AI is changing too quickly for static decisions. With models, vendors, and regulations shifting rapidly, buyers need to evaluate more than capability at the point of purchase.
They need to understand how their AI choices now will hold up over time.
As adoption accelerates, the structural risk of committing to an AI approach that cannot evolve is growing. That risk is already playing out in early deployments.
As Rhys Harris, AI Product Director at Content Guru, explained to CX Today, the real issue is not only performance:
"The real cost is not being able to adapt quickly enough."
The six questions below are designed to pressure-test flexibility, governance, and commercial reality before commitment.
1. Can We Swap AI Providers Without Changing Our Underlying CX Platform?
Flexibility starts with architecture. If AI capabilities are tightly embedded into workflows or platforms, switching later becomes expensive and disruptive.
Harris emphasized that change is inevitable and systems should be designed accordingly:
“Even if you look at the transition to cloud, having the correct infrastructure, the correct resilient foundations of the product that you're providing is as important in certain cases as the functionality.”
In practice, this means vendors should support interchangeable components across the stack, not just offer integrations on paper.
"Our customers can swap out the provider that they need for different segments of the customer journey… without disrupting services," Harris said.
If switching requires re-platforming or major redevelopment, lock-in risk is already present.
2. Are We Locking in Parts of the Journey?
Lock-in rarely begins with high-profile deployments. It often starts with smaller, internal use cases—agent assist, QA, analytics—before expanding outward.
These “easy wins” can quietly define long-term constraints.
Understanding where dependency begins is critical. Early decisions around voice, chat, or internal tooling can shape how easily the broader CX stack evolves later.
Buyers should make sure that they’re baselining metrics and outcomes, Harris said:
“But there also needs to be the flexibility to swap and future-proof against evolving requirements and business needs, without being locked into multi-year contracts that restrict that agility.”
3. What Does ‘Multi-Vendor’ Mean in Practice - and Who Controls the Switching?
Many vendors position themselves as multi-vendor, but the reality can differ significantly.
True flexibility is not just about having multiple integrations. It is about how easily those providers can be changed, and who controls that process.
Harris points to the volatility of the AI market as a key reason this matters:
"You need to do all of that to deliver complete and correct automation in the right areas and ultimately protect your customers from vendor instability."
If switching providers requires vendor intervention, new contracts, or architectural changes, “multi-vendor” may be more constrained than it appears.
4. How Will Latency and Performance Be Managed by Channel?
Not all AI workloads have the same requirements. Treating them as interchangeable can limit performance and cost efficiency.
Harris highlighted the difference between real-time and asynchronous use cases:
"If you're going to have a voice-to-voice conversational model at the beginning of your IVR, that needs to be really snappy… [which is] very different to something like a summarization of an interaction. Ultimately, it doesn't need to be low latency to achieve the same level of value.”
This distinction matters. Voice interactions require low latency and immediate responsiveness, whereas tasks like summarization or analytics can tolerate delays.
A vendor’s ability to match models to specific use cases indicates how flexible and efficient their approach will be.
5. How Do You Handle Governance, Auditability, and Supply Chain Risk Across Third Parties?
Governance is becoming a central part of the lock-in conversation, particularly as AI ecosystems expand to include multiple providers. At the same time, governance cannot be applied uniformly, Harris pointed out: