Introduction
Call centres remain the cornerstone of customer-facing businesses—yet they face mounting pressure to handle higher call volumes, maintain compliance, and deliver exceptional service under tighter budgets. The challenges are clear:
- Enhancing operational efficiency under increasing workloads.
- Improving customer experience while maintaining cost control.
- Ensuring regulatory compliance, particularly with Consumer Duty or the Treating Customers Fairly principles established by the Financial Conduct Authority (FCA) in the UK or the Telephone Consumer Protection Act (TCPA) in the US.
This blog explores how AI-powered automation can help address those challenges using a Databricks Solution Accelerator based on an insurance claims centre use case.
We’ll cover:
- The business drivers for AI automation.
- A detailed walkthrough of the solution architecture on Databricks.
- Dashboards that provide real-time insights to both managers and handlers in an insurance claims centre.
- Practical use cases and how to measure success.
Whether you're just starting with AI or looking to expand automation across your organization, this guide offers actionable insight to help you get started.
Why Automation, Why Now?
Cost Efficiency in Call Centre Operations
Automation in middle and back-office operations is no longer optional—it’s essential. Call centres dedicate a large share of their operational budget to manual processing, call handling, and post-call workflows. AI and automation can reduce these costs significantly, enabling organizations to scale operations efficiently and improve profitability.
Enhancing Operational Performance
Manual transcription and post-call note taking cause delays and inconsistency. AI automation offers immediate improvements:
- Real-time transcription speeds up call processing.
- Data extraction and summarisation reduce after-call work.
- Sentiment detection flags customer dissatisfaction for escalation.
- Process automation ensures agents spend time on complex, high-value tasks.
Ensuring Compliance and Customer Care
Staying compliant is essential, especially under regulations like the FCA’s Consumer Duty in the UK or TCPA in the US. AI enhances compliance through:
- Automated redaction of sensitive information.
- Detection of sensitive topics such as financial vulnerability.
- Audit trails and data governance using Databricks’ Unity Catalog.
From Call to Insight: The Databricks Solution Accelerator
The Databricks Call Centre Analytics Solution Accelerator provides a ready-to-use framework to ingest, process, and analyze call data using AI. It’s built on the Medallion architecture, allowing for scalable transformation from raw audio to actionable insight.
Bronze Layer – Raw Data Ingestion
This is the foundation. It ingests audio files (e.g., M4A format) from cloud sources using Lakeflow Declarative Pipelines —supporting both batch and real-time streaming pipelines.
- Storage: Databricks Volumes to store audio files
- Format Conversion: Convert to MP3 for downstream compatibility
- Governance: All assets tracked via Unity Catalog
Silver Layer – Transcription and Enrichment
This stage prepares the data for analysis:
- Transcription: Using the open-source Whisper model, voice is transcribed into text.
- Metadata Extraction: Duration, speaker identification, and timestamps.
- Enrichment: Attach conversation context to customer profiles.
Gold Layer – AI-Powered Insights & Analytics
The Gold layer delivers value by unlocking AI/ML-driven insights. Using Databricks' built-in AI functions, large-scale models are invoked directly via SQL. This enables provisionless batch inference, where infrastructure is automatically spun up as needed—eliminating idle resources and removing the need for manual endpoint management.
AI Functions in Action:
- ai_analyze_sentiment: Detects call tone (positive, neutral, negative).
- ai_classify: Categorizes calls by topic (claims, billing, renewal).
- ai_extract: Uses Named Entity Recognition (NER) to identify key data (policy ID, names).
- ai_summarize: Produces call summaries to help agents prioritize and take action.
- ai_mask: Redacts sensitive PII for compliance.
- ai_query: Drafts follow-up emails in JSON for automated customer communication.
This simplified pipeline allows teams to process thousands—or millions—of calls with minimal overhead.
Below is an example statement from the solution accelerator, showing how simply it is to use the AI-functions by applying them on a column, such as ‘transcription’, to derive insights.
Visualisation Layer: Dashboards for Strategic and Tactical Insight
After AI insights are generated from call transcripts, they are brought to life through intuitive dashboards built in Databricks SQL. These dashboards serve two distinct user groups: operational managers, who require strategic oversight, and front-line call handlers, who need actionable information in real time.
Manager Dashboard: Driving Strategic Oversight
The manager dashboard is designed to support data-driven decisions at a higher level. It includes the following components:
- Call Volume Trends: A time-series visualisation highlights spikes and dips in call traffic, helping to optimise staffing levels and understand demand patterns.
- Sentiment Breakdown: A pie chart summarises caller sentiment (positive, neutral, or negative), enabling managers to monitor overall customer satisfaction.
- Call Classification Summary: A tabular view categorises the most common topics of inbound calls, helping teams prioritise resource allocation and identify emerging issues.
- Agent Productivity: A bar chart comparing the number of calls handled by each agent supports performance reviews and workforce planning.
