How AI is changing the employee–customer connection

Our new era of AI-enabled ways of working began with digital transformation (DX) initiatives. These focused on migrating applications to the cloud, rapidly accelerating the digitalization of common business, government, and health processes essential for surviving the pandemic. Now, these initiatives provide the backbone for AI adoption. They provide the basic means of evaluating digital experiences (through ratings and feedback), then offer those providing these experiences with tools to address client requests.

As digitally enabled experiences and self-service digital platforms evolve, the human side of good business rests on foundational customer relationship-building. AI empowers employees, providing them with the right tools, knowledge, insights, and training, delivered at the right time, to better meet client needs.

IDC’s recent survey on behalf of Ricoh reinforces the importance of this employee/customer connection. This survey data shows that the top 2 critical success factors for IT leaders in the next 18 months are indeed improved employee experience and customer engagement.

EX and CX share a mutual and measured improvement, but many drivers leading to that relationship are behavioral and backed by employees’ improved readiness to engage with each other and for employees and customers to interact in mutually helpful ways. AI’s ability to bring behavior to the business performance formula is key. IDC’s recent research found that 50% of IT and line-of-business leaders use analytics and key performance indicators (KPIs) to measure links between EX and CX. Another 25% have proven a causal relationship for how EX and CX improvements drive each other.

Incorporated KPIs include customer satisfaction (CSAT), employee engagement and satisfaction (ESAT), and trending revenue shifts (source: n = 609; Ricoh Future of Work and Employee Experience Survey, February 2025).

AI-enabled tools, specifically agentic workflows, enable employees to pre-validate customer profiles by analyzing requests from earlier client engagements. AI insights update as client engagement progresses, by referencing key engagement and opportunity markers (e.g., CSAT) in real time. Many of these improvements address readiness on both sides of the customer conversation:

• “Do I remember you?” Context-aware access to historical conversations identifies where the last conversation ended, offering frictionless continuation and an organic close to desired client outcomes.

• “Can I efficiently help you?” AI-driven and targeted training skills paired with knowledge sharing boost client enablement readiness.

• “Will we connect?” AI bias detection and GenAI-suggested threads support productive conversation and engagement tactics.

As AI becomes more conversational with natural language capabilities, IDC expects more opportunities to capture EX and CX insights and assess them in real time. AI will likely improve automated customer service as one part automation and two parts communication with:

• Faster response time for basic client information requests — As AI tools improve their natural language processing capabilities, they will better parse customer requests and shift from retrieval to dialogue.

• Greater information-sharing accuracy and fidelity — As AI agents are able to access more data sources, they’ll be able to respond to complex requests more immediately and accurately, sharing both asked-for and deterministically needed information.

• Faster routing of complex requests to human assistants — AI agents gain insight through contextual learning to determine more rapidly when and how to transfer requests to human assistants.

AI-enablement tools work on both EX and CX, whether by supporting employees directly or pre-engaging clients and customers to better triage their needs. The benefit to employees? In having the right resources at their disposal, they can not only share valuable information but feel valued by the clients they support.

AI use cases — especially those for advisors and agents that work across multiple and diverse data environments — inevitably cause disruptions, including eliminating jobs, changing roles, and creating responsibilities across functions. Alleviating friction requires:

• Aligning the organization around AI adoption. Many managers and workers are concerned about the workforce’s readiness to successfully deploy AI-enabled tools.

• Evolving traditional command and control leadership models. Pure command-and-control leadership impedes AI adoption unless it is paired with opportunities for employees to contribute bottom-up insight into best AI-deployment practices. Stakeholders are often reluctant to change their hierarchies to give financial and operational leaders considerable design control over the organization. Centers of excellence offer valuable guardrails and guidelines for successful and secure AI deployment across the organization.

• Constricting focus on short-term productivity measures. Hyper-focus on productivity and financial performance may be the primary driver for AI adoption, but innovation matters for long-term business growth. Successful organizations that adopt AI-enabled methods must nurture the human side of workforce performance that captures unique insights.

• Prioritizing and funding skills development. Organizations that fail to update their workflows with digitally enabled learning will struggle to adopt tools that promote competitive growth. Career pathways must draw on continuous skills development across functions that integrate with training AI-enabled workflows.