Amazon Connect Customer Competency

AWS Partner has a company overview presentation that sets the stage for customer conversations about their Amazon Connect Customer capabilities and showcases AWS Partner’s demonstration capabilities.

Presentation contains information about the AWS Partner’s Amazon Connect Customer capabilities, including AWS specific differentiators, e.g., what is unique about the AWS Partner’s practice that can only be accomplished leveraging AWS.

Overview presentations contain:

• Company history
• Office locations
• Number of employees
• Customer profile, including number, size, and industries of customers
• Overview of Amazon Connect Customer practice
• Notable AWS projects

Please provide the following as evidence:

• Delivery of presentation by a business development executive at the beginning of the validation session. This should be limited to 15 minutes.

AWS Partner has internal mechanisms for maintaining their consultants' expertise on Amazon Connect Customer-related AWS services and tools.

Please provide the following as evidence:

• List of internal and/or external AWS-focused education events lead by AWS Partner staff (e.g. formal training, lunch and learns, meetups, user groups, etc.) in last 12 months.
• Resources provided by AWS Partner to staff for ongoing AWS skills development

AWS Partner must describe how Amazon Connect Customer opportunities are identified, how their sellers are trained to identify and sell those opportunities, and specific demand generation/lead generation efforts associated to their Amazon Connect Customer practice.

Please provide the following as evidence:

• A description on how the AWS Partner engages with customers, their internal sellers, and AWS sellers if applicable.

AWS Partner must describe how and when they engage with AWS sellers and AWS Solutions Architects.

Please provide the following as evidence:

• A verbal description for how and when they engage AWS sellers or AWS Solutions Architects on an opportunity or in the form of a demonstration of the AWS Opportunity Management tool in AWS Partner Central with sales qualified opportunities submitted (sales qualified = budget, authority, need, timeline, and competition fields completed).

AWS Partner must have a process to ensure that there are sufficient Amazon Connect Customer trained personnel to effectively support customers.

Please provide the following as evidence:

• An established training plan including on-boarding processes that identify job roles (sellers, solutions architects, project managers) and required training paths
• A verbal description of methods used to allocate required resources to Amazon Connect Customer projects

AWS Partner has processes for working with customers to determine and define expected outcomes associated with the projects.

Please provide the following as evidence:

• Project deliverable templates or other resources used for project scoping and definition

AWS Partner has processes to determine scope of work with specific criteria defining customer project with expected deliverables.

Please provide the following as evidence:

• Project templates or other resources (e.g., RACI Matrix) used for project scoping and definition

AWS Partner has standard Statement of Work (SOW) templates for Amazon Connect Customer projects that can be customized to customer needs.

Please provide the following as evidence:

• Default SOW template

AWS Partner assigns Project Manager to each project to ensure project remains on time and within budget.

Please provide the following as evidence:

• Documentation to show that Project Managers were assigned to each of the 4 customer example projects.

AWS Partner has processes to document, manage, and respond to requests for changes to the project scope.

Please provide the following as evidence:

• Documentation of change management practices

AWS Partner has a customer acceptance process.

Please provide the following as evidence:

• Example customer training documents
• SOW language describing handoff responsibilities and acceptance criteria

AWS Partner implements customer satisfaction checkpoints as part of the project plan.

Please provide the following as evidence:

• Project plan and customer satisfaction results for milestone-defined checkpoints

The AWS Partner must have a documented AWS Business Plan that explicitly identifies Amazon Connect Customer as a focal point of their partnership with AWS.

The Business Plan must demonstrate:

• Amazon Connect Customer is identified as a strategic priority or key growth area
• Specific goals, investment commitments, and pipeline targets related to Amazon Connect Customer
• Named executive sponsor and practice leadership for the Amazon Connect Customer practice

Evidence expected:

• Screenshot or export of the AWS Business Plan showing Amazon Connect Customer as a focal point

The AWS Partner must have a named Practice Lead responsible for the Amazon Connect Customer practice with dedicated accountability for practice growth, delivery quality, and partner ecosystem engagement.

The Practice Lead must demonstrate:

• Direct accountability for Amazon Connect Customer revenue, pipeline, and delivery outcomes
• Active participation in Amazon Connect Customer partner programs, events, or advisory councils within the last 18 months
• Authority over resource allocation, hiring, and investment decisions for the Amazon Connect Customer practice

Evidence expected:

• Name, title, and LinkedIn profile (or equivalent) of the Practice Lead
• Brief description of the Practice Lead's responsibilities and reporting structure
• Evidence of the Practice Lead's participation in at least 1 Amazon Connect Customer partner event, advisory session, or co-sell engagement in the last 18 months

The AWS Partner must demonstrate active investment in their Amazon Connect Customer practice through dedicated staffing and resource allocation.

The partner must have a minimum of 5 dedicated personnel (full-time or majority-allocated) working on Amazon Connect Customer engagements, including at least:

• 1 Solutions Architect or Technical Lead with hands-on Amazon Connect Customer (inclusive of Amazon Connect Customer Basic) deployment experience
• 1 Delivery/Project Manager with Amazon Connect Customer (inclusive of Amazon Connect Customer Basic) project experience
• 1 Sales/Business Development resource actively generating Amazon Connect Customer (inclusive of Amazon Connect Customer Basic) pipeline

Evidence expected:

• Staffing roster showing names, roles, and allocation percentages (minimum 5 personnel)
• Organizational chart or practice structure showing how the Amazon Connect Customer practice fits within the broader organization

The AWS Partner must provide detailed documentation of how their technical delivery teams for Amazon Connect Customer are structured and organized.

