Accelerating Issue Resolution with Knowledge and AI

# CSS Executive Forums

# Silicon Valley

CSS Executive Forum – Silicon Valley

November 5, 2025

Insights from the CSS Executive Roundtable at ServiceNow, Santa Clara, October 29, 2025

At the latest CSS Executive Forum, support and engineering leaders gathered for an interactive roundtable focused on “Accelerating Issue Resolution with Knowledge and AI.” Moderated by Brett Austad of Google, with Hamid Haidari of RCG and Avi Karnon of Cribl facilitating, the discussion brought together Customer Service and Support (CSS) leaders to explore how modern knowledge systems and AI are transforming the way organizations resolve issues — faster, smarter, and more predictively.

Setting the Stage: From Reactive to Predictive Support

The session opened with a shared aspiration: to move beyond reactive case handling toward proactive and predictive support, powered by AI-driven insights and intelligent knowledge ecosystems.

Participants discussed where issue resolution typically breaks down — fragmented systems, inconsistent knowledge ownership, and limited collaboration across support, engineering, and product functions. The group also reflected on how AI can be a bridge — enhancing discovery, contextual search, triage, and summarization — but only if fueled by high-quality, continuously maintained knowledge.

What’s Working Well

Despite ongoing challenges, the group recognized meaningful progress over the years:

External knowledge and self-help: Customers increasingly resolve issues through community forums, documentation, and self-service portals. This culture of externalizing knowledge has significantly reduced dependence on reactive support.

AI-driven summarization and insights: Automated root-cause analysis, case summaries, and knowledge article generation help agents spend less time on repetitive tasks and more on value-added work.

Sentiment and escalation scoring: Predictive models identify early warning signs in customer tone and behavior, enabling faster intervention.

A maturing mindset: Compared to five or ten years ago, support organizations have evolved into powerful, insight-driven teams that leverage data and AI to improve both customer and agent experiences.

Where There’s Opportunity

The discussion also surfaced several recurring gaps holding organizations back from realizing AI’s full potential:

Siloed knowledge across support, product, and engineering limits reuse and slows down root-cause discovery.

AI strategy vs. business strategy: Many organizations still view AI as a technology initiative rather than a business capability tied to customer value and outcomes.

Unclear metrics: Measuring AI’s impact on resolution time, CSAT, and operational efficiency remains complex and inconsistent.

Skepticism and adoption barriers: Both customers and agents can be hesitant to trust AI recommendations, especially when transparency is limited.

Governance and maintenance gaps: Without clear ownership, knowledge and AI models degrade quickly — the classic “garbage in, garbage out” challenge.

Deep Dive: Governance and Maintenance of Knowledge & AI

The most vigorous part of the discussion focused on governance — widely recognized as the foundation for sustainable AI success. Participants agreed that while many organizations have launched AI pilots, few have the structure or processes to govern knowledge and AI at scale.

Pain Points Identified

Knowledge curation is often ad hoc, without formal roles or KPIs.

Accountability erodes as teams and ownership shift.

Feedback loops between product, support, and AI systems are weak.

Leadership enthusiasm doesn’t always translate into firsthand understanding or long-term sponsorship.

Recommendations from the Group

Establish a dedicated Knowledge & AI Governance Program with explicit accountability for quality and lifecycle management.

Ensure ongoing accountability and treat knowledge as a living asset requiring continuous care across functions.

Tie governance to business outcomes such as faster resolution, fewer escalations, and improved employee experience.

Engage leadership directly with AI, encouraging executives to “drink their own champagne” and build firsthand empathy for how it works.

Closing Reflections: Human + AI Symbiosis

The session concluded with a shared understanding: AI is not replacing human expertise — it’s amplifying it. By automating routine tasks and surfacing contextual insights, AI enables agents and engineers to focus on creativity, problem-solving, and empathy.

Participants left with one actionable question: “What’s one change you’ll take back to your team to make knowledge and AI work better together?”

Key Takeaways

Knowledge is the foundation; without it, even the best AI models fail.

AI’s value lies in augmentation, not automation alone.

Governance and accountability are essential for lasting success.

Leadership engagement must go beyond vision — it requires hands-on experience.

Cultural change: fostering trust in AI among agents and customers is as vital as technology itself.

Join the Conversation

If you found these insights valuable, subscribe to the CCO Perspective Newsletter for more content on the future of Customer Success, Service, and Support (CSS).

You’re also invited to join our growing community at community.successlab.us — where CSS leaders collaborate, exchange best practices, and explore how AI, automation, and innovation are transforming customer experience.

We’d love to host roundtables and discussions on key AI-driven topics in CSS, both in the San Francisco Bay Area and expanding to other regions.

I look forward to continuing these conversations and diving even deeper with industry leaders at upcoming CSS Executive Forums.

— Omid Razavi, Founder, SuccessLab