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 Customers receive inconsistent answers, AI systems provide outdated information, or bots confidently deliver incorrect guidance that creates compliance risks.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of customer service leaders have a backlog of articles to edit, and over one-third have no formal process for revising outdated content—yet many are deploying conversational AI that depends entirely on this content.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

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 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.

The solution isn’t to abandon LLMs, but to embrace Hybrid AI: a strategic combination of probabilistic models (like LLMs) and deterministic systems (like rule-based engines and case-based reasoning). This approach leverages the strengths of each while mitigating their respective weaknesses.

The Probabilistic Problem: Why LLMs Alone Fall Short

LLMs are probabilistic by nature. They generate responses based on statistical patterns learned from training data, predicting the most likely next token in a sequence. This architecture creates several critical challenges for knowledge management:

Inconsistency Across Sessions: Ask an LLM the same compliance question on different days, and you may receive subtly—or significantly—different answers. For a financial advisor seeking guidance on SEC regulations, this variability is unacceptable.

Hallucination Risk: LLMs can confidently generate plausible-sounding but entirely incorrect information. When dealing with legal requirements, safety protocols, or regulatory standards, “plausible but wrong” can result in violations, penalties, or harm.

No Guarantee of Source Fidelity: Even with RAG (Retrieval Augmented Generation), LLMs may paraphrase, combine, or inadvertently modify retrieved information. In contexts where exact wording matters—like contract terms or regulatory language—this transformation introduces risk.

Difficulty with Precise Logic: Complex decision trees, multi-conditional rules, and exact threshold calculations aren’t LLMs’ forte. They excel at pattern matching and natural language, but struggle with the precise, repeatable logic that many business processes require.

Where Deterministic Models Excel

Deterministic systems—including rule-based engines, case-based reasoning (CBR), and expert systems—follow explicit logic paths and produce identical outputs given identical inputs. This predictability makes them indispensable in specific contexts:

1. Compliance and Regulatory Guidance

When employees need to know whether a transaction requires disclosure under Dodd-Frank, or whether a marketing claim complies with FDA regulations, the answer must be:

• Precise: Based on current, exact regulatory language
• Consistent: The same question yields the same answer every time
• Auditable: The reasoning path must be traceable for compliance reviews
• Source-verified: Directly tied to authoritative regulatory texts

A case-based reasoning system can match the current scenario against verified precedents and apply rule-based logic to determine the correct answer. The system can cite the specific regulation section and explain why it applies—critical for audits.

2. Safety-Critical Procedures

In manufacturing, healthcare, or aviation, procedural knowledge must be exact. “Approximately correct” instructions for operating a bioreactor or responding to an aircraft warning can be catastrophic. Deterministic systems ensure that:

• Checklists are followed in exact order
• Conditional branches (if pressure > X, then Y) execute precisely
• No steps are inadvertently omitted or reordered
• Version control is strict—everyone sees the current approved procedure

3. Contract and Policy Interpretation

Employee questions like “How many vacation days do I have after three years?” or “Does this expense qualify for reimbursement?” should return definitive answers based on exact policy rules. Deterministic engines can:

• Parse structured policy documents
• Apply conditional logic (if tenure > 3 years AND role = manager, then vacation = X)
• Handle exceptions consistently
• Update globally when policies change

4. Financial Calculations and Pricing

Pricing rules, discount eligibility, tax calculations, and commission structures require mathematical precision. A deterministic engine ensures that:

• Complex pricing formulas execute exactly as defined
• Threshold conditions (order > $10,000 triggers 5% discount) apply consistently
• Edge cases follow specified logic
• Audit trails show exact calculation steps

5. Multi-Step Decision Processes

Many business processes involve sequential decision points with clear criteria—loan approvals, benefits eligibility, escalation protocols. Case-based reasoning systems can:

• Match new cases against historical precedents
• Apply learned decision patterns consistently
• Incorporate feedback to refine case libraries
• Explain decisions by reference to similar past cases

The Hybrid Advantage: Best of Both Worlds

The optimal approach combines these complementary technologies:

Probabilistic LLMs Handle:

• Natural language understanding and query interpretation
• Contextual responses that require nuance and judgment
• Summarization and synthesis across multiple sources
• Conversational interaction and clarifying questions
• Handling ambiguous or exploratory queries

Deterministic Systems Handle:

• Regulatory compliance questions
• Policy and procedure lookups
• Calculations and rule-based decisions
• Version-controlled authoritative content
• Situations requiring perfect consistency

The Orchestration Layer routes queries to the appropriate system based on:

• Query classification (compliance vs. general knowledge)
• Risk level (high-stakes vs. informational)
• Required precision (exact vs. approximate answers)
• Source requirements (verified vs. general knowledge)

Real-World Implementation: A Compliance Scenario

Consider a pharmaceutical company’s knowledge management system:

Query: “Can we make this marketing claim about our drug’s efficacy?”