The partner must describe:

• Whether delivery teams are centralized (e.g., developers concentrated in one country/region) or regionalized (e.g., teams aligned to regional sales territories)
• How technical resources are allocated to Amazon Connect Customer (inclusive of Amazon Connect Customer Basic) projects across geographies
• How knowledge sharing and best practices are maintained across delivery teams
• How the partner ensures consistent delivery quality across team locations

Evidence expected:

• Organizational diagram or written description of the delivery team structure
• Description of resource allocation model for Amazon Connect Customer (inclusive of Amazon Connect Customer Basic) engagements
• List of delivery team locations (countries/regions)

The AWS Partner must demonstrate that their technical delivery personnel have the skills to design, deploy, and optimize Amazon Connect Customer solutions. At minimum, each of the following badges and courses on AWS Skill Builder must have been completed by at least 4 technical personnel (Solutions Architects, Developers, or Engineers):

• Amazon Connect Fundamentals Badge
• Amazon Connect AI Fundamentals Badge
• Amazon Connect Communications Specialist Badge
• Amazon Connect Developer Badge
• Amazon Connect Outbound Communications Badge
• Amazon Connect Reporting & Analytics Badge
• Conversational AI Experiences Course (inclusive of NLX)

The 4 completions per badge/course may be achieved by the same individuals across multiple badges or by different individuals — the requirement is based on the absolute number of completions per badge/course, not on unique individuals. For example, if 4 individuals each complete all 7 badges/courses, the requirement is met. Equally, if 28 different individuals each complete 1 badge/course (with at least 4 completions per badge/course), the requirement is also met.

Badges:

• Amazon Connect Customer Learning Plans
• Conversational AI Experiences Course

Evidence expected:

• List of technical personnel who have completed the Amazon Connect Customer technical learning badges and Conversational AI Experiences course, showing at least 4 completions per badge/course
• Badge completion certificates or AWS Skill Builder completion records for each individual
• Date of completion for each badge/course

The AWS Partner must have at least 1 Amazon Connect Customer specific offering listed in AWS Marketplace that demonstrates their delivery capabilities for Amazon Connect Customer solutions.

Acceptable Marketplace offering types include:

• Consulting/professional services offering for Amazon Connect Customer implementation
• Managed services offering for ongoing Amazon Connect Customer operations and optimization
• Solution or accelerator built on Amazon Connect Customer capabilities

The offering must be:

• Currently active and publicly visible in AWS Marketplace
• Specifically focused on Amazon Connect Customer (not a generic AWS consulting offering)

Evidence expected:

• URL to the AWS Marketplace listing with the keywords "Amazon Connect Customer" in the title or description
• Brief description of the offering and how it relates to Amazon Connect Customer delivery

The AWS Partner must demonstrate that they are proactively generating and registering Amazon Connect Customer (inclusive of Amazon Connect Customer Basic) opportunities through the AWS ACE (APN Customer Engagements) pipeline — not merely responding to AWS-sourced leads.

The partner must have a minimum of 10 ACE-registered opportunities with Amazon Connect Customer (inclusive of Amazon Connect Customer Basic) product tags within the last 18 months, of which:

• At least 5 must be Partner-Originated (PO) opportunities
• At least 5 must have reached Qualified+ stage or beyond

Evidence expected:

• List of ACE Opportunity IDs (minimum 10) with: ACE Opportunity ID, Opportunity Name (customer name may be anonymized), Opportunity Type (AWS-Originated or Partner-Originated), Current Stage, Product tags, Created Date
• The validation team will cross-reference these IDs in ACE to verify accuracy

The AWS Partner must describe how and when they engage with AWS sellers and AWS Solutions Architects on Amazon Connect Customer (inclusive of Amazon Connect Customer Basic) opportunities.

The partner must demonstrate:

• Evidence of co-selling or joint customer engagement with AWS sellers on at least 2 Amazon Connect Customer (inclusive of Amazon Connect Customer Basic) opportunities in the last 18 months

Evidence expected:

• Written documentation of the partner's AWS co-sell engagement process for Amazon Connect Customer
• Evidence of co-sell activity (ACE opportunity IDs where AWS-Originated opportunity was accepted, meeting notes, joint presentation decks)

The AWS Partner must demonstrate proactive demand generation and market-facing investment in their Amazon Connect Customer practice.

The partner must provide evidence of at least 3 of the following 4 activities within the last 18 months:

• Published thought leadership content on Amazon Connect Customer topics (blog posts, whitepapers, webinars, conference presentations)
• Customer-facing marketing assets (solution briefs, one-pagers, demo videos) specifically for Amazon Connect Customer offerings
• Public offering for an Amazon Connect Customer solution, accelerator, or managed service
• Joint marketing activity with AWS (co-branded webinar, case study, event sponsorship, or AWS blog feature)

Evidence expected:

• URLs, documents, or screenshots demonstrating at least 3 of the 4 activities above
• Date of publication or activity (must be within the last 18 months)

The AWS Partner must have a documented methodology for assessing customer readiness for Amazon Connect Customer adoption. This methodology should evaluate the customer's current customer experience environment, AI maturity, data readiness, and organizational readiness for Amazon Connect Customer capabilities.

