If you’ve worked in CX long enough, you know how much can go wrong in the process of setting up new technology or replatforming from one solution to another. For whatever reason, CX implementation takes longer than expected, hidden risks resurface at critical moments, small errors made during setup compound into major problems, the list goes on.

This has only become more true as CX systems grow more complex. AI has become increasingly powerful in its ability to enhance the entire CX workflow – but that also means that when AI fails, the negative effects are harder to diagnose and reverse.

In this article, we’ll break down six of the most common challenges in AI-driven CX implementation and replatforming. More importantly, we’ll share practical guidance for overcoming them – before they cost your business valuable time and money.

Why it’s important to get CX implementation right

Every CX implementation creates a foundation, and like any foundation, small compromises made early on don’t disappear. They accumulate as setup debt.

Setup debt accrues when teams move quickly to get their new system live across multiple channels. Layered routing rules build up as teams adapt to new use cases without revising earlier decisions, creating fragile and opaque workflows.  Hidden dependencies – the implicit, often undocumented relationships between systems that rely on one another – lay the groundwork for unexpected problems. 

These foundational flaws usually don’t break anything right away, so the growing setup debt remains invisible until it’s too late.

AI accelerates this setup debt faster than anything else.

AI systems depend on clean data, consistent routing, clear intent signals, and predictable workflows. When something in the setup process is wrong, AI still “works” – it just quietly underperforms. The result? Slower resolutions. Inconsistent experiences. Loss of confidence across the CX org. Wasted time and resources.

Getting CX implementation right isn’t just about launching successfully, it’s about minimizing setup debt so your AI-powered CX platform can actually scale.

CX implementation and replatforming: 6 challenges

Ensuring a smooth implementation, or replatforming from one CX solution to another, depends on a number of factors unique to your organization and its specific tech stack. But there are several common hurdles you must consider early on if you want to avoid problems down the road.

1. Configuration sprawl across multiple tools and channels

When CX platforms span many channels, workflows, products and screens, configuration quickly becomes fragmented. Admins are forced to juggle many different concepts to understand how the new system will behave, often without a single source of truth to guide them.

The sprawling nature of the setup process inevitably becomes a trust problem. Teams are unable to confidently answer whether the system is configured correctly or consistently across tools and channels because there are simply too many variables to maintain at once.

2. Hidden dependencies that diminish AI performance

Many CX systems rely on upstream settings that aren’t always visible – routing logic and control signals that AI and automation depend on in order to function correctly. When these dependencies aren’t accounted for in the initial setup, downstream behavior becomes disrupted and AI systems inherit the gaps.

Over time, this manifests as:

  • Context-starved models: AI lacks access to essential data it needs to reason effectively.
  • Session-only memory: AI can respond within a single interaction but cannot carry understanding across conversations or channels.
  • Shadow intelligence: AI makes decisions that CX teams are unable to track, understand, and manage.

Instead of optimizing for customer outcomes, the system can only optimize for handoffs to human agents. Organizations often see this as a shortcoming of the AI system itself – when in reality it’s often an issue of configuration, not capability.

3. Lack of clarity on required vs. optional elements

During implementation, admins are often presented with a long list of configuration options without clear guidance on what’s essential. As a result, they find themselves choosing between two problematic courses of action. 

They might over-configure their setup with a “just in case” mentality, and end up with an overly complex system they struggle to maintain moving forward. Or they might skip foundational steps that only reveal themselves as problems later.

4. Vague order of operations

The order of operations in setting up CX technology can be as critical as the setup steps themselves. And with no clear setup sequence, teams may end up configuring features and elements in whatever order feels logical at the time.

Unfortunately, these early decisions are often made without the necessary context, leading to invalid assumptions and rework.

5. Siloed or missing best practice information

Best practices for CX setup and maintenance often live outside the product – scattered across documentation, passed down through tribal knowledge, or kept with a single admin. 

As teams grow and change, this knowledge degrades, and setups drift further from recommended patterns. Complexities that would be avoidable with shared data end up breeding confusion and mistakes. New team members make critical errors because they lack the right information. Adoption stalls.

6. Internal confidence gaps before and after go-live date

One of the most alarming problems with AI-driven CX implementation is this: a recently deployed system may appear to be working, but it’s actually plagued by a number of configuration issues.

The result is AI that doesn’t perform well, but hasn’t broken down entirely. Its predictions are weak, its responses are inconsistent, and so on. No one recognizes a clear problem to diagnose, but teams slowly lose confidence that their system will hold up at scale.

For example: imagine you’re a CX leader whose company just deployed an AI solution to communicate with customers. The process works, but the AI-generated replies consistently lack nuance despite being technically correct. You’re likely to lose faith in the usefulness of the solution – long before your team is able to understand that setup mistakes are to blame for the tool’s underperformance.

CX Implementation checklist: 6 best practices to consider

Now that we’ve covered key CX implementation and replatforming challenges, let’s wrap up with a handy checklist to help you avoid each one.

1. Make configuration state explicit.

Treat setup as a system unto itself. Ensure admins can clearly see what’s configured, missing, or incomplete across the entire CX environment instead of relying on assumptions.

2. Surface and validate dependencies early.

Prioritize a comprehensive awareness of cross-product dependencies. Identify and document them before enabling AI features to avoid downstream failures.

3. Clearly define what’s required for initial setup.

Establish a clear minimum viable configuration needed to safely go live, and defer enhancements until you’re sure the foundation is solid.

4. Follow a structured order of operations.

Successfully configure core objects and data first – and then channels, routing, automation, agent workflows, and so on. Whether your order of operations is straightforward or lengthy and complex, make sure you don’t start working until you’ve created a deliberate, repeatable sequence.

5. Apply best practices during setup.

Use guidance that explains why each step matters so that configuration decisions are intentional and scalable.

6. Validate readiness before go-live and prioritize explainability.

Review setup holistically and ensure teams can understand how the system behaves, especially when AI is involved. Confidence across the CX org is essential to ensuring that AI can scale as teams expand and change.

Kustomer’s AI Setup Assistant helps you replatform with confidence

The above challenges make it clear: CX implementation doesn’t fail because of some major mistake by your teams or a glaring flaw in your infrastructure. It fails because even the smallest instance of guesswork in the setup process can lead to cracks in your foundation.

Kustomer’s new AI Setup Assistant was built to eliminate that guesswork.

Whether you’re migrating from Zendesk or starting from a blank slate, AI Setup Assistant analyzes your current configuration state and guides you towards a production-ready org grounded in best practices.

That means:

  • Faster time to value
  • Lower replatforming risk
  • Stronger AI foundations
  • Higher admin confidence before and after go-live date

Learn more about AI Setup Assistant and get ready to set the foundation for a scalable, AI-driven CX organization.