Customer experience services for contact center and support leaders.

Insight Global’s Customer Experience (CX) practice covers three service lines. Enterprise Technical Support runs Tier 1 through IoT and device monitoring. Contact Center Operations covers on-premises, remote, or hosted models with omnichannel and multilingual coverage. Service Operations handles the back-office workflows behind the customer experience: data entry, document retrieval, and order-to-cash. The work runs across financial services, telecom, retail, healthcare, and technology, where volume, compliance posture, and customer base differ enough that teams are built to the vertical rather than from a generic template. Every engagement is scoped under one of two delivery models: agents and specialists placed on your team for situations where you manage the work, or a managed CX team where Insight Global owns the operational outcome against a statement of work. The same practice can move a program from staffed to managed as scope grows, so the model fits the maturity of the program rather than forcing one structure on it.

Insight Global delivers across the major CX platforms its customers run. ServiceNow covers IT service management and enterprise technical support. NICE handles contact center workforce management and omnichannel routing. Adobe-powered platforms run digital service workflows and customer-facing content. Specialists also work across the CRM layer (Salesforce Service Cloud, Microsoft Dynamics), the telephony and CCaaS layer (Genesys, Five9, Amazon Connect), and the knowledge-management tools that integrate into the agent desktop so case history and resolution data stay in one place. These are platforms the practice is fluent in and works in daily, matched to the stack the customer already runs rather than one Insight Global imposes. Platform choice follows the customer’s standard, which keeps the work inside their environment, their security model, and their licensing instead of adding another tool for their team to own and maintain.

The CX practice supports the industries where customer interactions carry operational and regulatory weight. Financial services customers run customer support and back-office operations under strict compliance and data-handling requirements. Telecom and broadband operators manage high-volume customer-facing contact centers. Retail and consumer brands handle order management, returns, and post-purchase support, often with sharp seasonal peaks. Healthcare payers and providers run member services and call centers where privacy rules and accuracy expectations are non-negotiable. Technology customers run Tier 1 technical support and IoT or device monitoring. Each vertical has its own compliance posture, customer-base expectations, and tolerance for handle time and error rate, so the practice builds teams to the vertical rather than applying one generic contact-center model. The Enterprise Operations parent practice means a CX customer can extend the same relationship into adjacent back-office and operations work as scope grows.

Yes. Insight Global is layering AI agents into the CX delivery model in three concrete places. Ticket resolution automation handles the repetitive Tier 1 volume that buries agents (password resets, routine status checks, knowledge-base lookups). AI-driven workforce management runs scheduling, forecasting, and adherence inside the delivery pod, removing the need for a separate WFM tool. AI-powered QA audits a far higher percentage of calls than human auditors can reach, surfacing coaching patterns instead of sampling them. The agents are built by IG Labs, our in-house AI product organization, and the agentic capability set spans Agentic Enterprise Technical Support, Agentic Contact Centers, and Agentic Service Operations. The customer-facing change is faster first-contact resolution on routine issues, smaller delivery pods (a 5-person pod becomes a 3-person pod plus AI agents), and more human-agent capacity for the conversations that actually need a human.

Insight Global delivers a qualified CX shortlist within 48 hours for clearly scoped roles, which is the documented speed of the recruitment engine when the requirement is well defined. Standing up a full managed CX team is a different question, and the honest answer is that it depends on scope. Smaller pilots ramp in weeks. Full multi-channel programs take longer. Specialized scopes such as regulated industries with compliance requirements, multilingual coverage at scale, or specialized platforms extend further still. Rather than promise a generic stand-up number that does not survive contact with a real scope, the practice scopes the timeline up front, against the actual channels, volume, and metrics the program is measured on, before anyone commits to a date. CX talent benches across the US and India keep the team available for surge capacity around product launches, seasonal volume, and incident response, so the timeline is set by scope rather than recruiting lag.

Staffing places agents, support analysts, and service-ops specialists on the customer’s team, under the customer’s management. A managed CX team places a CX pod under Insight Global’s management, accountable to a statement of work. The dividing line is who owns the outcome. When roles are clearly scoped and the customer has a strong CX or contact-center manager to direct the work, staffing is the right fit and the lighter-weight option. When the work is deliverable-driven, such as a contact center stand-up, a multi-year support program, or a back-office migration, a managed team is usually the better structure. It comes with a delivery lead, workforce management, quality assurance, and program management, gives the customer a single point of accountability instead of a set of individuals to coordinate, and keeps the same team in place across pilot, ramp, and steady-state so the operating knowledge does not reset partway through. Both models draw from the same vetted CX bench.

Managed CX engagements are scoped to the operating metrics the buyer is accountable for: first-contact resolution, average handle time, customer satisfaction, NPS, deflection rate on self-service, agent productivity, and cost-to-serve. The statement of work names which subset matters for the engagement and what the targets are, so the contract measures the same things the buyer’s leadership measures. Reporting runs at the cadence the buyer needs, from weekly operating reviews to monthly executive readouts, and the delivery team adjusts the operating model in response to the numbers rather than waiting for a quarterly correction. The point of an outcome-based statement of work is that the engagement is renegotiated on what the team delivered against those metrics, not on hours logged. That structure also makes the AI-agent work measurable: automation only counts if first-contact resolution and cost-to-serve actually move.