The methodology must include:

• Assessment of the customer's current customer experience platform and readiness for recommending Amazon Connect Customer Basic vs. Amazon Connect Customer based on customer size, complexity, and AI readiness
• Assessment should evaluate customer readiness for rapid conversational AI deployment using Amazon Connect Customer's integrated design tools
• Evaluation of data availability and quality for AI features (e.g., interaction recordings for conversational analytics, knowledge bases for AI-powered agent assist)
• Use case prioritization framework for Amazon Connect Customer features (which AI capabilities to deploy first based on business impact and complexity)
• ROI/business case development for Amazon Connect Customer adoption

Evidence expected:

• Written description of the Amazon Connect Customer assessment methodology
• Assessment template, framework, or playbook used with customers
• Example output from at least 1 customer assessment (customer name may be anonymized)

The AWS Partner must demonstrate awareness and application of responsible AI practices specific to Amazon Connect Customer deployments, including:

• Data privacy and consent: How customer consent is obtained and managed for AI features that process voice and interaction data
• Bias and fairness: How the partner evaluates and mitigates potential bias in AI-powered routing, sentiment analysis, or performance evaluations
• Transparency: How customers and end-users (callers, agents, managers) are informed about AI features in use
• Human oversight: How human review and override mechanisms are built into AI-powered workflows
• Governance of autonomous AI agent actions: How the partner ensures AI agents operating within Amazon Connect Customer do not take unauthorized actions on behalf of customers (e.g., financial transactions, account changes)
• Observability/testing for AI: How the partner leverages Amazon Connect Customer's unified observability, testing, and simulation capabilities to validate AI behavior before production deployment

Evidence expected:

• Written documentation of responsible AI practices applied to Amazon Connect Customer deployments
• Example of how at least 1 responsible AI practice was implemented in a customer deployment

Amazon Connect has expanded from a single product into a set of four agentic AI solutions: Amazon Connect Customer (customer experience), Amazon Connect Decisions (supply chains), Amazon Connect Talent (hiring), and Amazon Connect Health (healthcare). Partners holding the Amazon Connect Customer Services Competency must understand these boundaries to correctly scope engagements and advise customers.

The partner must demonstrate:

• Clear understanding of the Amazon Connect product family and the distinct use cases each product addresses
• Ability to identify when a customer inquiry or use case falls outside the scope of Amazon Connect Customer and into an adjacent Connect product (e.g., a healthcare provider asking about patient scheduling may benefit from Amazon Connect Health)
• How the partner handles cross-product scenarios in their sales and scoping process — including when and how they engage AWS or other partners for adjacent Connect products

Evidence expected:

• Brief written description (1 page max) of how the partner positions Amazon Connect Customer within the broader Amazon Connect product family
• Description of the partner's process for identifying and triaging customer needs that may span multiple Connect products
• At least 1 example of how the partner has scoped or would scope an engagement where Amazon Connect Customer is the primary solution but adjacent products may apply

The AWS Partner must have a documented, repeatable methodology for assessing customer readiness to migrate from legacy customer experience platforms to Amazon Connect Customer (inclusive of Amazon Connect Customer Basic). This methodology must demonstrate structured expertise in evaluating migration complexity, risk, and effort.

The methodology must include:

• Legacy platform discovery and inventory (IVR trees, routing logic, agent configurations, integrations, reporting, recordings, historical data)
• Migration complexity scoring framework (factors: number of queues/skills, IVR complexity, integration count, compliance requirements, multi-site considerations)
• Migration strategy selection (big-bang vs. phased vs. parallel-run) with criteria for when each approach is appropriate
• Risk assessment and mitigation planning specific to Contact Center platform migrations
• Effort estimation model for migration projects

Evidence expected:

• Written migration assessment methodology document
• Assessment template or scoring framework used with customers
• Example migration assessment output (customer name may be anonymized) showing complexity scoring and strategy recommendation

The AWS Partner must demonstrate deep technical expertise in executing contact center platform migrations, including the critical cutover phase that determines migration success.

The partner must describe their approach to:

• IVR/contact flow migration (translating legacy IVR logic, DTMF trees, and routing rules to Amazon Connect Customer contact flows with Customer AI features)
• Agent workspace transition (migrating agent desktop configurations, screen pops, CTI integrations to Amazon Connect Customer agent application)
• Telephony migration (number porting, SIP trunk cutover, DID management, DNIS routing)
• Data migration (historical reporting data, call recordings, customer interaction history, knowledge bases for AI features)
• Reporting & analytics migration (translating legacy reporting configurations, dashboards, wallboards, and KPI definitions to Amazon Connect Customer reporting & analytics — including real-time metrics, historical metrics, contact lens analytics, and custom metrics dashboards)
• Workforce management migration (migrating legacy forecasting models, scheduling configurations, capacity plans, and agent adherence tracking to Amazon Connect Customer Forecasting, Capacity Planning & Scheduling (FCS) — including historical volume data needed to seed forecasting models)
• Cutover planning and execution (parallel-run validation, rollback procedures, go/no-go criteria, communication plans)
• Global resiliency planning (configuring Amazon Connect Global Resiliency (ACGR) for active-active or active-passive multi-region deployments, traffic distribution groups, and automated failover where customer requirements demand high availability beyond a single AWS Region)
• Hypercare and post-migration stabilization

Evidence expected:

• Migration execution playbook or runbook showing the end-to-end migration process (must reference approaches to reporting/analytics migration, WFM/FCS migration, and global resiliency where applicable)
• Cutover checklist template with go/no-go criteria
• Example of a migration cutover plan executed for a customer (anonymized)
• Description of rollback procedures and when they have been invoked
• Example of reporting & analytics migration mapping (legacy report/dashboard → Amazon Connect Customer equivalent)
• Description of approach to seeding Amazon Connect Customer FCS forecasting models with historical data from legacy WFM systems

The AWS Partner must demonstrate working technical knowledge of at least 2 legacy or competitive contact center platforms from which they have executed migrations to Amazon Connect Customer (inclusive of Amazon Connect Customer Basic).

