The Amazon Connect Podcast

Amazon Connect Talent is changing the hiring equation: instead of letting a resume knock candidates out before they ever speak to a recruiter, it gives frontline and high-volume applicants a real chance to be heard. Ayesha Borker, Vikram Modgil, and Logan Gaffney reveal how Amazon is turning its own hiring science, AI, and Connect’s communications foundation into a screening experience built for scale, fairness, and better outcomes.
Ayesha Borker, a principal solution architect specializing in Amazon Connect, has spent eight years helping shape the Connect journey and bringing Talent from concept to market. Vikram Modgil leads worldwide go-to-market for Amazon Connect Talent and brings product-side experience from Amazon Connect and Chime SDK. Logan Gaffney is a senior solutions architect focused on applied AI and communications, supporting public sector use cases and future-facing deployments.
You’ll discover how Talent works from the top of the funnel: recruiters upload a job description, AI helps generate interview questions and evaluations, and candidates are assessed through structured, competency-based interviews. The team explains why this is not a sourcing tool, why it’s focused on frontline and high-volume roles, and why the system intentionally avoids resume-based filtering that can miss qualified people.
We break down the difference between skills and competency, why that matters for retention and performance, and how the product is designed to test for behavioral evidence rather than keyword matches. Vikram also shares the guardrails behind the experience, including accessibility-aware question design, transcript-based processing, and why the product avoids making hiring decisions for humans.
Aisha adds the behind-the-scenes lesson of launching a brand-new category: working backwards from customers, validating the real problem, and resisting the pressure to “just add AI” without solving anything meaningful. Logan pushes the conversation beyond hiring into public sector scenarios, where asynchronous interviews could reduce friction for benefits, licensing, immigration, and other appointment-heavy processes.
If you care about AI that actually solves a business problem, not just another flashy demo, this episode is essential listening. It’s especially valuable for recruiting leaders, customer experience teams, and public sector innovators looking to scale access without sacrificing judgment, empathy, or fairness.
#Hiring
#CustomerService
#Leadership
#FutureOfWork
#Talent
#AI
#AmazonConnect
#AWSConnect
#ConnectTalent
#talentacquisition 
#agentichr
#workforcetransformation

https://aws.amazon.com/blogs/contact-center/be-the-5-what-we-learned-by-shipping-ai-at-scale/
Most AI projects fail for the same reason: teams start with the technology, not the business problem. Brian Dawson and Hannah Bloking reveal why the companies that actually ship to production do something radically different - they work backwards from the customer experience, the data, and the controls they need before they ever reach for a model.
The conversation cuts through the AI hype cycle fast. Instead of chasing the latest acronym - RAG, MCP, A2A, and whatever comes next - Brian and Hannah explain why the real unlock is solving a specific business problem with the right mix of generative and deterministic workflows. They share the mistakes that stall deployments, from giant prompts and bad knowledge bases to teams that assume AI will magically fix broken processes.
You'll discover:
Why "be the five percent" is really about getting AI into production, not just running pilots
How to decide when an AI experience should be conversational and when it should stay simple
Why accuracy depends on the last five to ten percent of undocumented human knowledge
How observability and telemetry expose what customers are really asking for
Why security, legal, and compliance need to be involved early, not at the end
How small, bite-sized releases beat massive AI transformations every time
Hannah brings hard-won lessons from shipping AI at scale inside AWS and working directly with Amazon Connect customers. Brian adds frontline perspective from customer deployments around the world, including the patterns that make businesses move faster once they stop trying to force their problems into an LLM. Together, they show how trust, speed, and scale come from clarity - not complexity.
If you've been told to "do AI" without a clear plan, or you're trying to turn a promising proof of concept into something real, this episode gives you the framework to avoid the trap. Essential listening for customer experience leaders, contact center teams, product owners, and anyone responsible for making AI actually work in the real world.

Amazon Connect’s New Real-Time Tools for Smarter Contact Centers
Amazon Connect is expanding how contact centers report, route work, and manage their teams. In this episode, Douglas and Elias Sayigh break down the latest updates and explain what they could mean for supervisors, administrators, and operations leaders.

They cover enhanced analytics—including AI-generated summaries, more detailed metrics, routing data in the analytics data lake, and root cause analysis in preview. They also discuss quality management improvements, such as importing evaluation forms from PDFs with AI, points-based scoring, and expanded language support.

The conversation explores workforce management updates like shift bidding and agent-level unplanned shrinkage, as well as operational tools for capacity limits, early-media DTMF support, larger data tables, and more responsive outbound campaigns.

If you use Amazon Connect—or want to turn contact center data into faster, more useful decisions—this episode offers a practical overview of what’s new and why it matters.

https://aws.amazon.com/blogs/contact-center/failure-didnt-lose-your-customers-mediocrity-did/
https://aws.amazon.com/blogs/contact-center/deflection-is-dead-resolution-is-king/
Amazon Connect is shipping fast, but the real story is how customer feedback turns into the features you use next. Abhishek Pandey reveals the process behind product feature requests, voice of the customer, and the global system that helps AWS decide what gets built, what gets prioritized, and what gets unlocked with a workaround today.
Douglas sits down with Abhishek Pandey, Worldwide Tech Leader for Applied AI and Solutions at AWS, to unpack how customer ideas move from a solution architect conversation into the product pipeline. If you have ever wondered what happens after you say, “I wish Connect could do this,” this episode shows you the machinery behind it.
You’ll discover:
How product feature requests are collected, bucketed, and routed across Amazon Connect teams
Why 90 to 95 percent of new features are driven by customer feedback
How AWS weighs a single large customer request against themes coming from many accounts
The role of voice of the customer conversations in shaping roadmaps
Why workarounds, GitHub repos, and solution architects often bridge the gap before a feature ships
Abhishek also explains how AWS spots patterns across regions like EMEA, ANZ, and the Americas, and how those signals influence everything from routing and forecasting to newer launches like Amazon Connect Agentic Voices and Agentic CX Designer. He shares how quarterly ideation, PRFAQs, and internal proof of concepts can turn a rough idea into something real when it solves a meaningful customer problem.

A small explanatory video before the onslaught of new content