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 BPO Challenge: Scale Meets Complexity

Modern BPO providers face unprecedented challenges. Agent turnover rates reaching 35-40% translate to billions in training costs annually, while clients demand faster resolution times and higher satisfaction scores across multiple channels. For companies managing hundreds of clients simultaneously, maintaining knowledge consistency and ensuring regulatory compliance becomes exponentially complex.

The traditional approach of siloed knowledge bases and manual training processes simply cannot scale to meet these demands. Agents struggle to find accurate information quickly, leading to longer handle times, increased transfers, and frustrated customers. This is where AI-powered knowledge management becomes transformative.

Real-World Transformation: The Power of AI Knowledge Integration

eGain’s AI Knowledge Hub has demonstrated remarkable results. One major telecommunications provider achieved a 70% deflection rate for incoming calls through AI-powered virtual assistance, while simultaneously reducing case handling time by 25%. The impact on agent performance was equally impressive, with engagement scores jumping to 92% compared to the industry benchmark of 67%.

A leading health insurance company leveraged eGain’s platform to reduce agent training time by 33% during the challenging transition to remote work. With over 2,000 agents requiring immediate upskilling for complex health insurance queries, the AI-guided knowledge system enabled rapid competency development without compromising service quality.

Perhaps most significantly, a multinational financial services provider saw First Contact Resolution rates improve by 36% while slashing training time by 40%. These improvements directly translated to enhanced customer satisfaction and substantial cost savings.

Strategic Advantages for BPO Leaders

The eGain AI Knowledge Hub delivers three critical capabilities that address core BPO challenges:

AI-Guided Agent Support: Real-time guidance helps agents navigate complex scenarios, effectively making every agent perform like the organization’s best performers. This dramatically reduces the impact of high turnover rates.

Automated Content Management: The platform’s collaborative framework enables distributed content creation with automated quality workflows, ensuring knowledge remains current and accurate across all BPO client programs.

Predictive Analytics: Advanced analytics identify knowledge gaps and optimization opportunities before they impact service delivery, enabling proactive improvements.

The Competitive Edge

For BPO providers competing in an increasingly AI-driven landscape, knowledge-powered customer engagement isn’t just an operational improvement—it’s a strategic differentiator. Companies implementing comprehensive AI knowledge platforms report not only immediate operational benefits but also enhanced client retention and new business acquisition.

The future belongs to BPO providers who can seamlessly blend human expertise with AI intelligence, delivering the empathy and nuance clients expect while achieving the efficiency and consistency that drives profitability. In this transformation, AI-powered knowledge management serves as the foundation for sustainable competitive advantage.

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?

As a consumer searching for content or ideas, generative AI offers the promise of a clear, concise response that can be clarified or refined, albeit with a chance that it may be incorrect or out of date.

Business needs, especially for customer service, are different from consumer needs. The customer service department must give consistent responses to a high volume of questions, which must be accurate, up-to-date, compliant with regulations, and understandable to avoid callbacks or escalations. A business will also need to know trends in the questions asked, what responses are working best, and if there are new types of questions.

To assess where generative AI can help with CX, it helps to look at the corporate content landscape, which consists of the following types:

Curated Content: This includes the knowledge base and source documents. Curated content is supposed to be accurate, up-to-date, approved, under change control, etc. This should be the source of truth for the organization.

Documented Content: This can include web sites, intranet, communities etc. The content might have been accurate and even approved when published (apart from opinion pieces or external content) but may not have been looked at since. For example, web sites might contain details of products that are no longer supported or press releases that are out of date. Documented content is often required to fully respond to customer enquiries which means advisors may need to search or browse different sources to augment curated content.

Generated Content: This is information from the web or other sources that forms part of the customer interaction. This is typically helpful ‘human-generated’ content that advisors have picked up and believe to be helpful. Generative AI can augment this type of content generation.

All three types of content are often needed in Customer Service but where they can be used and how they need to be treated, when using generative AI, are different. Below are ten use-cases that not only illustrate how it is done but also explain how to derive tangible benefits from generative AI in the customer contact center.

1. Creating effective prompts from knowledge artifacts

Generated content depends heavily on the prompts used to produce it. Where the knowledge management process has involved documenting artifacts such as the vision for the solution, the desired experience when using the system, the brand values, the tone and the writing style, and the tone of voice for target personas, generative AI can be used to build more effective prompts so that the generated material has the right content and tone of voice for the intended target audience.

2. Identifying likely questions

When a new knowledge base is developed or where there is a need to prepare content for new products or services prior to release, guessing what questions might be asked takes a lot of time and is often inaccurate. This results in creating and approving answers for questions that are never asked. With a suitable prompt, the sorts of questions that the target customer segment is likely to ask about the product or service can be generated. These can be used (possibly together with generated answers) for the first version of a knowledge base which can then be expanded with analytic insights as new questions are detected.