For each legacy platform, the partner must describe:

• The platform's architecture and key components (e.g., Avaya CM/AES/CMS, Genesys Engage/Cloud, Cisco UCCE/UCCX/Webex CC, NICE CXone, Five9 VCC)
• How the platform's routing logic, IVR, reporting, and integrations map to Amazon Connect Customer (inclusive of Amazon Connect Customer Basic) equivalents
• Known migration challenges specific to that platform (e.g., proprietary protocols, data export limitations, licensing dependencies)
• How Customer AI features provide uplift over the legacy platform's native capabilities (e.g., replacing legacy QM with Contact Lens, replacing legacy IVR with agentic self-service)

Evidence expected:

• Documentation showing technical knowledge of at least 2 legacy or competitive contact center platforms
• Platform-to-Connect mapping documentation (legacy or competitive component to Amazon Connect Customer or Amazon Connect Customer Basic equivalent)
• Description of Customer AI feature uplift over legacy or competitive platform capabilities for each platform

The AWS Partner must demonstrate a structured approach to enabling Amazon Connect Customer AI features as part of or immediately following a contact center migration — ensuring that migrations deliver transformational value beyond a platform swap.

The core value proposition of migrating to Amazon Connect Customer is access to AI-native capabilities that legacy or competitive platforms cannot match. Partners in the Contact Center Transformation category must demonstrate they do not simply ""lift and shift"" configurations — they have a deliberate strategy for activating AI features that drive measurable improvement over the previous environment.

The partner must demonstrate:

• A phased AI enablement methodology that defines which Amazon Connect Customer AI features are activated at each stage of the migration (Day 1, Day 30, Day 90, ongoing)
• Criteria for determining which AI features to prioritize based on customer readiness, complexity, and expected business impact
• Experience mapping legacy or competitive platform capabilities to Amazon Connect Customer AI equivalents (e.g., legacy QM → conversational analytics + performance evaluations; legacy IVR → agentic self-service; legacy WFM → forecasting & agent scheduling)
• Approach to managing change with agents, supervisors, and managers as AI features are introduced post-migration
• How the partner measures the ""AI uplift"" achieved — the incremental value delivered by Amazon Connect Customer AI features that the legacy platform did not provide

Evidence expected:

• AI enablement roadmap template or methodology document showing phased feature activation approach
• Legacy-to-Connect Customer AI feature mapping (at least 1 legacy or competitive platform mapped to Amazon Connect Customer AI equivalents)
• At least 1 customer example where AI features were enabled as part of a migration engagement, with before/after metrics demonstrating AI uplift over the legacy environment
• Change management approach for introducing AI capabilities to contact center staff

The AWS Partner must demonstrate deep expertise in designing and deploying AI agents using Amazon Connect Customer AI agent designer and agentic self-service capabilities.

The partner must demonstrate:

• Expertise in Amazon Connect Customer AI agent designer for building first-party and custom AI agents
• Experience designing multi-step, multi-turn agentic workflows that autonomously resolve customer issues without human intervention
• Understanding of orchestrator AI agent patterns, including MCP (Model Context Protocol) tool integrations for connecting AI agents to external systems and data sources
• Experience with guardrails, escalation logic, and human handoff design for agentic workflows
• Understanding of when to use first-party AI agents vs. custom AI agents vs. hybrid approaches

Evidence expected:

• Documentation showing AI agent design patterns and orchestration approaches used by the partner
• Example AI agent architecture showing multi-step resolution flows, tool integrations, and escalation paths
• Description of at least 2 deployed AI agent use cases (customer names may be anonymized) with containment/resolution rates achieved
• Description of guardrails and responsible AI practices applied to agentic workflows

The AWS Partner must demonstrate expertise in designing conversational AI experiences that leverage Amazon Connect Customer's natural language understanding and generative AI capabilities.

The partner must demonstrate:

• Expertise in designing multi-channel conversational experiences (voice, chat, messaging) using Amazon Connect Customer excluding Amazon Connect Customer Basic
• Experience with intent design, entity extraction, conversation flow design, and context management for complex customer interactions
• Experience with generative AI integration within Amazon Connect Customer (Amazon Bedrock-powered responses, dynamic content generation, RAG implementations for knowledge-grounded responses)
• Understanding of voice-specific challenges (ASR optimization, barge-in handling, DTMF fallback, multi-language support)
• Experience measuring and optimizing conversational AI performance (intent accuracy, containment rate, fallback rate, customer effort score)

Evidence expected:

• Conversational AI design methodology or framework used by the partner
• Example conversation flow designs showing multi-turn interactions, context management, and fallback handling
• Evidence of generative AI integration within Amazon Connect Customer workflows
• Performance metrics from at least 1 deployed conversational AI solution (intent accuracy, containment rate)

The AWS Partner must demonstrate expertise in deploying AI-powered agent assist capabilities that augment human agents in real-time during customer interactions.

The partner must demonstrate:

• Experience deploying Agent Assist or equivalent AI-powered agent assist within Amazon Connect Customer excluding Amazon Connect Customer Basic
• Expertise in knowledge base design, curation, and integration for AI-powered recommendations (content ingestion pipelines, knowledge article optimization, RAG architecture)
• Experience with real-time transcription and AI-powered next-best-action recommendations during live interactions
• Understanding of agent adoption strategies for AI assist tools (how to ensure agents trust and use AI recommendations)
• Experience measuring AI assist effectiveness (recommendation acceptance rate, time-to-resolution improvement, agent satisfaction with AI tools)

Evidence expected:

• AI-powered agent assist architecture showing knowledge base integration, real-time processing, and recommendation delivery
• Knowledge base design methodology (how content is structured, maintained, and optimized for AI retrieval)
• Agent adoption metrics or strategy documentation
• Performance measurement framework for AI assist features (acceptance rate, impact on AHT, FCR)

The AWS Partner must demonstrate expertise in the ongoing tuning, optimization, and continuous improvement of AI-powered customer experiences deployed on Amazon Connect Customer excluding Amazon Connect Customer Basic

The partner must demonstrate:

• A structured approach to monitoring AI model performance in production (accuracy drift, intent confusion, hallucination detection for generative AI responses)
• Experience with AI-specific testing frameworks (conversation testing, A/B testing of AI responses, regression testing after model updates)
• A feedback loop process for incorporating real-world interaction data into AI improvement (failed intents, low-confidence responses, agent overrides of AI recommendations)
• Experience with prompt engineering and optimization for generative AI features within Amazon Connect Customer
• Understanding of when to retrain, fine-tune, or redesign AI components based on performance data

Evidence expected:

• AI performance monitoring framework or dashboard showing metrics tracked in production
• Testing methodology for conversational AI and agentic workflows
• Example of at least 1 AI optimization cycle (problem identified → root cause analyzed → fix implemented → performance improved)
• Description of prompt engineering practices and how they are managed across deployments

The AWS Partner must demonstrate expertise in validating, monitoring, and testing AI-powered customer experiences before and after production deployment using Amazon Connect Customer's observability and simulation capabilities.

