Alchemer launched Iris on September 6, 2026, an AI-native customer feedback platform that unifies surveys, reviews, reputation management, and other listening channels into a single system designed to act on customer signals. For customer support teams, the platform promises to shrink the gap between hearing a complaint and resolving it - automatically triggering follow-ups, responding to reviews, and moving work into the tools agents already use.
Iris combines feedback collection, AI-driven analysis, and workflow automation under one architecture. The company, which serves more than 11,000 mid-market and enterprise customers across 80 countries, said general availability begins in Q4 2026.
How Iris moves from signal to action
The platform embeds AI across the entire feedback lifecycle. A conversational navigation feature lets users describe what they want in natural language, and Iris guides them to the right tool or workflow. An AI Survey Builder generates surveys from a stated objective, while an AI Removal Assistant flags reviews that may violate Google content policies and prepares removal requests.
Action and workflow automation connects insights directly to operations. Iris can respond to reviews, initiate customer follow-ups, and push tasks into the business systems an organization already uses. A human-in-the-loop design means teams decide where AI runs and where human review stays mandatory.
What the leadership says
Martin Mrugal, CEO of Alchemer, said the platform targets a specific operational problem. "The organizations that win won't be the ones with the most feedback or the best dashboards; they'll be the ones that shorten the distance between hearing something and doing something about it," Mrugal said. "We built Iris to help organizations move from signal to action faster."
Ryan Tamminga, Chief Customer Officer, emphasized that automation is meant to extend human expertise, not replace it. "While Iris can automate the feedback-to-action cycle, our goal is to extend human expertise and supplement where needed," Tamminga said.
Customer results on the underlying technology
Alchemer reported that early adopters of its AI capabilities, which now feed into Iris, have logged concrete gains. Malwarebytes identified 15 customer segments where seven had been assumed, informing four product launches and achieving double-digit revenue growth. H&R Block Canada hit a 100% review response rate across more than 900 locations during peak season. Washburn & McGoldrick cut feedback analysis time by more than half.
The launch arrives as customer service leaders face mounting pressure to adopt AI. Gartner research from February 2026 found that 91% of customer service leaders felt pressure to implement AI this year. Kim Hedlin, Director of Research in Gartner's Customer Service & Support practice, said, "Service organizations are entering a period where AI and human expertise must work in tandem. Leaders are not just deploying AI-they are redesigning service models to ensure that technology enhances the customer experience while humans provide context, empathy and judgment."
Why this matters for customer support professionals
Iris targets the operational reality that most support teams live with: feedback pours in from too many channels, and acting on it takes too long. The platform's automation layer - review responses, follow-ups, cross-system workflows - directly affects the metrics support leaders are measured on, including response rates and time-to-resolution. For supervisors evaluating AI for Customer Support, Iris represents a shift from dashboards that report problems to systems that route work. The Gartner data underscores that the question is no longer whether to adopt AI, but how to design workflows where the technology handles volume while agents supply judgment. Professionals looking to build these workflows can explore the AI Learning Path for Call Center Supervisors to understand integration patterns that keep humans in control of automated decisions.
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