3. Generating draft content for knowledge base articles

Knowledge authors often struggle with the process of turning sprawling compliance-type documents into something that can be used by advisors. With suitable integration, workflows and controls, content can be generated from curated sources and the response checked before the draft is circulated to appropriate experts for approval as part of the knowledge workflows.

As well as creating draft content from curated sources, additional draft content can be generated from documented content and community content. This typically requires some content pre-processing so that it can be passed into a generative tool to constrain the responses and may still need to be checked by an SME rather than getting passed directly to a customer.

4. Augmenting search results and VA responses with generated content

Where a search of available content, either through a VA or web self-service site, does not give a high confidence result, generated content can be used as an additional step before escalation to human-assisted service. Where the question relates to curated content there would need to be a way to pass the necessary content along with the prompt and validate the resulting generated content. When external content is used as a source, the generated content may be used for handling the query with a disclaimer noting that the response has been generated, while pointing to external reference sources.

5. Re-purposing content into different styles or forms

Re-styling an entire knowledge base is a large task, which is often neglected. Generative AI can help automate it when used as part of content workflows. For example, generative AI could perform the bulk of the work in creating customer facing content from advisor-facing content to supplement self-service or a VA.

6. Summarizing feedback for content refinement

User feedback can be summarized from multiple sources into actionable tasks, using generative AI, and content can be refined, based on that feedback. Otherwise, authors would need to read every piece of feedback which might mean poring over hundreds of comments every day. Once the feedback has been summarized it can form part of a prompt that instructs generative AI to revise an article to be in line with the feedback.

7. Creating automatic chat responses from approved content

If the customer query has not been successfully handled by a VA dialogue or auto-suggest, then the query is possibly a hybrid of several questions and so a generated solution would probably be a better fit than a traditional knowledge base article. If the suggestion can be used directly this will be a productivity gain where advisors repeatedly type out similar responses rather than reuse the generated answers.

8. Creating useful additional content to support or augment curated content

The memorable parts of customer service interactions are often the small bits of ‘value add’ that advisors drop into the call after the main issue is resolved. Generated content can be helpful in this respect. Generative AI can augment advisors with the most relevant additional information and uplevel the customer experience to that provided by the most informed advisors.

9. Restructuring content drafted by advisors into specific formats for summary or escalation

After-call work can be a significant part of the agent workload – it is the time when they summarize the call and create notes to help the next person in the chain. Generative AI can take a chat transcript or draft notes and create a concise summary to facilitate escalation or transfer to another department. Having a standard approach to these handoffs can be of benefit to the end-to-end resolution process.

10. Creating follow-up correspondence for advisors / supervisors

Where an advisor needs to compose a follow up note (letter or email) including customer data, generative AI can assist by creating the right tone and format and including real-time data passed from the customer record.

The ability to quickly and easily create complementary customer service content and improve clarity and readability of that content helps organizations improve their customer and advisor experiences, while reducing costs. Whilst generative AI is available to all, it will be organizations that have robust Knowledge Management processes and practices and seamlessly integrate generative AI into those processes that will be able to gain the greatest advantage.

The training treadmill is a financial drain for businesses. US companies spent $92.3 billion on it in 2021 alone and have very little to show for it. It is futile for businesses to throw more training at the employee competency problem for the following reasons:

• Humans retain only 25 percent of new information they learn just after two days, according to the forgetting curve theory. In fact, research by the University of Waterloo found retention at a mere 2–3 percent after just seven days.
• Today’s employees are millennials and Gen Z with short attention spans—12 and eight seconds respectively. They would rather learn on the job than sit through training.
• It is hard to teach situational know-how. An example is the ability of a customer contact center agent to diagnose and solve a customer problem or provide them advice, based on a specific symptom or situation.

Hybrid Work Adds to the Challenge

Although many companies have attempted to get employees back to the office with mixed results, the hybrid work model is here to stay. For example, a recent survey of customer contact center agents showed that 76 percent of agents are still working remote. Hybrid and remote work models make training even more challenging, and employees are often orphaned with no fellow employee to turn to for answers if they need help. It also makes onboarding and shadowing difficult, increasing employee turnover, which takes a toll on business performance.

The solution? Push the right contextual knowledge at the right time to employees in the flow of their work. This level of employee empowerment requires knowledge modernization, which can be enabled through a knowledge hub.