Hybrid AI Response Path:

1. LLM Component interprets the natural language query and extracts key elements (drug name, specific claim, marketing channel)
2. Routing Logic classifies this as a compliance-critical query requiring deterministic handling
3. Rule-Based Engine checks:
   • FDA approval status and approved indications
   • Clinical trial results vs. claim anguage
   • Regulatory precedents for similar claims
   • Required disclaimers and substantiation
4. Case-Based Reasoning retrieves similar past marketing claims and their regulatory outcomes
5. Deterministic Output provides a definitive answer: “No, this claim is not permissible because it exceeds the FDA-approved indication. See 21 CFR 202.1 and precedent case #4729.”
6. LLM Enhancement explains the reasoning in clear language and suggests compliant alternative phrasings

The answer is consistent, auditable, and source-verified—yet delivered through a conversational interface.

Making the Transition

Organizations moving toward Hybrid AI should:

Identify Critical Domains: Audit your knowledge base to categorize content by risk, precision requirements, and regulatory importance. Compliance, safety, legal, and financial domains are prime candidates for deterministic handling.

Establish Guardrails: Define clear policies for when LLMs can operate independently vs. when they must defer to deterministic systems. Create routing logic based on query classification.

Maintain Authoritative Sources: Structure your compliance, policy, and procedural knowledge in formats that deterministic engines can process reliably—rule bases, decision trees, and case libraries.

Design for Auditability: Ensure that every answer from your system can be traced back to its source and reasoning process. This is non-negotiable for regulated industries.

Test for Consistency: Regularly verify that critical queries return identical answers across sessions. Implement automated testing that flags any drift in responses to compliance questions.

Conclusion: Precision When It Matters

LLMs have revolutionized how we interact with information, but they’re tools, not panaceas. In enterprise knowledge management—especially in regulated environments—consistency, precision, and auditability aren’t optional features. They’re requirements.

Hybrid AI acknowledges this reality. By combining the natural language capabilities and contextual understanding of LLMs with the reliability and precision of deterministic systems, organizations can deliver knowledge management solutions that are both user-friendly and trustworthy.

The question isn’t whether to use AI in knowledge management—it’s how to use the right AI for each task. In contexts where being wrong or inconsistent carries real consequences, deterministic models aren’t just important. They’re essential.

Organizations implementing knowledge management systems should assess their content by risk profile and implement routing logic that directs high-stakes queries to deterministic systems while leveraging LLMs for broader knowledge discovery and conversational interaction.

The Modern L&D Challenge: A Perfect Storm

Learning and development professionals are navigating a complex landscape of interconnected challenges that traditional training methods struggle to address:

New Workplace Realities

The shift to hybrid and remote-first work environments has fundamentally disrupted how we deliver training. The spontaneous learning opportunities that naturally occurred in physical offices—the quick question to a colleague, the impromptu mentoring moment—have largely disappeared.

Evolving Workforce Expectations

Today’s workforce, particularly Millennials and Gen Z, approaches learning differently than previous generations:

• Shortened attention spans: Millennials average just 12 seconds of focused attention, while Gen Z manages only 8 seconds
• Preference for just-in-time learning: Modern employees want to learn on the job, similar to how they use GPS for navigation or financial apps for money management
• Resistance to lengthy training sessions: Traditional classroom-style training no longer aligns with how this generation prefers to consume information

The Retention Crisis

Perhaps most critically, research reveals a sobering truth about traditional training effectiveness: humans retain only 25% of what they learn just two days after training, and a mere 2% after one month. This means the vast majority of investment in traditional training programs essentially evaporates within weeks.

