A first AI deployment in a call center looks like a technical project — integrate telephony, connect CRM, and done. In practice it is an operational project that involves technology, security, agents and managers. This guide summarizes the full list — worth reviewing before kickoff.
Weeks 1-2: Discovery and preparation
Before integration starts, you need to map the existing state. Without this information, deployment will be delayed.
- ☐ Who is my telephony provider? Voipe, Origami, or another?
- ☐ Which CRM do we use? Salesforce, HubSpot, Pipedrive, Monday, or in-house?
- ☐ How many calls do we handle per day? (average + peak)
- ☐ What is the average call length? (sales differs from service, differs from retention)
- ☐ What call types do we have? Initial list — even if not final
- ☐ Who is the technical owner of integration? On our side and on the vendor's side
Weeks 3-4: Technical integration
This is the stage most vendors describe as "easy." In practice it depends on existing infrastructure.
- ☐ Telephony connection — streaming live recordings from the platform
- ☐ CRM connection — bidirectional: pulling customer data + pushing transcripts and summaries
- ☐ Security review — DPA, encryption, audit log
- ☐ Process documentation — document all integrations for reproducibility and knowledge transfer
- ☐ Sandbox testing — run 50-100 test calls before production
Weeks 5-6: Classifier configuration
This stage requires involvement from the call-center team, not just IT.
- ☐ Map real call types — 4-8 types is a common range for a typical call center
- ☐ For each type define: fields to extract, quality score, red flags
- ☐ Calibrate against examples — run 20-30 calls per type and verify the classifier works
- ☐ Review summaries — do they answer what call-center managers actually want to know?
Weeks 7-8: Team preparation
A deployment that does not involve the agents will fail operationally.
- ☐ Notify agents — before the system goes live, not after
- ☐ Explain why — quality, training, not punishment
- ☐ Update employment agreements (if needed)
- ☐ Update privacy policy and customer agreements — on transcription and AI processing
- ☐ Train managers on using the dashboard, alerts and reports
- ☐ Train the QA team on working with system outputs
Week 9: Go-Live
- ☐ Gradual launch — usually start with one team or one call type
- ☐ Monitor accuracy in the first 2 weeks — log false alerts and incorrect classifications
- ☐ Set a weekly review with the internal team and the vendor
- ☐ Document improvement points and plan fixes
Months 2-3: Expansion and optimization
Full platform value arrives after 6-8 weeks of use.
- ☐ Add more call types that weren't in the first stage
- ☐ Calibrate classifiers on real data — there are always things you didn't anticipate
- ☐ Define alerts on critical events — based on emerging patterns
- ☐ Build automated reports for different manager levels
- ☐ Connect additional systems if needed (Slack/Teams for alerts, BI for custom dashboards)
Common mistakes in a first deployment
From the experience of call centers that went through this:
- Waiting for perfection before go-live — deployment improves in real use, not in planning. Gradual launch with real calls exposes problems faster than 3 months of planning.
- Skipping team preparation — the technology will work, but agents will resist. Investing time in explaining is an investment, not a bottleneck.
- Relying only on the vendor for CRM integration — sometimes we need to verify that the CRM API is available, that we have permissions, etc.
- Choosing too many classifiers at the start — 4-5 well-calibrated classifiers are better than 15 that don't work.
A short Discovery call with the vendor helps understand which of these stages will need special attention in your call center.
Get conversation-intelligence insights
Practical writing on call-center performance, QA and coaching - straight to your inbox.
