And Why Trusted Knowledge Is the Real Foundation for AI Success Most AI reliability problems aren’t model problems. They’re knowledge problems. Consider Millennium Tower, the “Leaning Tower of San Francisco.” When it opened in 2009, it looked flawless from the outside, but homeowners began noticing signs that something wasn’t right. Balls rolled across kitchen floors…
Executive Summary Private equity firms face a distinctive and high-stakes challenge when operating portfolio companies with established brands and loyal customer bases: how to transform legacy, labor-intensive cost structures through AI-powered automation without disrupting the customer experience that underpins the entire value proposition. To illustrate the financial stakes: a 500-agent contact center with fully-loaded agent…
Jensen Huang knows how to simplify complexity. At GTC 2026, he described the AI stack as a five-layer cake: Energy, Chips, Infrastructure, Models, and Applications. Clean. Memorable. Useful. But there’s a layer missing. Look at where the gap sits: between Models and Applications. That’s exactly where enterprise AI breaks down in practice. You have extraordinary…
Week #1 Developer Series “Don’t tell me how to use your product — show me how to build with it.” — SVP of IT, $5B+ US Credit Union That single sentence opened one of the most memorable customer conversations I’ve had at eGain. The technology executive leading IT at one of America’s largest credit unions…
The promise of AI in customer experience automation has captivated enterprises worldwide. Yet for every success story, there are countless organizations struggling to extract meaningful returns from their AI investments. The gap between AI’s potential and its practical delivery isn’t a technology problem—it’s a fundamental mismatch between how AI solutions are architected and what real-world…
The enterprise software giants want you to believe that AI-powered customer service is a platform problem. Buy their solution, integrate it with your existing systems, train it on your data, and watch the magic happen. Except it doesn’t. Here’s what they won’t tell you: 95% of enterprise AI implementations fail to deliver ROI. Not because…
Six Critical Hurdles in Customer Self-Service AI Deployment — And How Leading Enterprises Are Overcoming Them AT A GLANCE: Key Takeaways for Knowledge-Ready AI Self-Service AI self-service plateaus when knowledge is fragmented, unstructured, or not governed for AI consumption Six distinct hurdles — from knowledge quality to QA blind spots — must be addressed as…
Executive Summary The AI revolution in customer service is not about replacing human expertise—it’s about elevating it. As artificial intelligence transforms customer operations, a critical truth emerges: AI’s capability and impact depend entirely on the quality of knowledge it accesses. Without trusted, curated knowledge, even the most sophisticated AI systems deliver inconsistent, incorrect, or non-compliant…
Key Takeaways Business Context: Customer service requires generative AI applications that are accurate, compliant, consistent, and measurable—fundamentally different from consumer use cases Content Foundation: Three content types (Curated, Documented, Generated) each require different treatment when integrating generative AI 10 Practical Use Cases: From creating effective prompts to generating follow-up correspondence, each use case shows how…
By Ashu Roy, CEO, eGain There’s a blind spot in most boardrooms today, and it’s costing enterprises their competitive edge in the AI era. Despite decades of digital transformation, most organizations still fundamentally misunderstand the strategic value of business knowledge. The Knowledge Paradox Walk into any Fortune 500 company and ask who owns customer data….