AI-powered customer experiences require rigorous testing and ongoing monitoring to ensure quality, safety, and compliance. Unlike traditional contact center configurations where flows are deterministic, AI-driven interactions are probabilistic — meaning partners must have structured approaches to validating AI behavior, detecting drift, and preventing unintended outcomes in production.

The partner must demonstrate:

• A structured pre-production testing methodology for AI agents and conversational AI flows, including conversation simulation, edge case testing, and guardrail validation
• Experience with Amazon Connect Customer's AI agent performance metrics (goal success rate, faithfulness score, tool selection accuracy) and how these metrics inform deployment readiness decisions
• A production monitoring framework that detects AI performance degradation, intent confusion, hallucination in generative AI responses, and guardrail violations
• Regression testing practices for validating AI behavior after model updates, prompt changes, or knowledge base modifications
• Approach to A/B testing AI responses and measuring the impact of AI changes on customer experience metrics (CSAT, containment rate, escalation rate)
• How the partner uses simulation and testing tools to validate AI agent behavior across diverse customer scenarios before production deployment

Evidence expected:

• Pre-production AI testing methodology or checklist showing how AI agents are validated before go-live
• Production monitoring dashboard or framework showing AI-specific metrics tracked (goal success rate, faithfulness score, containment rate, escalation triggers)
• At least 1 example of a regression testing cycle (AI change introduced → testing performed → results validated → deployed or rolled back)
• Description of how the partner uses Amazon Connect Customer's testing and simulation capabilities in their delivery process
• Approach to managing AI model versioning and rollback procedures when AI performance degrades

Providing details about who the customer is, their situation and business allow us to establish credibility by providing a degree of authenticity. In addition, this information also allows AWS to verify proper customer alignment to the designation as designations can be aligned with an industry and/or a customer segment.

Provide some background information about the customer; name, industry, size, market segment (Enterprise/SMB/ISV/Startup)?

Note:

• If a public URL for the case study is available, please provide along with your response.
• For anonymous case studies, the customer name can be omitted.

The partner must articulate the customer's critical business challenge that aligns with the specific AWS specialization program's focus area, whether it addresses industry-specific needs, targeted use cases, or specialized workloads. Their documentation must identify both immediate and long-term business risks the customer faced without intervention, supported by quantifiable metrics or concrete business impacts.

The description should establish a clear connection between the customer's challenge and the specialization program's core objectives, demonstrating why this particular case study exemplifies the program's intended scope.

What is the key business challenge for the customer and the risk of not addressing this challenge?

Working backwards from our goals/objectives allows us to formulate an optimal implementation strategy as well as identify the technical solution best suited to meet those goals/objectives. Partners are required to work with the customer to identify what those targets are, but business and technical.

Describe some of the customer's goals and objectives as part of their engagement with your organization.

Each AWS specialization program defines the coverage scope of a domain through definition, distinct categories and/or solution areas. The partner must explicitly articulate how the provided case study meets the definition, category and/or solution area defined by the program by providing substantive evidence demonstrating alignment.

If categories are defined, the case study submission must include a clear mapping between the implemented solution and the chosen specialization category, supported by concrete elements, implementation approaches, and outcomes that validate their solution's fit within the selected category requirements.

Describe how the provided customer reference meets the definitional scope outlined by this competency. If categories are defined, then specify which category corresponds to this customer reference and describe why this reference is a good fit for that category.

Note: The competency definition can be found in the introduction tab.

The partner must demonstrate comprehensive technical expertise in their chosen specialization domain through detailed solution documentation that contains an in-depth analysis of service selection decisions, including rationale for choosing specific AWS services over available alternatives. Their technical solution description should directly reference the architecture diagram and explain each component's role in the overall system, establishing clear connections between customer requirements and technical decisions.

Describe the technical solution implemented. Reference the architecture diagram in the explanation.

Please include the following in your response:

1. Justify each service relevant to the designation domain, outlining the analysis of the alternatives considered and rejected.
2. Describe the integration points between different system components

Every business challenge has multiple solutions. AWS prioritizes customer needs, requiring partners to implement solutions that maximize business value while minimizing cost and complexity.

Describe how your solution optimally solves the customer's business challenge and why it was selected amongst the alternatives?

Please include in your response:

1. Description of the alternatives/approaches/options that were considered before arriving at this solution.

Partner solutions implemented for customers must be production grade and live. We measure this by confirming the AWS Annual Recurring Revenue (ARR) that the solution is driving. Proof-of-concepts are not acceptable customer references.

What is the estimated AWS ARR and confirm whether it meets the designation revenue requirements (if any)?

Note: Any designation specific revenue prerequisites can be found on the designation checklist website.

AWS Partners are required to understand AWS customer opportunity registration and tracking mechanisms implemented through ACE. The data found through these mechanisms enables AWS to expedite the validation of the customer reference.

If available, Please provide the ACE opportunity ID and AWS account ID

Note: Not providing the ACE opportunity ID and/or AWS account ID may introduce delays in the application processing time or lead to requests for more information from the validation team.

