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Ingram Micro is urging channel partners to stop treating AI as primarily an infrastructure decision. Cheryl Rang, the distributor’s vice president of technology solutions, says the more useful question is how a partner applies AI to a customer problem—not whether the workload runs in a cloud, a data center or on an endpoint.
Rang made the case in a CRN interview published November 11, 2025. Her central message is that AI becomes commercially meaningful when it is connected to a repeatable business practice, measurable customer outcomes and services an IT provider can support over time.
The strategy shift: from “where” AI runs to what it accomplishes
AI discussions often begin with deployment location: public cloud, private infrastructure, an edge device or a PC. Rang’s argument changes the order of decisions. “No matter what AI becomes, the through line is how you’re applying it,” she says. In another formulation, “It’s about how you apply it, not just where.”
For a managed service provider, that means starting with a business workflow or risk and then selecting the appropriate model, application, data controls and operating method. The technology still matters, but it is evaluated against the result it enables.
What an applied-AI plan looks like for an MSP
1. Start with a customer outcome
Define the problem in operational terms before choosing a tool. Examples include reducing manual review in a fraud process, improving the handling of security alerts, protecting sensitive healthcare information or helping employees find information faster.
2. Map the workflow and data
Identify where people spend time, which systems contain the required information and what permissions apply. This exposes integration work and privacy constraints early instead of treating an AI model as a standalone purchase.
3. Choose the application layer
Possible layers include an AI-enabled endpoint, a security control, a workflow agent or a customer-experience application. The right layer depends on the task, latency requirements, data sensitivity and the systems already in use.
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4. Package operation as a service
An MSP can create a durable offering around configuration, policy, monitoring, user training, integration and periodic improvement. That turns a one-time deployment into an operating practice rather than a novelty demonstration.
Practical use cases Ingram Micro highlights
AI PCs and PCs that run large language models
At the endpoint, AI-capable PCs can support workloads that use local processing. The business question is not whether every employee needs one; it is whether a particular workload benefits from local responsiveness, reduced data movement or an application designed for on-device AI. Partners should validate application compatibility, security controls, management requirements and the customer’s data policy before recommending a hardware change.
Fraud detection in banking
Fraud analysis is an example of AI applied to a high-volume decision process. A partner’s role may include connecting data sources, implementing appropriate access controls, integrating alerts with existing systems and monitoring how staff act on model-generated findings. The interview does not publish a performance benchmark, so any expected improvement must be established in the customer’s own evaluation.
Secure data management in healthcare
Healthcare deployments require careful treatment of sensitive information. An applied strategy can focus on data classification, controlled access, auditability and workflows that help authorized staff use information without exposing it unnecessarily. AI does not remove the need for governance; it makes identity, retention and security policies more important.
Stronger security protocols
AI can be incorporated into detection and response processes, but the useful deliverable is a better security operation: clearer prioritization, faster investigation or more consistent enforcement. Partners should define who reviews automated actions, how exceptions are handled and what evidence is retained.
Agents that improve operational efficiency
Agent-based systems can carry out multistep tasks across connected applications. The value depends on reliable integrations, bounded permissions and human oversight. An agent that drafts a response or gathers account information is a different risk from one that changes production settings or sends an external communication without approval.
What Ingram Micro says it contributes
Rang describes Ingram Micro’s role as training, enabling and supporting partners that are still becoming comfortable with AI. The distributor’s stated aim is to help partners apply AI to customer businesses rather than requiring them to become foundation-model companies.
That distinction is explicit: Ingram Micro does not expect partners to build their own data center to train large language models. Instead, it plans to help them select and apply available capabilities, develop the necessary skills and turn those capabilities into customer services.
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How Xvantage puts the message into daily partner work
Ingram Micro’s Xvantage platform includes a built-in AI agent. According to Rang, it can surface partner opportunities, recent company news and emerging solution areas. Associates can use that context to prepare for customer meetings and move beyond a purely transactional product conversation.
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This is an internal productivity and selling application, not evidence of a measured revenue or productivity uplift. The interview does not state an adoption rate, financial result or benchmark for the agent. Partners should verify current Xvantage capabilities and any eligibility requirements before relying on a specific feature.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Comparing partner approaches to applied AI
| Approach | Application layer | Infrastructure investment | Skills and enablement | Customer outcome to measure | MSP services potential |
|---|---|---|---|---|---|
| AI-enabled endpoint | PC and user productivity | Endpoint refresh or management changes; exact cost is not stated | Device management, application compatibility and user training | Task completion, responsiveness and policy compliance | Lifecycle management, security and support |
| Fraud or security analytics | Detection and decision support | Data integration and processing capacity; exact cost is not stated | Security operations, data governance and response procedures | Investigation quality, alert handling and controlled response | Monitoring, tuning, response and compliance support |
| Healthcare data management | Information access and governance | Secure data controls and approved integrations; exact cost is not stated | Identity, privacy, retention and audit practices | Authorized access, traceability and reduced manual work | Governance, implementation and ongoing administration |
| Workflow agent | Cross-application automation | Connectors, permissions and orchestration; exact cost is not stated | Process design, testing and human-oversight controls | Cycle time, error reduction and exception handling | Agent management, integration and optimization |
How an MSP can turn AI into recurring revenue
Rang’s “business practice” framing points to services that continue after deployment. A practical offering can include:
- Readiness and use-case assessment: inventory workflows, data sources, permissions and risk.
- Implementation: configure the selected application, connect approved systems and establish access policies.
- Enablement: train users, document acceptable use and create escalation procedures.
- Managed operation: monitor performance, review exceptions, maintain integrations and adjust policies.
- Governance reviews: reassess data handling, permissions and business results as the customer’s use changes.
Recurring revenue is an opportunity, not a published guarantee. The distributor’s interview supplies no financial forecast or standard pricing model, so each MSP must tie fees to the scope, risk and operating effort of its own service.
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- What specific customer decision or workflow will AI change?
- Which data may the application access, and which data must remain excluded?
- What existing systems must be integrated?
- Who approves, reviews or reverses an automated action?
- How will success be measured without claiming an unverified benchmark?
- What support, training and governance continue after launch?
What this means for channel strategy
The competitive distinction is less likely to be ownership of a particular model than the ability to apply AI safely and repeatedly in a customer’s environment. Partners that can connect use cases to integration, security, user adoption and ongoing operations are better positioned to remain relevant as models and deployment options change.
That is the practical meaning of Rang’s warning that partners cannot simply return to life before ChatGPT. AI adoption is becoming part of normal business operations; the strategic work is deciding where it creates defensible value and building the service discipline to deliver it.
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