Customer experience Archives - eGain https://www.egain.com/de/blog/category/customer-experience/ Knowledge-Powered Customer Engagement Thu, 23 Oct 2025 22:43:34 +0000 en-US hourly 1 https://www.egain.com/egain-media/2025/04/egain-favicon-2025.png Customer experience Archives - eGain https://www.egain.com/de/blog/category/customer-experience/ 32 32 Bridging the Knowledge Gap: How Telecommunications Companies Can Transform Customer Experience with eGain https://www.egain.com/blog/bridging-the-knowledge-gap-how-telecommunications-companies-can-transform-customer-experience-with-egain/ Thu, 23 Oct 2025 22:42:48 +0000 https://www.egain.com/?p=35253 In today’s hyper-competitive telecommunications landscape, customer expectations have never been higher. Yet many telecom providers find themselves trapped in a paradox: despite massive investments in contact center infrastructure and CRM systems, customer satisfaction continues to decline. The culprit? A fundamental weakness in knowledge management that leaves agents scrambling for answers while customers grow increasingly frustrated with inconsistent service.

The Knowledge Crisis in Telecommunications

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

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

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

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

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

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

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

The eGain Solution: Knowledge-Powered Customer Engagement

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

AI Knowledge Hub: The Foundation of Trusted Knowledge

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

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

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

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

AI Agents: Automating with Assured ActionsTM

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

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

Conversation Hub: Omnichannel Engagement

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

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

Seamless Integration with Existing Platforms

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

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

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

Proven Results in Telecommunications

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

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

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

The Path Forward

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

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

]]> Why Customer Experience is the North Star for AI ROI: Lessons from MIT’s Sobering Reality Check https://www.egain.com/blog/why-customer-experience-is-the-north-star-for-ai-roi/ Sat, 30 Aug 2025 01:06:26 +0000 https://www.egain.com/?p=34533

As CEO of eGain, I’ve spent the better part of two decades watching enterprises grapple with technology adoption challenges. But the recent MIT study from the Nanda group has crystallized something I’ve been observing in boardrooms across the Fortune 2000: while 95% of AI initiatives are failing to deliver meaningful ROI, there’s one glaring exception that should inform every C-suite’s AI strategy going forward.

The MIT Wake-Up Call: AI’s Promise vs. Reality

The numbers are stark and sobering. Despite billions in AI investments, only 5% of enterprise AI projects are generating significant returns. This isn’t just a technology problem—it’s a strategic misalignment that’s costing organizations both opportunity and credibility in their AI transformation journeys.

The MIT research identified three critical failure patterns that every CXO should understand:

First, the ROI desert outside of customer experience. While most business functions struggle to demonstrate AI value, customer service and CX consistently emerge as the bright spots. This isn’t coincidental—it’s structural.

Second, the enterprise adoption paradox. Employees who effortlessly use AI tools like ChatGPT in their personal lives suddenly become reluctant adopters when faced with enterprise AI solutions. This disconnect reveals fundamental flaws in how we’re designing and deploying AI within organizational contexts.

Third, the scaling chasm. Promising prototypes repeatedly fail to deliver value at enterprise scale, creating a graveyard of pilot programs that never see production deployment.

For business and technology leaders navigating AI investments, understanding why these patterns exist—and why CX breaks the mold—is critical to building sustainable AI strategies.

Why CX is AI’s Natural Habitat

Customer experience isn’t just performing better with AI by accident. Three structural advantages make CX the ideal proving ground for enterprise AI implementation.

The Measurement Advantage

Unlike many business functions that operate with fuzzy metrics and quarterly assessments, customer service runs on real-time, granular measurement. Average handle time, first-call resolution, customer satisfaction scores, agent utilization—every interaction generates actionable data. This measurement-rich environment creates the perfect feedback loop for AI optimization.

When you deploy an AI-powered knowledge assistant or conversation summarization tool in a contact center, you know within days whether it’s working. Agent productivity metrics shift. Customer satisfaction scores move. Call volumes change. This immediate feedback allows for rapid iteration and optimization—something that’s nearly impossible in functions where success is measured quarterly or annually.