The partner must demonstrate implementation of AWS services specifically relevant to their chosen specialization domain. Their solution documentation must detail which specialized AWS services are utilized, how they are integrated, and what advanced features are activated.

For technology partners with productized offerings, documentation must specify how their solution integrates with and leverages specialized AWS services to enhance their core product capabilities. Partners must provide concrete examples of advanced service features implemented and their direct contribution to solving domain-specific challenges.

For industry specific designations that do not highlight domain specific AWS services, this requirement can be WAIVED.

Confirm which key AWS services is leveraged and/or integrated with in this solution and what advanced capabilities of said services were activated?

The partner must demonstrate solution success through quantifiable metrics that align with the case study documentation.

Their submission must identify and detail at least two specific Key Performance Indicators that directly measure business impact and improvement.

Each KPI must include baseline measurements, improvement targets, actual results, and clear methodology for measurement, providing concrete evidence of how the solution enhanced customer operations.

What two (2) specific KPIs were measured to help improve the customer business?

The partner must candidly identify any challenges that were observed during the implementation providing a thorough analysis of the lessons learned from these shortfalls, and specific actions taken or planned to address these gaps in future implementations.

This transparency demonstrates the partner's commitment to continuous improvement and ability to adapt solutions based on real-world implementation experiences.

What were some of the challenges observed during this engagement and what is being done to ensure these challenges are mitigated for future customers?

AWS Partner must submit architecture diagrams depicting the overall design and deployment of its AWS Partner solution on AWS as well as any other relevant details of the solution for the specific customer in question.

The submitted diagrams are intended to provide context to the AWS Solutions Architect conducting the Technical Validation. It is critical to provide clear diagrams with an appropriate level of detail that enable the AWS Solutions Architect to validate the other requirements listed below.

Each architecture diagram must show:

• All of the AWS services used
• How the AWS services are deployed, including virtual private clouds (VPCs), availability zones, subnets, and connections to systems outside of AWS.
• Elements deployed outside of AWS, e.g. on-premises components, or hardware devices.
• how design scales automatically - Solution adapts to changes in demand. The architecture uses services that automatically scale such as Amazon S3, Amazon CloudFront, AWS Auto Scaling, and AWS Lambda.
• how design has high availability with multi-AZ or multi-region deployment. When intentional tradeoffs have been made (e.g. to optimize cost in favor of high availability), please explain the customer's requirements.

Please provide the following as evidence (required for all provided customer examples):

• An architecture diagram depicting the overall design and deployment of your solution on AWS.
• Explanation of how the major solutions elements will keep running in case of failure.
• Description of how the major solutions elements scale up automatically.

AWS expects all Services Partners to be prepared to create AWS accounts and implement basic security best practices. Even if most of your customer engagements do not require this, you should be prepared in the event you work with a customer who needs you to create new accounts for them.

Establish internal processes regarding how to create AWS accounts on behalf of customers when needed, including:

• When to use root account for workload activities
• Enable MFA on root
• Set the contact information to corporate email address or phone number
• Enable CloudTrail logs in all regions and protect CloudTrail logs from accidental deletion with a dedicated S3 bucket

Please provide the following as evidence:

• Documents describing Security engagement SOPs which met all the 4 criteria defined above. Acceptable evidence types are security training documents, internal wikis, or standard operating procedures documents.
• Description of how Secure AWS Account Governance is implemented in one (1) of the submitted customer examples.

Define standard approach to access customer-owned AWS accounts, including:

• Both AWS Management Console access and programmatic access using the AWS Command Line Interface or other custom tools.
• When and how to use temporary credentials such as IAM roles
• Leverage customer's existing enterprise user identities and their credentials to access AWS services through Identity Federation or migrating to AWS Managed Active Directory

Establish best practices around AWS Identity and Access Management (IAM) and other identity and access management systems, including:

• IAM principals are only granted the minimum privileges necessary. Wildcards in Action and Resource elements should be avoided as much as possible.
• Every AWS Partner individual who accesses an AWS account must do so using dedicated credentials

Please provide the following as evidence:

• Security engagement Standard Operation Procedure (SOP) which met all the 2 criteria defined above. Acceptable evidence types are security training documents, internal wikis, standard operating procedures documents. Written descriptions in the self-assessment excel is not acceptable.
• Description of how IAM best practices are implemented in one (1) of the submitted customer examples.

AWS Partner has defined metrics for determining the health of each component of the workload and provided the customer with guidance on how to detect operational events based on these metrics.

Establish the capability to run, monitor and improve operational procedure by:

• Defining, collecting and analyzing workload health metrics w/AWS services or 3rd Party tool
• Exporting standard application logs that capture errors and aid in troubleshooting and response to operational events.
• Defining threshold of operational metrics to generate alert for any issues

Please provide the following as evidence:

• Standardized documents or guidance on how to develop customer workload health KPIs with the three components above
• Description of how workload health KPIs are implemented in (1) of the submitted customer examples.

Create a runbook to document routine activities and guide issue resolution process with a list of operational tasks and troubleshooting scenarios covered that specifically addresses the KPI metrics defined in OPE-001.

Please provide the following as evidence:

• Standardized documents or runbook met the criteria defined above.

Deployments are tested or otherwise validated before being applied to the production environment. For example, DevOps pipelines used for the project for provisioning resources or releasing software and applications.

Use a consistent approach to deploy to customers including:

• A well-defined testing process before launching in production environment
• Automated testing components

Please provide the following as evidence:

• A deployment checklist example or written descriptions met all the criteria defined above.