- Average Call Duration: Visualised call length trends reveal potential training opportunities and allow leaders to flag inefficient processes.
- Fraud Trends: A bar chart surfaces patterns in potentially fraudulent activity, supporting faster risk response.
- Compliance Dashboard: A table flags calls related to hardship, ensuring alignment with Consumer Duty principles and other regulatory requirements.
Handler Dashboard: Supporting Decision-Making
The handler dashboard equips agents with tools to respond more effectively to the customer:
- Personal Performance View: Agents can drill down into their own metrics—such as call count, sentiment patterns, or average duration—to self-monitor and improve performance.
- Sentiment Alerts: Visual cues quickly highlight dissatisfied or vulnerable customers, enabling handlers to respond more empathetically and escalate issues appropriately.
- Searchable Transcript Table: This interactive table allows agents to find key terms and entities (e.g., names, policies, or complaint keywords) within call transcripts using Named Entity Recognition (NER).
- Case Summary Cards: Automatically generated by AI, these summaries include recommended next steps, sentiment analysis, and any red flags—reducing the cognitive load on agents and speeding up post-call tasks.
Together, these dashboards bridge the gap between operational oversight and daily execution, enabling a smarter, faster, and more compliant call centre.
Use Cases: Expanding the Reach of AI Automation
The flexibility of this solution accelerator makes it applicable to a wide range of real-world challenges. Below are some key use cases and potential enhancements:
1. Intelligent Call Processing
AI models can automatically extract structured data such as policy numbers and names from conversations. This allows for seamless integration with CRM systems by pre-filling forms or initiating workflows—reducing manual input and error rates.
2. Automated Call Classification & Routing
AI can infer the intent, urgency, and sentiment behind each call. This enables intelligent routing to appropriate teams and prioritisation of high-risk or dissatisfied callers for immediate escalation.
3. Real-Time Fraud Detection & Risk Assessment
The current solution can be extended with additional ML components (e.g., XGBoost or Isolation Forest), implemented as callable functions within Unity Catalog. These models can identify:
- Speech inconsistencies that suggest deception
- Repeat callers or suspicious behavioural patterns
- Short-duration calls, often associated with scam attempts
4. Workforce Analytics
Managers can track a range of metrics—call volume per agent, average handling time, SLA adherence, and more. Trends in these indicators can reveal training needs, process inefficiencies, or policy violations.
Measuring Success: KPIs for AI Automation
To evaluate the impact of AI automation in call centre operations, it’s essential to define measurable outcomes. Below is a summary of key performance indicators (KPIs) mapped to typical business objectives:
|
Objective |
KPI |
Example |
|
Reduce overheads |
% cost savings |
Replace manual transcription with AI |
|
Automate workflows |
% reduction in manual tasks |
Auto-generate emails & summaries |
|
Speed resolution |
% reduction in call duration |
Shorter, more effective calls |
|
Detect fraud faster |
% increase in accuracy |
Surface hidden anomalies |
|
Compliance tracking |
% of flagged cases reviewed |
Hardship detection for Consumer Duty |
Databricks Technical Advantage
A Unified Platform for Streamlined Security and Performance
Databricks offers a single, integrated environment that eliminates the need for complex, multi-tool integrations—reducing vulnerabilities and minimizing performance bottlenecks. This cohesive architecture enhances operational efficiency and security across all components of the application.
Centralised Governance with Unity Catalog
- Fine-grained access control for audio and transcripts
- Automated lineage, audit trails, and data masking
- Roles-based access and attribute-based permissions
End-to-End Pipeline Automation
From ingestion to dashboard delivery, use Databricks Lakeflow Jobs to orchestrate:
- Bronze-to-Gold medallion transformations
- AI inference
- Dashboard refreshes in near real-time
Scalable, Modular, Reusable
The architecture supports:
- Ingestion: Batch or streaming.
- Governance: Unity Catalog.
- Processing: Lakeflow Pipelines + AI Functions.
- Serving: SQL Warehouse.
- Visualisation: BI Dashboards or Genie Rooms.
Beyond the Call Centre: Broader Enterprise Transformation
While this solution focuses on call analytics, the same architecture and insights can support wider insurance and financial operations—such as claims, underwriting, collections, fraud detection, client onboarding and voice summarisation. The voice of the customer is a strategic asset - this accelerator serves as an entry point for modernizing how your business functions as a whole.
Conclusion
Databricks' Call Centre Analytics Solution Accelerator gives you everything you need to turn calls into insights:
- AI-powered automation of repetitive tasks
- Reduced resolution times and costs
- Improved compliance and fraud detection
- Real-time dashboards for agents and managers
- Scalable, governed architecture with Unity Catalog
- Strengthened compliance with regulatory frameworks like Consumer Duty.
Ready to get started?
Explore the Databricks Solution Accelerator or speak with your Databricks representative to build your own AI-powered agent workflow today.