The Training Infrastructure Advantage

Here’s where CX’s notorious challenge becomes its AI superpower. High attrition rates in contact centers have forced CX leaders to build sophisticated training, quality assurance, and performance management systems. These aren’t nice-to-have programs—they’re survival mechanisms.

When you introduce AI tools into an environment that already has structured onboarding, continuous coaching, and performance measurement, adoption accelerates dramatically. New agents don’t resist AI assistance; they embrace it as part of their standard toolkit. Contrast this with other business functions where tenured employees view AI as a threat to their accumulated knowledge and established workflows.

The rotating door that frustrates CX leaders becomes an advantage for AI adoption. Fresh agents approach AI-assisted workflows without preconceptions, while comprehensive training programs ensure rapid proficiency.

The Automation Readiness Advantage

Contact centers have been automating processes for decades. IVR systems, routing algorithms, case management workflows—the infrastructure for intelligent automation already exists. Introducing AI-powered enhancements feels like a natural evolution rather than a revolutionary disruption.

Agents are comfortable working alongside automated systems. They understand the value of tools that can surface relevant information, suggest next-best actions, or handle routine inquiries. This cultural and technological readiness dramatically reduces the friction that kills AI initiatives in other parts of the enterprise.

The Knowledge Infrastructure Imperative

The third pattern identified by MIT—the failure to scale from prototype to production—reveals perhaps the most critical challenge facing enterprise AI today. The root cause isn’t technical capability; it’s knowledge architecture.

Most enterprise AI implementations fail at scale because they’re built on fragmented, inconsistent, and often outdated information sources. When your AI assistant is drawing from dozens of disparate systems, conflicting policies, and siloed documentation, the output becomes unreliable at best, counterproductive at worst.

This is where the concept of trusted knowledge infrastructure becomes paramount. Instead of connecting AI directly to every possible data source and hoping for coherence, successful implementations start with a curated, unified knowledge foundation that serves as the single source of truth for AI systems.

The Strategic Imperative for CXOs

For business leaders, the implications are clear:

Start with CX, but don’t stop there. Use customer experience as your AI laboratory. Build competency, demonstrate value, and create organizational confidence in AI capabilities. Then systematically expand to adjacent functions, carrying forward the lessons learned and infrastructure built.

Invest in knowledge architecture before AI tools. The most sophisticated AI system is only as good as the knowledge it accesses. Organizations that prioritize trusted knowledge infrastructure as the foundation for AI initiatives consistently outperform those that focus primarily on AI tools and technologies.

Embrace the measurement culture. CX’s success with AI isn’t just about the technology—it’s about the culture of measurement and continuous improvement. Functions that want to succeed with AI must adopt similar approaches to metrics, feedback loops, and iterative optimization.

For technology leaders, the message is equally important:

Design for organizational context, not just technical capability. The best AI solution is worthless if it doesn’t align with how people actually work. CX succeeds because AI tools are designed around existing workflows, measurement systems, and training programs.

Build for scale from day one. Prototype success that can’t scale is worse than no success at all. Invest in knowledge infrastructure and integration capabilities that can support enterprise-wide deployment.

Focus on user experience, not just underlying algorithms. The enterprise adoption paradox exists because consumer AI tools prioritize user experience while enterprise solutions often prioritize technical sophistication. Learn from CX’s focus on agent experience and workflow integration.

The Path Forward

The MIT study serves as both a warning and a roadmap. While 95% of AI initiatives may be failing today, the 5% that succeed offer clear patterns that can be replicated and scaled.

Customer experience isn’t just leading AI ROI by accident—it’s succeeding because of structural advantages that can be systematically applied across the enterprise. Organizations that recognize this pattern and build their AI strategies accordingly will find themselves among the 5% that deliver meaningful returns.

The question isn’t whether AI will transform business operations—it’s whether your organization will be among those that figure out how to make it work. The answer starts with understanding why customer experience is leading the way and building your AI strategy on that foundation.

For CXOs ready to move beyond AI experimentation toward AI transformation, the path is clear: start with customer experience, invest in trusted knowledge infrastructure, and build the measurement and training capabilities that make sustainable AI adoption possible.

The 95% failure rate isn’t a technology problem—it’s a strategy problem. And like most strategy problems, it has a solution for those willing to learn from what’s already working.

]]>