry faces unprecedented challenges: 50% agent churn rates, training materials becoming obsolete before the ink dries on certificates, and employees stuck on an endless “training treadmill.” But there’s hope on the horizon—and it comes in the form of Generative AI paired with modern knowledge management.

The Perfect Storm Facing Customer Service

The customer service landscape has fundamentally shifted. Today’s challenges go far beyond traditional training hurdles:

The Retention Crisis: Humans retain only 2% of what they learn after just one month. Combined with remote work disrupting traditional onboarding, this creates a perfect storm of inefficiency.

Generational Shifts: Millennials have 12-second attention spans, while Gen Z clocks in at just 8 seconds. Both generations prefer learning on the job rather than sitting through lengthy training sessions.

The Churn Problem: Call centers face 35% “shrinkage” rates and 50% agent turnover, creating what one industry expert calls “an unending cycle of hiring and training workers, only to see them leave in a matter of weeks or months.”

Enter the Power Duo: GenAI + Knowledge Management

The solution isn’t just more training—it’s smarter, AI-powered knowledge delivery. The combination of Generative AI and robust knowledge management creates what industry leaders call “The Power Duo,” offering:

Contextual Knowledge

• Information delivered precisely when and where agents need it
• Embedded directly in their workflow
• Trusted, curated content that reduces errors

Conversational AI Capabilities

• Natural language interactions with knowledge systems
• Instant answers to complex customer queries
• Adaptive responses based on context and customer history

Real-World Impact: The Numbers Don’t Lie

The results speak for themselves. Organizations implementing AI-powered knowledge management are seeing transformational improvements:

EE (UK’s Largest Mobile Network)

• 37% improvement in First Contact Resolution
• 2x faster time-to-competency for new agents
• 43% reduction in agent training time

Leading Utility Company

• 6x reduction in “failure to find answer” scenarios
• 5x faster knowledge creation and curation
• Agent satisfaction trending up while training time trends down

How GenAI Turbocharges Knowledge Management

Generative AI isn’t just augmenting knowledge management—it’s revolutionizing it:

For Knowledge Authors:

• 60-80% of authoring and curation tasks can be automated
• AI drafts articles, translates content, and maintains consistency
• Actionable insights guide content improvements

For Customer Service Agents:

• Instant answers generated from multiple knowledge sources
• Conversation summarization and response suggestions
• Real-time guidance during customer interactions

For Analysts:

• Extract insights from reports automatically
• Generate executive summaries
• Track prompt effectiveness and optimize AI usage

The McKinsey Factor: 30-45% Cost Reduction Potential

According to McKinsey Research, companies implementing Generative AI in customer service can expect a 30-45% reduction in service costs through:

• Automated response suggestions
• Intelligent conversation guidance
• Smart case summarization
• Streamlined wrap-up processes

Beyond Cost Savings: The Customer Experience Revolution

While cost reduction grabs headlines, the real revolution is in customer experience quality. AI-powered knowledge management enables:

Consistency at Scale: Every agent has access to the same high-quality, up-to-date information, eliminating the knowledge gaps that frustrate customers.

Faster Resolution: With instant access to comprehensive knowledge, agents resolve issues on first contact more often, reducing customer effort.

Personalized Service: AI can suggest responses tailored to customer context, history, and preferences, making every interaction feel more personal.

The Technology Behind the Transformation

Modern AI knowledge platforms integrate seamlessly with existing customer service infrastructure, providing:

• Trusted Content Management: Curated, governed knowledge bases that ensure accuracy
• Closed-Loop Analytics: Continuous learning and improvement based on real interactions
• Process Orchestration: Automated workflows that keep knowledge current and relevant
• Advanced Security: Enterprise-grade controls that protect sensitive information

Looking Ahead: The Future of AI-Powered Customer Service

As one industry analyst noted, “We’re moving from training employees to empowering them.” The future of customer service lies not in more training sessions, but in intelligent systems that make every agent an expert from day one.