The Banking Industry: A Case Study in Complexity

The financial services sector perfectly illustrates the magnitude of these challenges. Banking represents a complex industry struggling with an increasingly challenged salesforce:

Industry Complexity

Modern banking involves:

• Complicated products with variables like loan types, collateral requirements, terms, rates, and payment structures
• Challenging concepts such as loan amortization, debt-to-equity ratios, and compound interest
• Feature overload including digital tools, alert systems, and overdraft protection options

Workforce Challenges

Banks often employ:

• Inexperienced bankers with limited sales skills
• Staff unfamiliar with complex product portfolios
• Teams working with weak sales processes and inadequate systems
• High turnover rates that compound training challenges

This combination creates a cycle where complex products require extensive training, but staff retention issues mean that training investment is frequently lost.

The AI Knowledge Management Solution

AI-powered knowledge management transforms the traditional approach by shifting from periodic training to continuous, contextual guidance. Instead of front-loading employees with information they’ll likely forget, this approach delivers relevant knowledge precisely when needed.

From Product Push to Needs Assessment

Traditional sales approaches often rely on product-focused pitches—the “special rate offer” mentality. AI knowledge management enables a more sophisticated, customer-centric approach by facilitating:

• Comprehensive financial check-ups
• Identification of next-best products or actions to achieve customer financial goals
• Optimal credit product recommendations
• Strategic debt refinancing advice

Addressing Core L&D Challenges

AI knowledge management directly tackles the fundamental problems plaguing traditional training:

Measurable Business Impact

Organizations implementing AI knowledge management solutions are seeing remarkable results:

Operational Improvements

• 60% reduction in agent training time
• 40% reduction in induction training
• 50% reduction in time to competency
• 67% reduction in handle time

Customer Experience Enhancement

• 97% customer satisfaction (CSAT) scores
• 18-30 point increase in Net Promoter Score (NPS)

Business Growth

• 10-15% increase in solution sales
• 100% individual compliance audit trail

Implementation: A Risk-Free Approach

For organizations considering AI knowledge management, eGain offers an innovative “Innovation in Thirty Days” program that eliminates traditional implementation barriers:

No-Risk Trial Structure

• Guided innovation consumption model that is safe, easy, and risk-free
• Your use case, your data, eGain’s product and cloud infrastructure
• Two weeks of discovery and configuration, followed by two weeks of production operation
• Complete freedom to continue or discontinue after the trial

Value Modeling

Organizations can also access comprehensive ROI modeling that:

• Leverages 20+ years of industry experience and consulting best practices
• Delivers personalized models based on actual business data
• Shows how ROI builds over time as solution roadmaps progress
• Builds compelling cases for technology investment

The Path Forward: Learning That Evolves

The future of learning and development isn’t about choosing between traditional training and AI knowledge management—it’s about creating an integrated ecosystem where both approaches complement each other. Traditional training remains valuable for foundational knowledge, soft skills development, and cultural onboarding. AI knowledge management fills the critical gap by ensuring that learning translates into effective action when it matters most.

Key Takeaways for L&D Leaders

1. Acknowledge the retention reality: Accept that traditional training alone cannot solve the knowledge retention challenge
2. Embrace just-in-time learning: Align with how modern workforces prefer to consume information
3. Focus on application: Shift from knowledge transfer to knowledge application
4. Leverage technology: Use AI to deliver contextual guidance that bridges the knowing-doing gap
5. Measure what matters: Track behavior change and business outcomes, not just training completion

Getting Started

For L&D professionals interested in exploring AI knowledge management further, several resources are available:

• eGain Knowledge Academy: Offers three levels of certification with best-in-class courses on knowledge management from industry experts, completely free at university.egain.com
• Value modeling sessions: Understand the potential ROI for your specific organization
• 30-day innovation trials: Experience the technology risk-free with your own use cases and data

The future of workforce development lies in the seamless integration of learning and doing. AI knowledge management doesn’t replace the human element in learning—it amplifies it, ensuring that every training investment translates into improved performance when employees need it most.

To learn more about implementing AI knowledge management in your organization, visit the eGain Knowledge Academy or explore their risk-free innovation trial program. The future of L&D is here—and it’s more intelligent, more contextual, and more effective than ever before.

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?

The New Reality: We’re All AI Businesses Now

Today’s businesses aren’t just experimenting with AI—they’re hiring for AI skills, training existing teams on AI tools, and automating processes at an unprecedented pace. The operational layer of every organization now runs on AI capabilities, particularly in customer service where the impact is most immediately visible.

Consider this: generative AI has become 33 times less expensive in just two and a half years. To put that in perspective, if something cost $10 in 2022, it now costs just 30 cents. While Moore’s Law says computing costs halve every 18 months, AI costs are halving every six months. This isn’t just a trend—it’s a fundamental shift that makes AI-powered customer service not just possible, but inevitable.

