Parloa Locks In SAP Partnership to Run AI Agents Inside Service Cloud

Parloa Locks In SAP Partnership to Run AI Agents Inside Service Cloud

AI agents provider Parloa has expanded partnership with SAP to embed its AI agents into SAP Service Cloud, giving enterprises a way to handle customer interactions with more context, continuity, and automation than most standalone systems currently allow.

The deal follows SAP’s strategic investment in Parloa, which came alongside a successful Series D funding round, and the confirmed plans for deeper product integrations and joint go-to-market activity in the months ahead. SAP is also using Parloa internally, having selected the platform to deliver concierge-style IT support to its own workforce.

Goodbye to Disconnected Systems

The partnership addresses a problem that has dogged enterprise customer service for years, which is disconnected systems. These force customers to repeat themselves across channels, slow down resolution times, and leave little room for the kind of personalisation that modern consumers expect. When each interaction is treated as a standalone event with no knowledge of what came before it, neither the customer nor the business benefits.

Under the new arrangement, Parloa’s AI agents and SAP Service Cloud AI agents will work together to connect conversations to real business data and service processes already running inside SAP. The idea is to carry interactions through to resolution rather than passing customers through a series of disconnected touchpoints. Parloa handles the front-end, customer-facing conversations with agents designed to sound human and stay on-brand, while SAP provides the process knowledge, business data, and governance layer that powers the actual resolution.

Malte Kosub, CEO and co-founder of Parloa, said enterprises need AI that works in real conversations rather than extended pilots, adding: “SAP solutions are embedded globally, and SAP Service Cloud is where many companies already manage customer experience. By bringing Parloa into that environment, we’re making it easier to apply what’s possible with effective AI where it actually matters.”

Racing to Own CX AI

Enterprise CX is in the middle of a land grab, with major players racing to combine AI agents, CRM data, and voice channels into unified platforms. Salesforce launched its own native contact centre offering earlier this year, fusing AI agents and live voice into a single system built on top of its Agentforce Service platform, while Microsoft introduced Dynamics 365 Contact Center in mid-2024, and Zendesk has been building out its own CCaaS product since acquiring Local Measure in early 2025.

SAP’s approach differs because it leans on partnerships instead of building every component in-house. The company has already endorsed Coveo‘s AI-powered search and recommendation app for SAP Service Cloud, and the Parloa partnership follows a similar pattern of bringing specialist capabilities into the SAP ecosystem through certified integrations. The Parloa solution will become an SAP Endorsed App, meaning it will carry premium certification with added security testing and measurements against SAP’s cloud operations best practices.

Managing AI After Deployment

Parloa supports the full lifecycle of AI agents, from design and testing through to deployment and ongoing optimisation. Its Agent Management Platform, known as AMP, gives companies control over how AI behaves in production, which is a meaningful feature for enterprises that need consistent performance across high-volume voice and digital channels.

The partnership’s real test will come down to execution. Enterprises are not short of vendors promising AI-powered customer interactions, and most CX leaders have learned to be sceptical of automation claims that sound better in a press release than they do in a live call queue. What Parloa and SAP have in their favour is the combination of Parloa’s conversational AI with SAP’s deep reach into enterprise back-end systems, which could solve the integration problem that makes so many AI deployments feel like expensive experiments rather than operational improvements.