Companies must structure their remote customer relationship management operations in a way that is consistent with their strategy, business objectives, and quality requirements.
However, their approach and the underlying questions that define this target model must be reexamined in light of AI.
Summary
The three key questions that define the target model for remote customer relationships must be reexamined in light of AI.
- Who does the work? AI is prompting a reevaluation of which activities are handled in-house and which are outsourced. It can reduce the volume of work assigned to service providers and create new roles, such as AI oversight and business expertise…
- Where do we operate? Automation and language assistance can alter the relative importance of labor costs, language, and working hours in location decisions, which must be reassessed based on the remaining activities.
- How should teams be organized? There may be fewer human interactions, but they may be more complex, requiring adjustments to staffing levels, skills, and objectives, as well as organizing the transition to AI for advisors.
Contents
Three Principles of the Operational Model for Remote Customer Relations
In this previous article Building a (Truly) Effective Operational Model for Remote Customer Relations, we explained that the performance of a remote customer relationship system depends on three key trade-offs:
-
Who operates it?
Operating model: in-house, outsourced, or a hybrid model. -
Where do we operate?
The location of operations: onshore, nearshore, or offshore. -
How should teams be organized?
Team size, distribution of activities, and processing levels.
The Arrival at maturity of AI, chatbots, and agent support tools does not render these trade-offs obsolete. However, it does profoundly alter the terms of the equation.
1. Who is in charge?
With AI, the scope of a business activity is no longer necessarily entrusted in its entirety to a team or a service provider. A single request may combine automated processing, input from an advisor, and a decision by an expert. This breakdown calls for a reevaluation of the boundaries of outsourcing and the expected value of each stakeholder.
Break down tasks to better distribute the work
The distinction between simple, automated requests and complex requests handled by advisors is still insufficient to build the target model.
A single request often involves several tasks of different types. For example, when handling a complaint, AI can reconstruct the history, identify missing documents, and draft a proposed response. The advisor assesses the situation, communicates with the customer, and decides whether to offer a goodwill gesture within the scope of their authority.
Conversely, a seemingly simple request may require human intervention when information is missing, a rule has an exception, or multiple systems provide conflicting data
Review outsourcing contracts and their business models
Automating some of the standardized interactions can reduce the volume of work assigned to service providers. It can also profoundly change the nature of their work: the remaining interactions may become longer and require more specialized expertise.
A decline in volume does not, therefore, automatically result in a proportional decrease in costs. It can call into question systems that were historically designed to streamline the processing of homogeneous requests.
Contracts must therefore evolve. Are volume commitments, rates, and productivity targets still appropriate? How should gains be shared when AI is provided by the client or by the partner? How should we compensate for contact prevention and sustainable resolution when the business model is based on hours or interactions handled?
Enhancing the value provided by partners
AI can strengthen the role of partners capable of combining domain expertise, technology integration, and continuous improvement. Their contribution can extend to knowledge maintenance, evaluation of automated responses, exception handling, and analysis of the reasons for contact.
The company must nevertheless retain the necessary capacity to set rules, assess service quality, and improve the system.
2. Where do we operate?
Recalculate the economic benefits of the facilities
When certain tasks are automated and advisors are supported by technology, the cost of human labor may account for a smaller portion of the total processing cost. In this case, differences in labor costs between locations may become less significant.
However, the analysis must take into account the costs of technology, integration, monitoring, and error correction. A system that appears to be cost-effective may lose its advantage if it results in multiple re-runs or follow-up contacts.
Reassess linguistic and scheduling constraints
Translation, rephrasing, and writing assistance tools expand the possibilities for multilingual organization. However, their value must be assessed based on the channel and the situation.
Drafting a standardized written response and navigating a sensitive verbal negotiation involve different requirements. Understanding unspoken nuances, local references, and the client’s situation continues to be key to the quality of the relationship.
Similarly, AI can expand the availability of the service, but the fact that a channel is always accessible does not guarantee that requests will always be resolved. The system must still be able to access information, have the authority to take action, and be able to mobilize the necessary resources.
An agent available at night may simply shift the wait time if approvals or back-office operations are limited to business hours.
Incorporating the Geography of Technological Dependencies
Consideration must also be given to the location of the hosting infrastructure, data access conditions, and dependencies on technology providers.
Service continuity now depends on a combination of human and technical resources. A platform outage or a provider’s unavailability can affect multiple locations simultaneously.
3. How should teams be organized?
As AI takes over certain tasks, the work of advisors is changing: there are more exceptions to resolve, more proposals to evaluate, and more situations to resolve. This shift requires a joint review of staffing levels, delegations of authority, responsibilities, and performance criteria.
Distinguish between contact volumes and workload to determine the optimal capacity
Automation can reduce the number of inquiries handled by agents while increasing their average duration or complexity. Failed processing attempts can also generate new inquiries.
We must therefore distinguish between initial requests, the interactions they generate across various channels, and the human time required to resolve them.
Rethinking Levels of Processing and Delegation
The N1/N2 models can evolve without necessarily disappearing. AI can be used at several stages: to assess a request, prepare an expert response, or perform an operation after validation.
The challenge is to define the conditions for processing: in which cases should the process continue automatically, should approval be requested, or should the case be transferred?
The handover to the human team is a critical moment. The advisor must be familiar with the context, the actions already taken, and the reason for the handover. He or she must also have the necessary skills and authority to move the situation forward.
Redefining Work and Skills
The concentration of complex interactions can increase the cognitive and emotional burden on teams. Treatment duration goals must take this into account, and teams must have time to review a case, consult an expert, or debrief on a difficult interaction.
The training must also prepare advisors to work with AI: verifying the suggested responses against the case file and business rules, correcting them if necessary, and continuing the process when the tool reaches its limits. This requires maintaining a solid level of business expertise and equipping advisors with the tools they need to resolve the situations entrusted to them.
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