The Knowledge Hub

Many enterprises think they have knowledge in their organization. The problem is that so-called knowledge is often legacy or faux knowledge, large documents employees must read to get the answer to a customer query, or dumb keyword searches, where employees must contend with hundreds of hits to reach the answer. Moreover, these knowledge assets are siloed, outdated, and inconsistent, which encourages employees to stop using them.

Implemented as a hub, modern knowledge management eliminates silos and becomes a single source of trusted information. It goes beyond documents and search to include policies, procedures, compliance, and functional expertise (step-by-step guidance on how to resolve a customer problem or execute an account opening process, or recommend a health plan), served up in the context and flow of work—either on-demand from employees or proactively.

Knowledge Hubs Complement Training

Some organizations leverage knowledge hubs to complement training, transforming the experiences of all stakeholders alike—customers, employees, business managers, and others. Here are some examples:

• Leading BPO provided its 8,000 client services employees with the eGain Knowledge Hub™ to handle millions of interactions from hundreds of its business clients, while cutting agent churn to half of the industry average.
• A health insurance company reduced employee training time by 33 percent and sustained agent performance—even when 2,000 of them had to transition to remote work when the COVID-19 pandemic struck. They also consolidated knowledge from 17 disparate systems into a single hub for consistent, knowledge-guided service.
• A mammoth federal government agency reduced case handling time by 25 percent and boosted their employee engagement score to 92 percent versus their industry benchmark of 67 percent.

Already an essential complement to training, the modern knowledge hub has become even more important in the era of hybrid work. Get your organization on the knowledge bandwagon to take your employee and business performance to the next level.

Originally published on ATD

Check out the details below to learn how customer contact centers are the sweet-spot use-case for multiskilling employees.

The Plight of the Contact Center Agent

It is no secret that the job of a contact center agent is no walk in the park. As self-service technologies improve, automating routine interactions, contact center agents are getting the brunt of the work handling complex queries. This means agents need to be multiskilled to be successful. For example, they should be able to handle a variety of interaction channels and possess broad and deep product and functional knowledge.

The Challenge

Contact centers have long employed specialists rather than generalists—for example, hiring agents who specialize in handling a specific interaction channel (messaging, chat, phone), a specific function (service, sales), a specific product (home mortgage, CDs), and so on. These agents would then go through onboarding and training, focused on specific areas before being deployed in the contact center. While this specialty or single-skill approach has some benefits, it is too complex, risky, and expensive to implement and optimize as business needs change.
Multiskilling provides more flexibility and better utilization of contact center agents. But how do you do it at scale? The answer lies in implementing modern knowledge as a hub. A modern knowledge hub can help multiskill agents easily, cost-effectively, and at scale. It reduces the need to hire and train specialists and reduce or eliminate the need for complex skills-based routing, while maximizing the use of all agents in the contact center.

What Is a Knowledge Hub?

The knowledge hub includes and orchestrates information and content management, profiled access, intent understanding, search methods, reasoning, compliance, analytic insights, integrations with existing systems for 360-degree view and context, regulatory compliance, and conversational and process guidance (next best thing to say and do) in one platform. This guidance collects and curates trusted working knowledge from experts in all the required skill domains that the agent needs to master. Eliminating inconsistent and disconnected silos, the knowledge hub acts as an employee skills hub, serving up trusted data, information, content, and guidance in the flow of work to all agents in the contact center both proactively and on demand. This is multiskilling on an elevated level, where agents get access to not only the breadth but also the depth of skills across many domains.

Real-World Examples

The proof of knowledge-powered multiskilling is in the value pudding, which is nothing short of sensational! Here are some examples from our enterprise clientele:
Premier mobile service provider reduced training time by 50 percent across 10,000 contact center agents and associates in more than 600 retail stores, while improving first-contact resolution (FCR) by 37 percent and their brand loyalty by 30 points, as measured by the net promoter score (NPS). Their customer-facing workforce can now handle any customer contact, sales, or service!
Health insurance company reduced advisor training time by 33 percent and sustained advisor performance even when their more than 2,000 advisors began working remotely overnight when the COVID-19 pandemic began. Despite not having the “next cube” safety net in the new era of remote work, their advisors have been able to hit all their performance metrics, handling customer contacts across a dizzying array of health insurance offerings, recommending plans and providing member service.
Global bank slashed agent training time from 10 weeks to four weeks and improved first-contact resolution (FCR) by 36 percent, while enforcing compliance in service and sales conversations and processes.

One of our banking clients said it best: “eGain Knowledge enabled any agent to handle any kind of customer contact,” which is truly the holy grail of contact center operations! While we discussed the contact center use-case in this post, the knowledge hub can be leveraged to multiskill employees in other business functions as well. This will keep the workforce interested, motivated, and engaged.

Gain the edge with knowledge by developing a multiskilled workforce!

Originally published on ATD