Organizations that embrace this AI-powered approach are already seeing measurable results:

• Faster onboarding (often 2x improvement)
• Higher agent satisfaction and retention
• Better customer experiences
• Significant cost reductions

Getting Started: A Risk-Free Approach

For organizations ready to transform their customer service operations, the path forward is clearer than ever. Leading vendors now offer guided pilot programs that allow companies to:

• Experience AI-powered knowledge management with their own data
• Model expected business value before making investments
• De-risk the selection process with no-cost trials

The Bottom Line

The combination of Generative AI and knowledge management isn’t just the future of customer service—it’s the present. Organizations that act now will gain a competitive advantage that compounds over time, while those that wait risk being left behind in an industry where customer expectations continue to rise.

The question isn’t whether AI will transform customer service, but how quickly your organization will embrace the change. In an era where every customer interaction matters, can you afford not to give your agents the AI-powered tools they need to succeed?

1. Delight, don’t just deflect

Businesses should implement self-service with the strategic intent of not only deflecting phone calls but also delivering delightful self-service experiences for customers and building the brand. Poor self-service, purely focused on reducing live customer contact, can turn off customers. Here are some ways to delight customers through self-service:

• Provide multiple self-service options whether it is the interaction channel that a customer might prefer or the way they might want to use self-service to find answers—some might like FAQs, others might prefer natural language conversations, and some might prefer step-by-step guidance, for example
• Make sure to allow customers to escalate from self-service to human-assisted service without losing self-service context. Who wants to repeat the mother’s maiden name ad nauseum?!

2. Teach them to “fish”

You know the proverb “give a man a fish, you feed him for a day; teach a man to fish, you feed him for a lifetime.” Use technologies such as cobrowse to teach customers how to use self-service so they can help themselves when they come back for service the next time—this is like providing training wheels for kids when they learn how to ride a bike.

3. Self-service everywhere

Customers should literally “trip” on self-service. Make it easy to find and ubiquitous.

• Make it available at touchpoints where your customers “live”—web, app, IVR, and more
• When customers are on hold for human-assisted service, organizations can send a contextual self-service link to the customer, where they might find the answer, while assuring them they won’t lose their place on the call (or live chat) queue if they decide to try out self-service. Since most calls today originate from smart phones, this approach is an effective way to gently nudge customers to digital self-service.

The caveat: Your self-service better be contextual, correct, and consumable for this approach to work.

4. Don’t force it

While this is not a complete list, here are some common self-service blunders:

• Trapping customers in a self-service cul de sac or dead-end without allowing them to escalate to a human when they want to
• Trying to handle complex or high-stakes customer queries through self-service without assessing the organization’s self-service capabilities and maturity
• Continuing to push self-service when customer sentiment is going south in a self-service conversation
• Not preserving self-service context when the conversation is escalated to human-assisted service

Businesses should triage customer queries based on customer sentiment, nature of the query, value of the customer, and other factors. For instance, high-stakes queries on life-and-death matters or large and complex financial transactions could be routed to human experts whereas routine low-stakes queries can be routed to self-service first. As part of a unified omnichannel Knowledge Hub, AI can be used to triage customer queries at scale.

5. Trust or bust for AI

Gartner warns that 100% of AI projects for CX will fail without integration with a modern knowledge management system! 61% of contact center leaders and consumers are concerned about erroneous or inconsistent answers from AI, according to a recent KMWorld State of AI survey. Trust ignites user adoption, which, in turn, ignites business value. The opposite is true when answers cannot be trusted. Consumers and frontline employees associate “trusted” with correct, consumable, compliant, and contextual. A surefire way to deliver trusted answers is to implement a central hub for AI Knowledge, where conversational, generative, and agentic AI are backed by rich content management, pre-built connectors to trusted enterprise data and content, and analytics.

