... THE TRUE PINNA- CLE OF SUCCESSFUL CUSTOMER SERVICE AND SUPPORT, ENABLED BY AGENTIC AI, IS WHEN THE CUSTOMER DOESN ' T NEED TO REACH OUT AT ALL.
This is one of the highest costs a service business can incur. Direct operational costs range from $ 200 to $ 500. But the Technology & Services Industry Association( TSIA) reports that the true cost when factoring in vehicle depreciation, labor burden, and opportunity costs can exceed $ 1,000 per incident.
Multimodal AI strengthens that decision point. Here’ s how.
• AI agents: Analyzes visual evidence, matches error states to known failures, validates environment or configuration issues, and predicts whether a field visit is truly required.
• Human agents: Makes the final determination, communicates next steps to the customer, and manages expectations.
Across industries, organizations using multimodal visual AI report an average of 19 % reductions in avoidable technician dispatches, driven by more accurate remote diagnosis and better dispatch decisioning.
THE SEE-SAY-SOLVE FRAMEWORK
In this article I want to introduce the SEE – SAY – SOLVE methodology( see FIGURE 1), an original operational model for applying multimodal AI in contact center environments to improve resolution accuracy and FCR across enterprise contact centers.
• Phase 1( SEE) visual ingestion: The AI ingests telemetry and visual inputs.
• Phase 2( SAY) multimodal contextualization: The system interprets visual signals into structured text.
• Phase 3( SOLVE) execution: The human agent uses these insights to guide the customer, supported by AI-recommended actions.
32 CONTACT CENTER PIPELINE
This model preserves human leadership and judgment while providing AI-powered clarity.
TELEMETRY-BASED PROACTIVE SUPPORT
While the interactions above describe a customer reaching out to us, the true pinnacle of successful customer service and support, enabled by agentic AI, is when the customer doesn ' t need to reach out at all.
... THE TRUE PINNA- CLE OF SUCCESSFUL CUSTOMER SERVICE AND SUPPORT, ENABLED BY AGENTIC AI, IS WHEN THE CUSTOMER DOESN ' T NEED TO REACH OUT AT ALL.
By integrating device and system telemetry into the SEE-SAY-SOLVE methodology, we can move from reactive ticket-handling to proactive problem-solving that connects machine detection directly to human resolution.
• SEE( The silent signal): Instead of waiting for a customer to report a " slow laptop " or " network error," AI agents continuously monitor telemetry streams( e. g., CPU temperatures, application crash logs, battery health cycles).
FIGURE 1
The AI " sees " the anomaly the moment it deviates from the baseline, often identifying that a hard drive is failing days before the user loses data.
• SAY( pre-emptive outreach): Once the signal is caught, the AI triggers a proactive communication flow.
If the issue is critical, the AI prepares a warm handoff summary, translating raw telemetry codes into plain English context. It doesn ' t just say " Error 404 "; it tells the system, " The user ' s primary application has failed three times in the last hour."
• SOLVE( The empowered handoff): This is where the connection to the human agent becomes vital. When the customer engages- perhaps via a callback triggered by the AI- they are never asked, " What seems to be the problem?"
That’ s because the human agent already has the diagnostic data on their screen. They can immediately say, " I see your device flagged a memory failure this morning. I’ ve already ordered the part for you."
This seamless thread from the silent telemetry signal to the AI’ s alert and finally to the human agent’ s resolution of the issue transforms support from a cost center into a trust-building engine.
Industry analysis confirms the value of this shift. McKinsey reports that AI-driven proactive engagement and communication can reduce cost-to-serve by 20 % to 30 % while simultaneously boosting revenue by 5 % to 8 %.