Contact Center Pipeline July 2026 | Page 47

KPIs

FINALLY …

Voice AI is bringing about not just a staffing change but a new operating model. The teams that win will treat their contact center like a modern growth organization:

So, the actionable fix is to ask the engineering team to add retries and a fallback verification path. Having proper instrumentation and call analytics in place helps you diagnose issues at startup speeds.

B. Understand if it is a metric problem versus an execution problem

When a KPI doesn’ t move, it’ s usually one of two things:

1. Metric problem: the definition is wrong or doesn’ t reflect value.

2. Execution problem: you’ re measuring correctly but not addressing the constraint.

It’ s important to know what to fix.

C. Realize that instrumentation matters more in the voice AI era

Call analytics are no longer optional. Modern large language model( LL- M)-based systems make it feasible to:

• Auto-tag intents and outcomes.

• Cluster failure patterns.

• Quantify policy violations.

• Surface the 20 calls that best explain why KPIs moved.

• One metric that defines winning.

• A few guardrails that keep optimization honest.

• Weekly targets and a repeatable operating loop.

• Fast diagnosis powered by call analytics.

We can take lessons from the Silicon Valley playbook to improve our organizations and get compounding gains in customer success.

Amber Sahdev is an engineer focused on voice AI and call analytics for contact centers. He previously worked in customer success at organizations like Salesforce. He is an inventor on patents related to large-scale compliance and monitoring and maintains widely used open-source AI projects. You can find him on LinkedIn.

BOX 2

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Canon’ s service organization has evolved significantly over time, moving beyond a traditional break-fix model toward a more proactive and insight-driven approach.

At the reactive stage, the focus was on responsiveness and resolution efficiency. As operations matured, we began investing in better data visibility and CRM integration, enabling a shift into predictive service, where teams could anticipate common issues, prepare agents with context, and reduce resolution time.

The transition to proactive service came through tighter alignment between service, product, and operations teams.

By identifying repeat issues and systemic friction points, we were able to intervene earlier, resolving problems before customers needed to reach out and reducing unnecessary contact volume.

Today, Canon continues progressing toward a generative model, where service insights are used to inform broader business decisions. Feedback from customer interactions plays a role in shaping product improvements, refining digital experiences, and guiding internal priorities.

A key lesson from this journey is that technology alone does not drive transformation. Progress required cultural alignment, cross-functional collaboration, and a willingness to view service as a strategic source of insight and long-term value.

CUSTOMER ENGAGEMENT

• In the generative stage, success requires trust in service insights and trust in frontline judgment.

Proactive and predictive customer service, enabled by disciplined CRM management, is how the promise of experience-led growth becomes operational.

CRM is the mechanism that connects proactive service to business outcomes by linking customer context to predictive insight and orchestrating timely intervention.

Experience-led growth does not begin with selling more. It begins with listening, anticipating, and acting before customers are forced to ask, accomplished by integrating humans with technology. For this is how you build successful and valuable customer relationships.

Jason oversees Canon ' s service operations, which has maintained BenchmarkPortal ' s Center of Excellence certification for 15 consecutive years, and which ranks in the top 10 % of contact centers globally. He manages over 1,100 service professionals who handle 200,000- plus monthly interactions.

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