6. Leverage the right technology and technology partner

The building blocks of a modern self-service system include:

• Rich interaction capabilities unified in a conversation hub, supporting a comprehensive set of interaction channels—SMS, social app messaging, live chat, cobrowse, email and more, including support of channel-specific features (e.g., support of Apple Pay when a customer is interacting through Apple’s Messages for Business)
• AI Knowledge Hub (explained earlier). Moreover, an AI knowledge hub built on a composable, BYO architecture provides you with the flexibility to leverage your own bots, LLMs, and other sourcing and consumption points
• Analytics Hub to get insights on contact center operations and AI knowledge scope and performance (e.g., agent performance by individual or queue or the effectiveness and adoption of GenAI prompts)
• Generative and agentic AI capabilities to speed up the knowledge management lifecycle and further automate service—from delivering trusted answers to also driving trusted actions on behalf of the customer, all backed by the AI knowledge hub
• Technology partner with a proven AI knowledge implementation method and domain expertise to generate quick business value

eGain client success stories

• Specialized Bicycles, a pioneering leader in e-bikes, replaced their hard-to-use, obsolete knowledge management system for self-service with the eGain AI Knowledge Hub. The eGain hub now serves as the single source of truth, i.e., trusted content and process knowhow—across 21 languages! Self-service search success has improved 85% and the use of conversational AI for diagnostics has soared 18X since the deployment of eGain!
• Hypergrowth retailer was struggling to meet the soaring demand for customer service. They tried out eGain’s Virtual Assistant through the eGain Innovation in 30 Days™ program, a risk-free production pilot. Delighted with the experience, the retailer deployed eGain chatbots for multiple brands. The bots are resolving a wide range of shopper queries, deflecting customer contacts by up to 90%. Where needed, they escalate the conversation to agents, who can see the full self-service context in the eGain Advisor Desktop™, to seamlessly move the conversation forward.
• Leading omnichannel retailer is automating and augmenting customer service with eGain, leveraging digital self-service, messaging, chat, and IVR deflection to digital service, all backed by the eGain AI Knowledge Hub™, to handle over 9 million customer contacts per year. They are deflecting 45% of phone contacts and 30% of IVR contacts with digital self-service and chat messaging, delivering joined-up omnichannel service with context-aware escalation to agent-assisted service.
• Large federal government agency achieved groundbreaking results after adopting the eGain AI Knowledge Hub. They were able to divert up to 70% of incoming calls to AI virtual assistants, cut case handling time by 25%, and streamline online form-filling with AI knowledge assistance. These impactful improvements boosted agent engagement to 92%, well above the industry average of 67%.

Conclusion

Done right with the backing of a trusted AI knowledge hub, self-service can help slash customer service cost dramatically while scaling service cost-effectively and elevating the brand.

At eGain, we’ve observed a consistent pattern: businesses enthusiastically launch GenAI pilots for customer service automation, only to encounter roadblocks when attempting to scale these initiatives. The fundamental issue is mistaking GenAI technology for a complete solution. This blog explores practical use cases that leverage AI Knowledge Hubs to deliver rapid implementation and sustainable value in customer service environments.

The Foundation: AI Knowledge Hub as a Single Source of Truth

Before diving into specific use cases, it’s essential to understand that successful GenAI implementation in customer service requires a solid foundation—an AI Knowledge Hub that serves as a single source of truth. This hub ensures all generated responses are:

• Correct: Based on factual information rather than AI hallucinations
• Consistent: Delivering uniform answers across all customer touchpoints
• Compliant: Adhering to regulatory requirements and company policies

By layering GenAI capabilities on top of this knowledge foundation, organizations can implement transformative customer service solutions in as little as 30 days, without the complexities and risks of building custom solutions from scratch.

Rapid-Implementation Use Cases

1. AI-Powered Self-Service Knowledge Portal

Implementation timeframe: 2-3 weeks

A knowledge portal enhanced with GenAI capabilities allows customers to ask questions in natural language and receive accurate, contextual responses drawn from your knowledge hub. Unlike generic GenAI implementations that might generate plausible-sounding but incorrect information, this approach ensures answers are grounded in your verified knowledge base.

Key benefits:

• Reduces call volumes by 25-40%
• Increases self-service success rates by up to 60%
• Maintains brand voice and compliance standards
• Provides 24/7 consistent service without additional headcount

Why packaged solutions win: Building this capability from scratch would require developing natural language processing capabilities, knowledge indexing systems, and user interfaces—all while ensuring proper governance. A pre-packaged solution delivers immediate value without these development challenges.