The Hidden Problem: Knowledge Chaos

Here’s where most AI implementations hit a wall. Picture generative AI as a brilliant new college graduate—highly capable, eager to help, but knowing absolutely nothing about your business. This AI can read whatever you give it and solve problems effectively, but there’s a critical requirement: the knowledge you provide must be trustworthy, consistent, and easily accessible.

The harsh reality? Most businesses have their knowledge scattered across content silos—SharePoint, Confluence, CRM systems, websites, and countless other repositories. Employees navigate this chaos by talking to each other, working around duplications and inconsistencies. It’s messy, but humans adapt.

AI doesn’t adapt the same way. Feed it contradictory or outdated information, and you’ll get garbage answers. This isn’t an AI problem—it’s a knowledge management problem that AI has made painfully evident.

The Trust Imperative: Building Knowledge Infrastructure That Works

Gartner made an unprecedented prediction recently, stating with 100% certainty (something they’ve never done before) that without a modern knowledge management system, AI tools simply won’t deliver results. This underscores a fundamental truth: trusted knowledge infrastructure is the foundation of successful AI implementation.

What makes knowledge infrastructure “trusted”? It requires two critical attributes:

1. Trust in Content

• Single source of truth: No conflicting versions or duplicate answers
• Contextual relevance: Understanding not just what users ask, but why they’re asking
• Transparent reasoning: Showing how answers were derived
• Collaborative feedback: Allowing users to rate and improve responses

2. Consumability

• Conversational interfaces: The most natural way humans consume knowledge
• Zero-friction access: Knowledge should be so easy to find that employees “trip over it”
• Integration with workflow: Embedded directly into the tools agents already use

The Architecture of Success

A modern knowledge infrastructure centralizes content from all silos, synthesizes it into consistent knowledge, and delivers it through intelligent APIs to both human agents and AI systems. This isn’t just theory—companies implementing this approach are seeing dramatic results.

The magic happens through AI-powered synthesis tools that can:

• Aggregate content from multiple sources automatically
• Check for duplications and inconsistencies
• Ensure compliance with company policies
• Structure information for optimal AI consumption
• Reduce knowledge synthesis time by a factor of five

Real-World Impact: The Agent Assistance Revolution

The most compelling application combines trusted knowledge with conversational AI directly in the agent’s workflow. Imagine this scenario:

An AI system listens to customer conversations in real-time, identifies intent and sub-intent, and when confidence thresholds are met, proactively guides the agent through resolution steps. The guidance adapts based on the agent’s experience level—giving seasoned agents high-level direction while providing new agents detailed step-by-step instructions.

This isn’t futuristic thinking. Companies are implementing these systems today, seeing immediate improvements in both efficiency and customer satisfaction.

The Bold Promise: 75% Cost Reduction

Here’s where skepticism typically kicks in. Is it realistic to expect a 75% reduction in customer service costs within two years?

The math breaks down into two phases:

1. First 50% reduction: Achieved through dramatically improved self-service capabilities powered by conversational AI and trusted knowledge
2. Second 25% reduction: Realized by making human agents twice as productive through AI-powered guidance and automation

While this might sound aggressive, consider that one executive who initially dismissed this target as “too aggressive” and aimed for 50% reduction has already achieved that milestone in just over a year.

The Implementation Reality Check

Many organizations have attempted the DIY approach—connecting a RAG (Retrieval-Augmented Generation) engine to an AI frontend. These projects often create exciting prototypes but fail at scale because they lack the enterprise-grade capabilities needed for production deployment:

• Guaranteed content correctness
• Consistency across all touchpoints
• Compliance with regulatory requirements
• Robust prompt management and version control
• Scalable architecture for thousands of content pieces

The Path Forward

The window of opportunity is now. Organizations that begin building trusted knowledge infrastructure today—starting with concrete, manageable steps—will be positioned to capture the full value of AI transformation over the next two to three years.

The companies that will thrive in this AI-driven future aren’t necessarily those with the most advanced algorithms or the biggest datasets. They’re the ones that recognize that knowledge is the new competitive advantage, and they’re investing in the infrastructure to make that knowledge trustworthy, accessible, and actionable.

The question isn’t whether AI will transform customer service—it’s whether your organization will lead that transformation or be left behind by it. The foundation you build today will determine which path you take.

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.