Onboarding a new client is one of the most expensive and most error-prone processes in any company. It requires document collection, data verification, system configurations, sequential communications, and coordination across different functions. Every manual step is a friction point, and every friction in the first few days increases the risk that the client will leave before seeing the value of the service.
AI agents are the technical answer to this problem. But between the pressure to adopt them and the ability to do so in a way that produces results, there is a gap that many companies are underestimating.
The pressure is real, the budgets are there
In a survey conducted in April-May 2025 of 265 customer service and support leaders, Gartner found that 77% of these leaders feel pressure from senior leadership to adopt AI, and 75% report having increasing budgets for AI initiatives compared to the previous year. The typical leader is planning to add five new roles in the next twelve months to manage these investments.
The numbers reflect a shift in priorities that directly affects onboarding. When senior leadership pushes to automate customer service, the first areas that come under review are those densest with repetitive tasks: document collection, identity verification, account setup, sending communications. All activities that make up new client onboarding.
Four areas where AI delivers measurable impact
Gartner has identified four categories of AI use cases in customer service with the greatest potential for impact. The first involves assisting human agents, with tools that suggest responses, retrieve information, and reduce handling time. The second is self-service, where the client resolves issues independently through AI-guided interfaces. The third is the automation of operational support processes, including analytics, knowledge base content generation, and quality assurance. The fourth, with the broadest implications, is agentic AI: systems that autonomously manage complex workflows and multi-step requests.
Client onboarding touches all four categories. A well-configured agentic system can collect the required documents, verify them, update internal systems, send welcome communications, detect when a client gets stuck at a step, and trigger an escalation to a human operator. All without anyone manually coordinating the individual steps.
The client is changing: enter the machine customer
There is one element that is changing the rules more radically than it may appear. In a survey conducted by Gartner of 4,879 customers between January and February 2025, 51% said they would be willing to use a personal GenAI assistant to manage service interactions on their behalf. Not just receiving assistance from an AI, but delegating the management of the supplier relationship to the AI.
This means that in the onboarding process, there may no longer be a human on the other side. The new client is an AI agent that collects information, fills out forms, answers verification questions, and negotiates contract terms. Companies building onboarding systems today must consider this possibility: their automated flows will need to be able to interact not only with real people, but also with other agents.
By 2029, 80% of common issues resolved without humans
The most cited Gartner prediction in this area was published in March 2025: by 2029, agentic AI will autonomously resolve 80% of common customer service issues without human intervention. The direction is clear. Customer service teams that today handle incoming requests from human clients will need to adapt to a future where the share of automated interactions becomes dominant.
For those managing onboarding, this trajectory implies a process redesign that goes beyond adding an AI tool. It means redefining which steps require human judgment, which can be fully automated, and how to structure escalation for cases where the agent cannot proceed on its own.
The risk that projects never mention
There is one data point that rarely appears in presentations on automation projects: Gartner estimates that over 40% of agentic AI projects will be cancelled by 2027, due to a combination of out-of-control costs, unclear value, and insufficient governance. This is not a minor warning. In a context where 77% of companies are increasing AI investments under pressure from leadership, the likelihood of launching poorly defined projects is real.
Automated onboarding projects are particularly exposed to this risk. The process involves sensitive client data, requires integration with often heterogeneous legacy systems, must comply with sector-specific regulatory requirements, and cannot afford errors in critical phases like identity verification or contract signing. An AI agent that makes mistakes in these steps does not just cause a service failure: it can generate legal or compliance problems.
The difference between a project that reaches production and one that gets cancelled almost always lies in the definition phase: how clear is the scope of agent autonomy, how are edge cases managed, who is responsible when something goes wrong, and how is the value produced measured against development and maintenance costs.
What changes for those managing the process today
Gartner describes the change underway with a precise formula: from people management to AI leadership. Customer service leaders will no longer primarily manage people, but AI systems that operate autonomously. This also applies to those coordinating onboarding: the work shifts from managing individual cases to designing flows, supervising agent performance, and handling exceptions.
The companies that adopt this approach deliberately -- starting from a clear process mapping and defined governance before implementation -- are the ones that Gartner identifies as best positioned to achieve operational efficiency, improve client experience, and maintain a competitive advantage in an evolving market.
