AWS Contact Center

Proven migration patterns for accelerating Amazon Connect deployments

Enterprise contact centers struggle to support multiple lines of business (LOBs) with separate IT and operations teams. Business Process Outsourcing (BPO) companies amplify this complexity, managing hundreds of customers with unique requirements. Contact center migration patterns address these challenges, accelerating deployments and simplifying operations.

This post explains five such proven patterns that create a robust foundation for medium to large-scale contact center migrations. While implementing these patterns requires upfront investment, they accelerate the overall migration timeline.

The migration challenge

Consider AnyCompany, which operates seven lines of business. Four LOBs run on their primary legacy platform, while three LOBs, acquired through recent mergers, operate on a different system. The company’s support structure spans two IT teams and three operations teams, each specializing in their respective legacy platforms.

AnyCompany decided to migrate all seven LOBs to Amazon Connect to reduce legacy platform costs, and provide improved customer experience in a secure and scalable cloud-based contact center. The company faces competing priorities that are familiar to many organizations. They must balance speed against perfection, transformation against basic migration, and decide between maintaining certain legacy components or starting fresh.

AnyCompany chose to migrate one of the smaller LOBs first. The initial migration must establish patterns for managing configurations, flows, and integrations across development, testing, staging, and production environments in both Regions. One of their legacy platforms uses proprietary technology and the supporting team requires upskilling to support the contact center after migration.

Migration accelerator patterns

The accelerator patterns build on three fundamental tenets:

  1. Reusability over reinvention: Building reusable components allows teams to develop functionality once and apply it across multiple areas of the contact center, reduce development time, and drive consistency.
  2. Modularity over monoliths: Modular design breaks complex systems into manageable pieces that teams can implement and maintain independently. This approach increases agility and reduces risk during the migration process.
  3. Configuration over customization: Template-based design reduces overall work by defining configuration rather than code to handle unique cases. This approach maintains flexibility while minimizing custom development.

Pattern #1 – Configurable CX interface

Amazon Connect delivers a comprehensive native user interface that empowers customers to configure essential contact center components. These include queues, routing profiles, user management, security settings, and an intuitive drag-and-drop interface for building customer experiences – from automated flows to agent interactions. Enterprise-scale contact centers typically implement multiple entry points in their flows. Core components such as language selection, customer identification, authentication, intent capture, and routing follow similar patterns. However, each experience is dynamically personalized using contact attributes and data stored in Amazon DynamoDB. After deployment, business teams require agility to make intra-day modifications to flow behaviors, including emergency announcements, special messaging, and language updates. Organizations also need robust configuration management across their development, testing, staging, and production environments.

This pattern implements a flexible schema using a NoSQL database like Amazon DynamoDB. The database hosts configurations that dynamically drive application behavior, while secure API operations read and write these configurations. Business users access the system through a secure, role-based web interface that maintains detailed audit trails and supports a flexible configuration hierarchy. Beyond basic flow configuration, the pattern allows key application sub flows – such as surveys, voicemail, and authentication – to be abstracted and pre-configured for consistent use across LOBs. This approach creates reusable components that can be individually tailored for different groups and environments.

This accelerator pattern offers modular implementation flexibility – teams can build it incrementally based on their migration needs. While reducing development and testing efforts for each future migration by weeks, it maintains a consistent customer experience framework. The solution enables personalization and customization through data configuration rather than code changes, streamlining the ongoing management of contact center operations.

Pattern #2 – Agent experience interface

Amazon Connect has an out-of-the-box agent contact handling option called the Agent Workspace. This unified desktop interface lets agents manage customer interactions across voice, chat, and tasks while accessing customer profiles, interaction history, and knowledge bases from a single interface. The system includes collaboration tools, multi-language support, and real-time performance dashboards.

Most enterprise customers configure Agent Workspace to meet their requirements. However, enterprises can also build a custom control panel using the Amazon Connect SDK and API. Customers who choose this option have to rebuild the capabilities native to the Agent Workspace. While this heavy lifting may be justified for some customers who must hyper personalize the agent experience, this pattern can be built for reuse across the different LOBs using a micro-application architecture. Each feature functions as an independent micro-application, enabling isolated development. Organizations can deploy specific features based on their needs and customize them through configuration rather than code changes.

This accelerator pattern reduces custom agent interface development time by months through automated deployment. It enables consistent agent experiences across LOBs, allowing workforce sharing without significant re-training. The modular design simplifies troubleshooting by providing a single application footprint for desktop, network, and server-side issues. Organizations using this pattern report faster feature development cycles and zero downtime while maintaining the flexibility to add capabilities over time.

Pattern #3 – Contact center developer, operations, and security (DevSecOps)

Global enterprise contact centers require multiple Regions, and provision multiple environments (development, testing, staging, production). Contact center workloads can use 10+ AWS services along with Amazon Connect. To innovate rapidly across multiple instances and services organizations plan for automated deployment processes and consistent environment configurations. Similar to any cloud application, a standardized DevSecOps approach reduces risk of configuration drift, security vulnerabilities, and improves time-to-market for new features.

This pattern implements a DevSecOps framework designed for Amazon Connect deployments. It provides infrastructure as code templates using deployment tools like AWS Cloud Development Kit (CDK). It also includes pre-built pipelines that automate the deployment and configuration of Amazon Connect instances, flows, AWS Lambda functions, and related AWS services. The pattern incorporates security controls, testing strategies, and automated validation at every stage of the deployment pipeline. Key components include modular code repositories for Amazon Connect instance management, configuration, and environment-specific infrastructure deployments. The pattern also provides branching strategies, code promotion workflows, and developer guidelines that ensure consistent delivery across environments. It covers automated propagation of code and configuration between environments (which was often a pain point with legacy technologies).

