Where Does Your Contact Center Fall On The AI Curve?

Take this 2-minute AI Maturity Assessment to benchmark your contact center’s AI implementation and unlock your personalized score + roadmap.

How do agents access the context they need during live support interactions?

They manually search across multiple tools and tabs.

Most info is available, but switching systems slows them down.

Agents use centralized tools, but context isn’t always current or complete.

Agents receive knowledge prompts and support articles during interactions.

Suggested replies, customer history, and summaries are available in real time—though not all agents use them consistently.

Which best describes your current use of automation or virtual agents?

We don’t currently use bots or automation in the support flow.

We use a basic chatbot for FAQs or form-filling.

Automation handles simple tasks like order tracking or password resets.

Virtual agents resolve common issues end-to-end and escalate when needed.

Automation handles a wide range of inquiries with contextual handoffs—but some edge cases still require tuning.

What happens when a customer switches channels (e.g., from chat to phone)?

They start from scratch each time.

Info sometimes carries over, but it’s inconsistent.

Agents can see past tickets, but not real-time conversation context.

Most transitions preserve customer history for the agent.

Customers can switch channels without repeating themselves—though some back-end systems still catch up manually.

How are customer inquiries routed to agents today?

First available agent, no customization.

Routing is based on department or agent skill.

We include some customer data like location or account type.

Routing adapts based on issue type, urgency, or past history.

Real-time context, sentiment, and behavior guide routing—though we’re still expanding logic for edge cases.

How is data from support interactions captured and used?

Agents write notes manually, if at all.

Call recordings or chat logs are stored, but rarely reviewed.

Some transcription or tagging is done after calls for QA.

Transcripts and summaries are generated automatically and reviewed by supervisors.

Real-time summaries and sentiment are logged, shared with CRM, and used for QA and coaching—though adoption varies team to team.

How personalized is your support experience for customers?

Our support experience is consistent but not personalized.

We use names, account details, or past orders in responses.

Segmentation helps us tailor support for different customer types.

Agents see real-time prompts based on behavior and journey stage.

Personalization is dynamic and predictive—but coverage varies by channel or journey phase.

How do you forecast volume and staff your team?

Mostly manual forecasting based on past volume.

We use WFM tools but rely on regular manual adjustments

Historical trends guide our planning, with limited automation.

AI forecasts demand and recommends schedules.

Scheduling adapts automatically to real-time changes—though agent preferences and constraints still require manual oversight.

Why is AI used in your contact center today?

It’s not yet in use.

To reduce ticket volume or speed up response times.

To help agents work more efficiently and reduce hold times.

To personalize experiences and improve CX outcomes.

AI supports loyalty, revenue, and operational insights—though not every department sees it as strategic yet.

How quickly can your team identify and act on CX trends or issues?

Mostly retroactive—problems show up after they’ve escalated.

We review dashboards weekly or monthly.

Some live data is available, but we lack real-time alerts.

Supervisors get real-time alerts and access to agent-level insights.

We monitor CX signals continuously and adjust workflows in real time—though not all insights are automated yet.

How integrated is AI across your contact center operations?

AI isn’t used or is limited to one channel/tool.

We’ve added some AI tools, but they work independently.

AI supports a few key use cases but isn’t connected across systems.

Most support workflows include AI—but not every system is aligned.

AI is embedded throughout our workflows and data flow—but integration with other business systems is still evolving.