Partner Guide
Why CallTower for AI
CallTower helps partners lead AI adoption conversations with confidence by anchoring every discussion in business outcomes, not technology hype.
- 30+ years of UC and CX experience
- Dedicated in‑house AI practise focused on delivering enterprise‑grade communications design, deployment, and optimisation, and customer experience solutions—not third‑party overlays
- Expertise orchestrating best‑of‑breed AI ecosystems, including Genesys, Five9, Parloa, Sestek, and Microsoft
- A consultative approach designed for real production environments and measurable CX outcomes
Lead with Business Outcomes
AI adoption conversations succeed when driven by business need, not technology curiosity.
Contact centres face pressure to:
- Improve customer experience
- Reduce cost
- Scale agent performance
Leading with outcomes helps partners:
- Align on real operational challenges
- Prioritise high‑impact AI use cases
- Tie AI initiatives to executive KPIs such as AHT, FCR, CSAT, and containment
This positions AI as a strategic business enabler, not a feature discussion.
Start the AI Conversation with Pain Points
CallTower recommends guided, consultative discovery to identify where AI can deliver measurable value.
Common Contact Centre Challenges
- Long wait times and high abandonment
- Low first contact resolution
- Agent shortages, attrition, and slow onboarding
- Limited hours versus 24/7 customer expectations
- High volumes of repetitive, low‑value interactions
- Multilingual support requirements
- Inconsistent CX and compliance exposure
- Limited visibility into agent performance and CX
- Rising cost to serve and margin pressure
- Excessive after‑call work and administrative overhead
Each challenge can be mapped to specific AI capabilities and outcomes.
Position AI Without Leading with Technology
Rather than introducing tools first, CallTower frames AI as targeted responses to business challenges.
This keeps the conversation focused on value and outcomes.
Core AI Capabilities
Virtual Agents
- Designed for high‑volume, repetitive interactions
- Always‑on availability
- Multilingual self‑service
- Interaction deflection
Agent Assist
- Real‑time guidance for agents
- Automated summaries
- Compliance support
- Faster time to proficiency
Agentic AI
- Autonomous execution of defined workflows
- Handles resolution, updates, and orchestration across systems
Map Challenges to the Right AI Model
Simple mapping reinforces credibility and prevents over‑engineering.
- Long wait times → Virtual Agents, Agentic AI
- Agent shortages → Agent Assist, Agentic AI
- Repetitive interactions → Virtual Agents, Agentic AI
- Low FCR → Agent Assist, Agentic AI
- Compliance risk → Agent Assist, Agentic AI
- High cost to serve → Virtual Agents, Agentic AI
Align AI to Measurable Business Outcomes
AI initiatives should always connect to metrics that customers already track.