2. Agent Assistance with Real-Time Knowledge Recommendations

Implementation timeframe: 3-4 weeks

This use case enhances your contact center by providing agents with AI-powered, contextual knowledge recommendations during customer interactions. The system listens to customer conversations (voice or digital) and proactively suggests relevant information, procedures, and solutions from your knowledge hub.

Key benefits:

• Reduces average handle time by 20-30%
• Decreases new agent ramp-up time by up to 50%
• Ensures consistent application of policies and procedures
• Improves first-contact resolution rates by 15-25%

Why packaged solutions win: Developing this capability internally would require integrating speech-to-text technology, real-time analysis systems, knowledge retrieval mechanisms, and agent desktop interfaces—a complex undertaking that diverts resources from your core business.

3. Intelligent Case Classification and Routing

Implementation timeframe: 2-3 weeks

This application uses GenAI to understand incoming customer inquiries, automatically classify them based on intent, and route them to the appropriate department or specialist. The AI draws from the knowledge hub to identify case types and determine optimal routing paths.

Key benefits:

• Reduces misrouted cases by up to 80%
• Decreases case resolution times by 15-20%
• Provides consistent customer experiences across channels
• Enables meaningful analytics on customer inquiry patterns

Why packaged solutions win: Building routing intelligence requires developing complex natural language understanding models, integration with multiple communication channels, and configuration of business rules—all capabilities already refined in packaged solutions.

4. AI-Guided Conversational Process Automation

Implementation timeframe: 3-4 weeks

This use case employs GenAI to guide customers or agents through complex processes, such as policy changes, claims processing, or product configuration. The AI references procedural knowledge from your hub while maintaining a natural conversation flow.

Key benefits:

• Ensures 100% process compliance
• Reduces error rates by up to 90%
• Decreases process completion time by 30-50%
• Improves customer satisfaction with complex transactions

Why packaged solutions win: Creating guided conversations requires sophisticated dialog management, process modeling capabilities, and integration with backend systems—components that would take months or years to develop internally.

5. Proactive Outreach with Personalized Knowledge

Implementation timeframe: 2-3 weeks

This application uses GenAI to identify opportunities for proactive customer communication based on behavior patterns, then generates personalized outreach content drawn from your knowledge hub. For example, sending preventive maintenance tips to customers whose products are approaching service intervals.

Key benefits:

• Increases customer retention by 5-15%
• Reduces inbound service requests by 10-20%
• Enhances customer perception of service quality
• Creates upsell and cross-sell opportunities

Why packaged solutions win: Building proactive systems requires developing complex event detection, personalization algorithms, and multi-channel delivery mechanisms—capabilities that packaged solutions provide out-of-the-box.

The “Build vs. Buy” Fallacy in GenAI Implementation

Many organizations initially gravitate toward building their GenAI solutions using developer tools like Microsoft’s CoPilot or Salesforce’s Einstein. While these platforms offer impressive capabilities, they’re fundamentally developer tools, not complete solutions. The journey from proof-of-concept to enterprise-scale deployment typically reveals significant gaps:

Common Challenges with DIY GenAI Solutions

1. Governance and Compliance Gaps: Ensuring responses meet regulatory requirements across different jurisdictions
2. Integration Complexity: Connecting GenAI with existing knowledge sources, CRM systems, and communication channels
3. Performance at Scale: Managing response times and system reliability during peak demand
4. Knowledge Management Overhead: Updating and maintaining the information that GenAI draws upon
5. Lack of Specialized Analytics: Missing insights specific to customer service operations

These challenges explain why many organizations find themselves with promising GenAI prototypes that never achieve operational scale.

The Enterprise Solution Advantage

Just as few companies today would consider building their own CRM or contact center systems from scratch, the same logic applies to AI Knowledge solutions. Enterprise-class solutions provide critical components that developer tools alone cannot.

