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:
“Some of the potential use cases for AI… carry more organizational risk than others.” Conversational agents and agent assistance applications “need a different level of governance than something that’s more internally facing.”
Enterprises need to be aware of the regulations already in place and ensure they meet the processing requirements in each region or country—and sometimes at the state level, Harris advised:
“It’s important, firstly, to be clued up on what governs the areas that you operate or intend to operate in. And then ultimately making sure that the data is stored locally by organizations that you trust.”
Given the current geopolitical climate, buyers need to be aware of the risks of working with vendors in different regions. The fact that certain providers, particularly in the hyperscaler community, are headquartered in the U.S., is “already visibly posing a risk to European countries in a way that, for example, France is much more now impactfully legislating to buy European first. And that’s quite a big change compared to the past 20 years,” Harris explained.
“Work with CX providers that can offer you the flexibility and the clarity around where your data is hosted first… that will come to speak to you about those types of concerns and help you through your internal governance processes in the correct way.”
Buyers also need to look beyond the primary vendor to the broader supply chain, ensuring that they conduct due diligence on subcontractors to mitigate supply chain risk.
This includes understanding where data is processed, how it is handled, and how compliance is maintained when components change.
Without this visibility, switching providers can introduce unintended regulatory exposure.
6. What Commercial Terms Protect Us if Costs Change?
Pricing models can create a different kind of lock-in driven by unpredictability.
Harris flagged consumption-based pricing as a particular concern:
“The biggest risk is consumption-based approaches to AI and working with vendors that will give you a token cost… or that give you a usage figure that can be hard to forecast against, particularly if you’ve got seasonal demand.”
Token-based or usage-based pricing may appear flexible, but can become difficult to manage at scale. Instead, buyers should look for pricing structures that provide clarity and protection, Harris said:
“Buyers should ultimately have the ability to do some level of bursting against a fixed commitment within a fixed term.”
Forecastable costs and transparent terms are essential for maintaining control over long-term spending.
Vendors should provide a set of clear license costs that show the buyer the services and protection they’ll receive over the life of the contract.
Providers should also understand seasonal and campaign-based demand, allowing buyers to flex up without additional changes to the terms and conditions.
Rethinking AI Vendor Selection
These questions reflect a broader shift in how AI should be evaluated.
Vendor selection is no longer just about features or model performance. It’s now also about designing for change. Organizations are not choosing a single tool: they are building an ecosystem, and the ability to evolve within it.
“Buyers need to be conscious of what happens if the outcomes don’t achieve what they were initially looking to do,” Harris said.
“This is always a risk when there’s a buzzword in the industry, and people say, ‘you’ve got to jump onto this type of a technology,’ without taking a step back to consider what they can do to enable them to have the correct flexibility.”
Harris frames this as a partnership challenge: “You need to work with a CX provider that is a trusted AI advisor.”
Lock-in often appears as a procurement detail. Over time, it becomes an operational constraint which ultimately impacts both employee and customer experience. The surrounding architecture, and the flexibility built into it, determine whether that system can adapt. The right questions make that visible early.
“It goes back to the measurements and metrics,” Harris said.
“What is important for your organization? What are the realistic targets you want to achieve against those key performance indicators? And how can you factor that into work with a partner that’s willing to say, ‘this is an acceptable level of performance that we both agree, this isn’t, and this is how we can work collaboratively to amend that.”
The vendor may need to “bring in a different provider or make tweaks to the product that sits around it.”
The answers can help buyers ensure they get the outcomes they need with the correct protections, building the capability to continuously adapt, without starting over.
