Enterprise customer support teams are being told AI will handle more complex live interactions, but fragmented legacy systems often prevent automated tools and human agents from sharing the same customer context. A partnership between shopping assistant platform Crescendo and digital transformation firm Alorica, announced in June, targets that gap by co-developing AI tools for enterprise voice, chat, and other live customer experience channels.
Traditional contact centers have measured automation success by how many interactions they deflect from human agents. Crescendo CEO Andy Lee argues that metric can mask unresolved problems. "AI changes the equation because it allows us to optimize for resolution instead of containment," Lee said. "An AI-native platform understands context across every interaction, continuously learns from every outcome, and helps customers move forward instead of navigating disconnected systems."
Crescendo's platform is designed to combine customer-facing AI with workforce management, quality assurance, analytics, and performance monitoring in a single system, rather than connecting separate tools. The company says its technology already supports more than 500 AI deployments worldwide. The partnership with Alorica brings in a global workforce of more than 100,000 agents, with the goal of keeping customer context intact when an interaction moves from an AI bot to a human representative.
Voice and live chat are particularly hard to automate because tone, intent, and human judgment matter. Alorica Co-CEO Max Schwendner said the collaboration aims to treat AI as the foundation of CX operations. "Brands that will lead are those that treat AI not as an add-on, but as the operating foundation of how CX is run," he said.
A different approach to AI architecture
Many CRM and CX platforms are adding generative AI features to architectures built long before the technology existed. Lee sees that as a limiting factor. "The limiting factor, in my experience, is the architecture underneath the AI model. When AI sits on top of fragmented systems, it naturally inherits fragmented data, workflows, and accountability," he said.
Crescendo's platform is built as a unified system from the start. That difference, Lee argued, determines whether AI can learn from every conversation and apply that knowledge across channels. "That's the difference between using AI to make an old system a little smarter and building a customer experience system that can actually learn and improve as it operates," he said.
From containment to revenue
The shift also opens a potential revenue opportunity. Customer service interactions often reveal unmet needs, product problems, and churn risks - information that typically stays within the support silo. Lee sees an opportunity to connect that data across interactions so the system can identify follow-up actions such as recommending a different product or addressing a threat before a customer leaves. "When companies resolve the immediate issue and understand what the customer needs next, service strengthens loyalty and creates new growth opportunities," he said.
Lee expects customer experience to become a key test of whether enterprises can translate AI investments into measurable results. "Companies that build the right foundation now will move faster, operate more consistently, and deliver experiences that continue to improve," he said.
Why this matters for customer support professionals
The industry conversation about AI replacing human agents has obscured a more immediate operational problem: ensuring that automated systems and employees have access to the same customer information. Lee said automation and frontline operations often run on separate systems, meaning an interaction loses context when it moves between them. Crescendo's approach is designed to maintain that continuity. For support professionals, that means fewer customers repeating themselves and more time focused on complex, high-value work - not chasing fragments of a case across disconnected tools. If you oversee contact center teams, understanding how AI for Customer Support can preserve conversation history across channels could shape what tech you evaluate next. For supervisors managing live interactions, platforms that lose context between AI and humans are not an improvement - they are a new version of the old problem.
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