CX Insight Magazine

July 2026
Stop Modernizing Your Contact Center.
Start Resolving Now.

The only choice isn’t rip-and-replace or wait for your vendor. The fastest path to AI is adding resolution on top of whatever you already run.

by Marnie Skehan, CX Strategist, Vapi

Ask a CX leader how they plan to modernize the contact center, and you tend to get one of two answers. Rip out the platform and migrate, or wait for the platform vendor to ship the AI they keep promising. Both make sense. Both are slow. Both are gargantuan undertakings. Both approaches delay the full return on investment from AI.

Now ask yourself, which parts of the customer’s problem can you resolve, end-to-end, by placing a layer of AI within or on top of the system you already run? That’s something you can take action on in your workflows and customer journey that doesn’t require any migration.

Let’s look at the migration path.

Replacing a large contact center platform is one of the hardest enterprise IT projects there is, and the reason isn’t any single integration. It’s that you have to re-imagine everything the old system already did, and reach critical feature parity before you can move anyone over. Years of call recordings and interaction history have to be considered. Routing, reporting, workforce management, and compliance flows all have to be re-created and re-tested. Hundreds or thousands of agents have to be retrained. Large global rollouts run in phases, region by region, and routinely stretch across several years, and for most of that window, you run the old and new systems in parallel and pay for both. The migration becomes the project, some spanning three to four years, and the customer experience improvements you wanted wait behind it.

Now the waiting path.

Plenty of contact centers have already moved to a modern cloud platform and assume modernization is handled. But customization on a shared multi-tenant platform is limited, and pushing it too far can collide with the SLA terms you are paying for. So you wait for the vendor to build Voice AI into the suite, or to acquire it. The pace of change is set by the core system and the vendor behind it, not by you.

Resolution is a separate path

It helps to be precise about what modernization is for. For twenty years, contact centers optimized to keep people out of the queue. Deflection and containment were the scoreboard. A call resolved in the IVR counted as a win, even when the customer hung up unsatisfied and called back an hour later, compounding costs. The better measure is resolution: how much of the customer’s actual problem gets handled, on their terms, via self-service or with tandem care with a valued human.

That is the shift for which agentic AI is built. Gartner projects that by 2029, agentic AI will autonomously resolve 80% of common customer service issues1 without human intervention, with a 30% reduction in operational costs. These are not bots that answer a thin question and then fragment the call across channels. With the power of Voice AI, they act: verify identity, check an order, process a return, update a record, then confirm the outcome in natural voice.

AI is not automatically cheaper, and Gartner has been clear that the cost case weakens for the most complex interactions. That’s not an argument for waiting, though. Automate the routine, repeatable work first, where the economics are clearest, and keep people on calls where empathy, understanding, and complexity demand human discernment.

Resolution is the part customers actually notice. SQM Group’s 2025 benchmark puts the average contact center’s first contact resolution at about 70%. So roughly a third come back, often more than once. Those repeat contacts cost you twice, as your agents or systems have to handle the same issues twice, and again in lost loyalty from the customer. According to Gartner, 96% of customers who have a high-effort interaction like this become disloyal to your brand.

Modernize in layers

My recommendation: add Voice AI optimizations with care and discernment, focusing on where they have the greatest impact without disrupting your existing human interface. This might be in front of a specific customer journey, as we did prior with IVR, natural language processing, and voice or chat bots.

Pick one workflow that is high-volume, repetitive, and rules-based. Maybe one of these:

1.After-hours capture.
2.Order and shipping status.
3.Password resets.
4.Authentication before a transfer.

 

Route that traffic to a Voice AI agent that connects to the relevant systems of record, does its best to resolve the request end-to-end, and elegantly transfers to a human agent at any time. The handoff is where you maintain your quality goals. Done well, the agent receives a structured summary: who the customer is, what they verified, what they already tried, and what they actually want. The human agent receives a warm transfer with context, rather than starting cold. Visibility through reporting and analytics traces the complete journey.

Beginning with a simple workflow, either internally or in production, is just fine as you move towards adopting AI Voice Agents. Managing risk here is quite similar to the flows we have designed in the past for other interaction automations. And measurement matters. You can compare resolution rates, average handle time, containment, transfer metrics, and cost per interaction. The numbers either move or they do not, and you find out in days or weeks rather than at the end of an exhaustive pilot program. It is truly platform agnostic as well. The same AI Agent workflow architecture works whether the core is a legacy premise stack, a multi-tenant Cloud system, or a mix of everything else. The key is to begin small, learn the power and efficiency on a small scale, and then adopt more complex workflows that genuinely deliver the ROI without compromising quality and customer loyalty.

Avoiding the projects that fail

A layered approach also protects you from the most common way these efforts die. Gartner predicts more than 40% of agentic AI projects will be canceled by the end of 20272, citing escalating costs, unclear business value, and weak risk controls. Part of the noise is what Gartner calls “agent washing,” vendors rebranding old chatbots and scripts as agents. Of the thousands of vendors claiming agentic capability, Gartner estimates only around 130 are the real thing. Most buyers are still early: in a Gartner poll of more than 3,400 attendees, only 19% reported significant investment in agentic AI, while 42% called their investment conservative. That is not a reason to sit out. It is a reason to start with intention and learn before the stakes are high.

The projects that survive tend to share a few traits. They start with one use case that has a clear return. They measure resolution, not activity. They build in governance from the start, with defined escalation paths, data controls, and human review for the cases that need it. A narrow first deployment gives you all of that almost for free. You prove value, set the guardrails, conduct low-cost A/B testing, and earn the organizational confidence to expand, without betting the whole operation on a single launch.

The question to ask your team

The framing most teams inherit is “which CCaaS, and when.” It is the wrong question, and it stalls real progress for years, whether you are facing a migration or waiting on a vendor. The better question is smaller and answerable now. Which single workflow could we resolve, end-to-end, with AI in the next quarter, on top of the platform we already run?

Pick that one. Measure whether it actually resolves the customer’s problem. If it does, do the next one. The contact centers that pull ahead over the next few years will not be the ones that picked the right platform first. They will be the ones who started resolving more customer needs sooner, with whatever they had.

Article Links

  1. https://www.gartner.com/en/newsroom/press-releases/2025-03-05-gartner-predicts-agentic-ai-will-autonomously-resolve-80-percent-of-common-customer-service-issues-without-human-intervention-by-2029
  2. https://www.gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027
Marnie Skehan

About the Author

Marnie Skehan is a CX Strategist at Vapi.


Vapi

Vapi is the enterprise platform for building and monitoring voice agents at scale.

For more information, visit
vapi.ai.