Establish internal processes regarding how to secure traffic within VPC, including:

• Security Groups to restrict traffic between Internet and Amazon VPC
• Security Groups to restrict traffic within the Amazon VPC
• Network ACL to restrict inbound and outbound traffic
• Other AWS security services to protect network security

Please provide the following as evidence:

• Written descriptions/documents on network security best practices met the criteria defined above.
• Description of how network security is implementation in one (1) of the submitted customer examples.

Establish internal processes regarding a data encryption policy used across all customer projects

• Summary of any endpoints exposed to the Internet and how traffic is encrypted
• Summary of processes that make requests to external endpoints over the Internet and how traffic is encrypted
• Enforcing encryption at rest. By default, you should enable the native encryption features in an AWS service that stores data unless there is a reason not to.

All cryptographic keys are stored and managed using a dedicated key management solution

Please provide the following as evidence:

• Data encryption and key management policy met the criteria defined above.
• Description of how data encryption is implementation in one (1) of the submitted customer examples.

Changes to infrastructure are automated for customer implementation

• Tools like AWS CloudFormation, the AWS CLI, or other scripting tools were used for automation.
• Changes to the production environment were not done using the AWS Management Console.

Please provide the following as evidence:

• Written description of deployment automation and an example template (e.g., CloudFormation templates, architecture diagram for CI/CD pipeline) met the criteria defined above.

Incorporate resilience discussion and advise an RTO & PRO target when engaging with customer. Customer acceptance and adoption on RTO/RPO is not required.

• Establish a process to establish workload resilience including:
• RTO & RPO target
• Explanation of the recovery process for the core components of the architecture
• Customer awareness and communication on this topic

Please provide the following as evidence:

• Descriptions or documents on workload resilience guidance met the three criteria defined above
• Description of how resilience is implementation in one (1) of the submitted customer examples including reasons for exception when RTO&RPO is not defined

Determine solution costs using right sizing and right pricing for both technical and business justification.

Conducted TCO analysis or other form of cost modelling to provide the customer with an understanding of the ongoing costs including all the following 3 areas:

• Description of the inputs used to estimate the cost of the solution
• Summary of the estimates or cost model provided to the customer before implementation
• Business value analysis or value stream mapping of AWS solution

Please provide the following as evidence:

• Description of how to develop cost analysis or modeling with the critical components defined above
• Cost analysis example in one (1) of the submitted customer examples. Acceptable evidence types are price calculator link, reports or presentations on business values analysis

The partner must demonstrate comprehensive technical expertise in Amazon Connect Customer through detailed solution documentation.

Describe the technical solution implemented, with specific focus on the Amazon Connect Customer components. Reference the architecture diagram in the explanation.

Evidence expected:

• Which Amazon Connect Customer features were deployed and why (e.g., agentic self-service, AI-powered agent assist, AI-powered manager assist, conversational analytics, post-contact summaries, forecasting & agent scheduling, performance evaluations, AI agent designer, custom metrics, screen recording, etc.)
• How the Amazon Connect Customer features were configured and customized for the customer's specific use case
• Integration points between Amazon Connect Customer features and other Amazon Connect Customer components (flows, routing, agent workspace)
• Integration points between Amazon Connect Customer features and external systems (CRM, knowledge bases, data lakes, identity providers)
• Justify each Amazon Connect Customer feature selection, outlining the analysis of alternatives considered and rejected

The partner must demonstrate that they went beyond default/out-of-the-box configuration of Amazon Connect Customer features and applied meaningful customization to meet the customer's specific needs.

Describe the customizations and configurations applied to the Amazon Connect Customer features in this deployment, including customizations to:

• Agentic self-service flows, AI agent designer configurations, custom AI agent behavior
• AI-powered agent assist knowledge bases, response tuning, guardrails
• Conversational analytics custom categories, rules, vocabulary, post-contact summary templates
• Performance evaluation scorecards for human and AI agents
• Forecasting models and agent scheduling configurations
• Custom metrics definitions for dashboards and APIs
• Screen recording for rules and policies

Evidence expected:

• Technical documentation showing Amazon Connect Customer configuration and customization details
• Screenshots, configuration exports, or architecture diagrams showing customization depth

The partner must demonstrate expertise in building the data pipelines and integrations required to power Amazon Connect Customer features effectively.

Describe the data architecture and integration approach for the Amazon Connect Customer features deployed, including:

• Data sources: What data sources feed into the Amazon Connect Customer features?
• Data pipeline: How is data ingested, transformed, and kept current? What is the refresh frequency?
• Integration architecture: How do the Amazon Connect Customer features integrate with the customer's existing systems (CRM, WFM, ticketing, data warehouse, identity provider)?
• Data quality: What processes are in place to ensure data quality and accuracy for AI features?

The partner must demonstrate that they measure the performance of Amazon Connect Customer features and have a process for ongoing optimization.

Describe how the performance of Amazon Connect Customer features is measured and optimized in this deployment, including:

• AI-specific KPIs: What metrics measure the effectiveness of Amazon Connect Customer features?
• Baseline vs. post-deployment comparison: How were baselines established and improvement tracked?
• Optimization process: What is the ongoing process for tuning and improving Amazon Connect Customer features?
• Feedback loop: How is agent, manager, and supervisor feedback incorporated into Amazon Connect Customer optimization?

The partner must candidly identify any challenges observed during the Amazon Connect Customer implementation, providing a thorough analysis of lessons learned and specific actions taken or planned to address these gaps in future implementations.

This transparency demonstrates the partner's commitment to continuous improvement and ability to adapt Amazon Connect Customer solutions based on real-world implementation experiences.

What were some of the challenges observed during this Amazon Connect Customer engagement and what is being done to ensure these challenges are mitigated for future customers?

For each customer example submitted under the Contact Center Transformation category, the partner must provide additional migration-specific evidence demonstrating end-to-end migration delivery capability. This requirement validates that the partner has hands-on experience planning, executing, and completing contact center migrations from legacy platforms to Amazon Connect Customer — not just greenfield deployments.