1. Purpose-Built Architecture: Designed specifically for customer service use cases
2. Pre-Built Workflows: Optimized for common customer service processes
3. Specialized User Interfaces: Designed for both agents and customers
4. Comprehensive APIs: Enabling integration with your technology ecosystem
5. Industry-Specific Knowledge Models: Accommodating the unique requirements of your sector
6. Service-Specific Analytics: Measuring impact on key customer service metrics

Conclusion: Focus on Differentiation, Not Infrastructure

The most strategic approach to GenAI implementation is focusing your technical talent on areas that truly differentiate your business—your products, services, and unique operational processes. For customer service applications, leveraging packaged AI Knowledge Hub solutions delivers faster implementation, lower risk, and superior ROI.

Our experience at eGain has consistently shown that organizations achieve the greatest success when they treat GenAI as one component within a comprehensive knowledge management strategy, rather than as a standalone technology. By implementing a robust AI Knowledge Hub, you create the foundation for numerous use cases that can be deployed rapidly while ensuring the accuracy, consistency, and compliance that customers and regulators demand.

The promise of GenAI in customer service is tremendous—but realizing that promise depends on implementing it within the right framework. With a packaged AI Knowledge Hub solution, you can begin transforming your customer service operations in as little as 30 days, while avoiding the pitfalls of custom development.

To learn more about implementing these use cases in your organization, contact us at eGain for a personalized demonstration.

Gen AI helps KM

Gen AI accelerates each step of the KM lifecycle on a modern knowledge platform.

• Discover: Identifying likely questions is the first, often-ignored, step to an effective knowledge base. Gen AI can extract questions from interaction history using best-practice, contextualized prompts in the knowledge platform.
• Create/curate: Gen AI can draft knowledge content, using long-form, complex documents, and other enterprise sources. It can adjust content for brand voice, interaction channel, and consumer persona. Finally, it can propose knowledge taxonomy based on question patterns and user profile.
• Deliver: Enterprises have boatloads of documents with “correct” content, but they are not “consumable” by users. With an irate customer on the line, no agent wants to read a tome! Gen AI generates consumable answers, referencing multiple knowledge articles and documents.
• Optimize: Knowledge must be measured and managed for business impact. Gen AI identifies knowledge gaps in accuracy and ease-of-use, suggesting alternatives for improvement.

KM helps Gen AI

Gen AI can transform business, but it also poses significant risks. Among them are hallucination, loss of trust in answers and in the technology itself, knowledge fragmentation, and compliance risks. A modern KM system implemented as a hub—like the eGain Knowledge Hub—helps safely operationalize gen AI by offering:

• Trusted content: KM can ensure that correct data and content are used to feed and train gen AI. Without this foundation, the initiative is likely to fail with disastrous consequences for the brand.
• Controls and governance: A modern knowledge hub comes with controls to determine which queries to process with gen AI and which ones not to. The knowledge manager can control its “creativity,” as the situation warrants and configure additional accuracy checks.
• Closed-loop analytics: KM provides insights and actionable recommendations on the use, effectiveness, and improvement of gen AI actions.
• Process orchestration: Gen AI is an exciting building block of KM, but not the only one. A knowledge hub also includes other AI technologies like reasoning and machine learning, plus critical components such as content management and conversational guidance. The hub orchestrates these capabilities to deliver effective journeys to agents, business, and customers.

Conclusion

The symbiosis between Gen AI and KM is powerful. Without robust KM, Gen AI remains a prototype. Without Gen AI, KM struggles with building and maintaining knowledge in a fast-changing operation.

Related links

1. Knowledge Management and Generative AI Transformation
2. Webinar | ChatGPT and Generative AI for CX
3. The Best Use Cases for Generative AI in Digital Customer Service
4. Harnessing generative AI for customer service: KMWorld webinar
5. Generative AI For Customer Service: Best Practices For Success
6. Blog: 10 Use-Cases for Leveraging Generative AI for Better CX and AX (Agent Experience)
7. Try generative AI for customer service
8. eGain Knowledge Hub
9. eGain AssistGPT