This accelerator pattern reduces technical foundation build time per LOB implementation by weeks, while improving quality and security compliance. It enables organizations to maintain consistency across environments while accelerating feature deployment and reducing human error. The modular design allows teams to adopt components incrementally based on their existing tooling and maturity level. It provides a repeatable framework that scales effectively as organizations expand their Amazon Connect footprint across multiple lines of business.

Pattern #4 – Monitoring and operations automation

AWS services publish metrics and logs to Amazon CloudWatch. You can view and analyze this data to monitor key operational metrics and set up alarms. Large-scale contact centers require monitoring across services, instances, and Regions to maintain operational excellence. Legacy platforms often struggle with visibility across their contact center infrastructure, leading to reactive problem-solving and extended resolution times. Enterprises must maintain service level agreements (SLAs) across multiple lines of business while ensuring proactive identification of potential issues before they impact the customer experience.

This pattern implements a configuration-driven monitoring and alerting framework that provides visibility across the contact center infrastructure. It uses Amazon CloudWatch as its foundation, enhanced with custom metrics and automated alerting through Amazon Simple Notification Service. It includes pre-built dashboards that offer both high-level and detailed views of health, business metrics, and customer experience indicators. The pattern supports threshold-based alerting with configurable notification channels (email, SMS, Slack) and includes an escalation management system for critical issues. Key components are packaged as infrastructure as code like AWS Cloud Development Kit (CDK), enabling consistent deployment across environments and Regions. Business teams can use it to monitor for high wait times, queue flooding, or errors impacting customer experience. IT teams can use it to monitor for system level metrics such as AWS Lambda function failures, API latency, voice quality scores, backend connectivity errors, and to detect and predict anomalies.

This pattern reduces the time required to establish monitoring capabilities, typically saving weeks of development effort per implementation. It enables proactive issue detection and resolution, helping maintain high service levels across all lines of business. The solution’s modular design allows organizations to start with essential monitoring and progressively add advanced capabilities such as voice quality monitoring, and integration with third-party ticketing systems. Importantly, it provides a standardized approach to monitoring that scales effectively as organizations migrate additional lines of business to Amazon Connect.

Pattern #5 – Voice and chat tuning and optimization

Contact centers implementing conversational AI for self-service use cases have to maintain optimal performance of their voice and chat implementations. Chatbots require sufficient sample utterances, nonoverlapping intents, and adequate error handling to drive efficient customer experiences and avoid escalations to live agents. Manual optimization of conversational interfaces by experts can become undifferentiated heavy lifting for experts, making it cost-prohibitive for organizations to maintain optimal performance.

This pattern implements an optimization framework (often generative AI-powered) to automate the analysis and tuning of chat and voice self-service experiences. The solution leverages foundation models to analyze bot configurations against a curated knowledge base of best practices derived from AWS expertise and real-world implementations. The pattern includes automated analysis of intents, sample utterances, slot configurations, and error handling strategies. It provides actionable recommendations through both an interactive web interface and integration with existing CI/CD pipelines. It can process bot definitions for ongoing optimization phases.

This pattern reduces bot optimization time from days to minutes, minimizing the need for lengthy professional consultations while maintaining high-quality at a fraction of the cost. It enables consistent application of best practices across all conversational interfaces while allowing organizations to maintain optimal performance without requiring deep technical expertise. It helps prevent common issues like intent confusion, insufficient training data, and poor error handling. Organizations using this pattern have seen improvements in self-service performance and increased containment rates while reducing escalations to agents.

Considerations and ideas

We recommend evaluating the value and the investment for each of the patterns listed in the preceding sections. Not every accelerator is a fit or a requirement for every customer. If an accelerator is a fit, organizations have the following choices:

    1. Engage AWS Professional Services to implement pre-built accelerators delivered to hundreds for customers to transform their contact centers. This is a good fit for customers who have an appetite and expertise to own, maintain, and enhance these accelerators in house without any incremental recurring cost.
    2. Identify our Amazon Connect partners who provide similar accelerator patterns using a software as a service model with monthly recurring costs for the patterns. This option is a fit for customers with limited in-house software development and maintenance expertise.
    3. Build it in-house. Amazon Connect along with other AWS services provides a rich set of API to facilitate this extensibility.

Additional accelerator pattern ideas include automated functional and load testing tools, routing simulation tools to validate the impact of routing configuration changes, resiliency operations automation, generative AI- based documentation of flows. As you design your contact center migration to the cloud, you may identify additional patterns.

Conclusion

In this post, we described five migration patterns – Configurable CX interface, Agent experience interface, DevSecOps, Monitoring and operations automation, and Voice and chat tuning. By focusing on reusability, modularity, and configuration-driven approaches, AnyCompany cut migration complexity and expedited time-to-value for the subsequent lines of business. Choose the approach that fits your team’s skills, budget, and goals. These patterns laid the groundwork for ongoing improvement and innovation in cloud-native contact centers, positioning AnyCompany to adapt and excel as technologies evolve.

With Amazon Connect, you pay for what you use. There are no upfront payments, long-term commitments, or minimum monthly fees. Should you need help with setting this up, you can get assistance from AWS Professional Services. You can also seek assistance from Amazon Connect partners available worldwide.

Ready to transform your customer service experience with Amazon Connect? Contact us.

Meet the authors

Parind Poi is a Senior Practice Leader at AWS Professional Services. He leads a specialized practice with deep expertise in customer experience (CX) on AWS. Parind is passionate about helping customers modernize their customer engagement workloads on cloud.
Prashant Desai is a Principal Consultant at AWS Professional Services, experienced in designing and migrating large contact centers to the cloud. With over 25 years in contact centers, legacy and cloud. Prashant constantly seeks innovative ways to modernize customer workloads and simplify customer experience.