SupportNinja has launched MomentumLabs, a CX transformation practice that combines its NinjaAI platform with human oversight, workflow redesign and strategic advice. Rather than treating AI as a bolt-on project, the company is positioning transformation as part of the day-to-day CX operation itself.
The launch coincides with recent McKinsey research, which argues that capturing value from AI increasingly depends on redesigning operating models rather than simply adopting new technology. This is particularly relevant to customer service, where adopting an AI-native operating model means changing how work is structured rather than simply adding AI to existing processes.
Why SupportNinja is targeting the workflow
SupportNinja's central argument is that AI deployments can fall short when they are pointed at a single tool or ticket queue rather than the workflow surrounding it. A chatbot bolted onto a broken process can simply move the friction somewhere else rather than remove it.
Because the company already runs CX operations for clients, MomentumLabs can see how the wider operation works, including policies, exceptions and handoffs between people and technology, rather than just the point where AI is applied.
SupportNinja explains that it starts this process with its Signal Report, a diagnostic designed to identify friction and opportunities for AI, automation and process redesign before implementation begins.
Human oversight as part of the pitch
SupportNinja is positioning human oversight as central to how automation scales, rather than as a temporary safeguard. Humans check AI outputs, catch failures and feed corrections back into the system. The company’s CEO Craig Crisler framed the shift as a break from the outsourcing industry's traditional response to growth:
"For decades, the outsourcing industry has answered growth with more headcount… That model doesn't solve the problems underneath the volume; MomentumLabs changes the equation."
As AI takes on more customer interactions, that human oversight becomes more important. AI systems can produce incorrect or unreliable outputs, making decisions about escalation and human intervention part of the operating model rather than an afterthought. AI hallucinations in customer service are one example of where those controls matter.
The evidence so far
SupportNinja points to early results from its own client base. Chatbot deflections, for example, rose for a global fitness brand from 20% to 60%, bot CSAT climbed from 2.5 to 3.6, and the bot now resolves 92% of what it handles. For a benefits administrator, proactive alerts across the claims journey reportedly cut status-check calls by 25%.
It also highlighted a fast-growing Medicare benefits platform which redesigned an inefficient ordering workflow rather than adding headcount. Quarterly order volume doubled from 9,000 to 18,000, while turnaround time fell from four to five days to six to 12 hours.
The bigger question
The launch also reflects the industry's growing focus on AI orchestration, where organisations need to coordinate AI, people and existing systems rather than deploy isolated tools. McKinsey's recent research on agentic CX similarly argues that workflows, decision rights and human escalation need to be redesigned alongside the technology.
For SupportNinja, MomentumLabs is an attempt to turn that principle into a service offering. Whether the model delivers results beyond the company's early customer examples remains to be seen. Either way, the launch is another clear example of AI moving beyond individual tools and into the redesign of the customer service operation itself.