For each applicable customer example, describe:

Source Platform: Which legacy contact center platform was the customer migrating from? (e.g., Avaya Aura/Elite, Genesys Engage/Cloud, Cisco UCCE/UCCX, NICE CXone, Five9, Mitel, Aspect, other)

Migration Scope & Scale: Quantify the migration scope:

• Number of agents migrated
• Number of queues, skills, or routing configurations migrated
• Number of IVR trees or self-service flows migrated
• Number of integrations (CRM, WFM, ticketing, data warehouse) re-established on Amazon Connect Customer
• Number of sites or locations included in the migration

Migration Approach & Justification: Describe the migration strategy selected and why:

• Big-bang (full cutover on a single date)
• Phased (migrating by site, team, or function over time)
• Parallel-run (running both platforms simultaneously during transition)
• Hybrid (combination of approaches)
• What factors drove the approach selection (risk tolerance, business continuity requirements, regulatory constraints, union agreements, etc.)?

Cutover Execution: Describe how the production cutover was planned and executed:

• What was the planned vs. actual downtime (if any)?
• What rollback plan was in place?
• How were agents, supervisors, and managers prepared for the cutover?
• What go/no-go criteria were established?

AI Uplift Over Legacy: Which Amazon Connect Customer AI features were enabled as part of or immediately following the migration that the legacy platform did not provide? Describe the incremental value delivered:

• Which AI features were activated (agentic self-service, conversational analytics, AI-powered agent assist, post-contact summaries, performance evaluations, forecasting & scheduling, etc.)?
• What measurable improvement was achieved vs. the legacy platform (e.g., reduction in AHT, improvement in FCR, increase in self-service containment, reduction in QA evaluation time)?

Time to Value: What was the elapsed time from project kickoff to production go-live? How does this compare to the partner's typical migration timeline for similar scope?

Evidence expected:

• Written responses to all 6 areas above for each applicable customer example
• Architecture diagram showing the before (legacy) and after (Amazon Connect Customer) state
• Migration timeline or project plan showing key milestones and actual completion dates
• At least 1 quantitative metric demonstrating improvement over the legacy platform

For each customer example submitted under the AI-Powered Customer Experience category, the partner must provide additional AI-specific evidence demonstrating production-grade AI deployment and optimization capability. This requirement validates that the partner has hands-on experience designing, deploying, and operating AI-powered customer experiences at scale — not just enabling default AI features out of the box.

For each applicable customer example, describe:

AI Capabilities Deployed: Which specific Amazon Connect Customer AI features are in production for this customer? (Select all that apply and describe each):

• Agentic self-service (autonomous AI agents handling customer interactions end-to-end)
• AI agent designer (custom AI agents built using Amazon Connect Customer's design tools)
• AI-powered agent assist (real-time recommendations, knowledge base suggestions, next-best-action)
• AI-powered manager assist (real-time coaching, automated performance insights, trend detection)
• Conversational analytics (sentiment analysis, custom categories, topic detection, post-contact summaries)
• Performance evaluations (AI-assisted scoring, automated QA)
• Predictive insights (forecasting, capacity planning)
• Custom AI integrations (partner-built AI capabilities integrated with Amazon Connect Customer)

Automation Metrics: Provide quantitative evidence of AI impact:

• Containment rate (percentage of interactions fully resolved by AI without human intervention)
• Autonomous resolution rate (percentage of customer intents resolved end-to-end by AI agents)
• AI-assisted resolution rate (percentage of interactions where AI recommendations were accepted by agents)
• Reduction in Average Handle Time (AHT) attributable to AI features
• Improvement in First Contact Resolution (FCR) attributable to AI features
• Any other quantitative metric demonstrating AI-driven business impact

AI Agent Architecture: Describe the technical architecture of the AI-powered experience:

• How are AI agents designed and orchestrated within Amazon Connect Customer?
• What external systems are connected to AI agents (CRM, knowledge bases, order management, billing, etc.)?
• How are tools and actions made available to AI agents (MCP, APIs, Lambda functions, etc.)?
• What is the escalation architecture (how and when do AI agents hand off to human agents)?
• How is context preserved during AI-to-human handoff?

Guardrails & Governance: Describe the safety and governance mechanisms in place:

• What guardrails prevent AI agents from taking unauthorized actions (e.g., financial transactions above a threshold, account deletions, PII disclosure)?
• How are AI responses validated for accuracy and compliance before being delivered to customers?
• What human oversight mechanisms exist (supervisor monitoring, real-time intervention capabilities)?
• How are AI agent permissions scoped and managed?
• What is the process for updating guardrails as business rules change?

Continuous Improvement Evidence: Demonstrate that AI features are actively maintained and improved:

• Provide at least 1 specific example of an AI optimization cycle: problem identified → root cause analyzed → fix implemented → performance improved
• How frequently are AI models, prompts, or knowledge bases updated?
• What feedback mechanisms exist (agent feedback on AI recommendations, customer satisfaction with AI interactions, failed intent analysis)?
• How is AI performance tracked over time (trending dashboards, alerting on degradation)?

Scale of AI Impact: Quantify the breadth of AI deployment:

• What percentage of total customer interactions are handled or augmented by AI features?
• How many AI agents or conversational AI flows are in production?
• What is the volume of AI-handled interactions per month?
• How has AI adoption grown since initial deployment (trajectory)?

Evidence expected:

• Written responses to all 6 areas above for each applicable customer example
• AI architecture diagram showing AI agent design, tool integrations, escalation paths, and guardrail placement
• Dashboard screenshot or metrics report showing AI performance metrics in production
• At least 1 documented AI optimization cycle with before/after metrics
• Guardrail configuration documentation or governance framework