Geoff Beckstrom, Sales Engineer, White Label Communications

A newsletter lands in my inbox every morning with a single premise: “There’s an AI for that.”

Scroll through it and you’ll find dozens of new tools, models, and integrations — all announced in the last 24 hours. It’s too long to read. That’s why I skim the headlines and move on.

That’s a natural response to a market that’s moving faster than anyone can track.

Contact center leaders are feeling this acutely. Right now, most are being told to ‘pick an AI strategy’, even though the market resets every 90 days. You’re expected to make a long-term decision in a short-term environment. And if you get it wrong, you’re locked in.

That’s because the winners of this race aren’t decided yet. And anyone who tells you otherwise is either guessing or trying to sell you something.

A Clarification Worth Making

Before getting into what AI can actually do for your contact center, it’s worth drawing a distinction that gets blurred constantly in vendor conversations.

An IVR is not AI.

Interactive voice response has been around since the 1970s. It routes calls. It follows decision trees. It’s useful, cost-effective, and plenty of operations need it. But when vendors started calling it AI because the market demanded AI, the word lost meaning. That’s why you see the “Do you use AI?” question answered with everything from “Yes, we have a genuinely intelligent real-time translation layer” to “Yes, we have a phone tree with a friendly voice.”

Real AI can help contact centers do things that weren’t possible before. It listens, interprets, adapts, and responds in real time. It processes what happened on a call and generates insight automatically. It connects to data you already have and makes it actionable in ways a single person couldn’t manage at scale.

Viewing AI through that lens changes what the platform question is actually about. The smartest move any contact center can make right now isn’t picking the best AI — it’s staying open to all of them. No one knows which tools will lead the pack in six months. Building around any single one is a bet you don’t have to make.

Quick gut check: is your platform actually AI-ready?

• Can you swap AI models without vendor approval?
• Can you route different tasks to different models?
• Can you plug in internal LLMs?
• Can you deploy new AI tools in weeks — not quarters?

If the answer is no to any of these, you’re not choosing AI — you’re being limited by it.

What This Looks Like on a Real Call

Picture an inbound customer-service call where the caller speaks Japanese and the agent speaks English. In a traditional model, the agent would have to put the caller on hold, locate an interpreter, brief them on the situation, then bring them into the call. Every step in that sequence creates friction, some of which is very expensive.

With the right AI layer running, none of that happens. The caller speaks in their native Japanese. The agent hears them in English, in real time. The agent responds. An AI-generated voice delivers that response back to the customer in Japanese. The conversation moves at normal speed. Nobody holds. Nobody scrambles.

Behind that seamless exchange, three separate AI functions are running at once: one transcribing speech to text, one translating, and one generating the voice response back to the caller. In our case, that’s DeepGram handling transcription and Azure handling translation, but the specific tools matter less than the approach. Each job in that workflow goes to the model that does it best.

From First Ring to Final Note

Post-call analysis follows similar logic. After a call closes, an LLM reviews the transcript against whatever prompts you’ve defined: summarize the call, score it across five quality dimensions, flag sentiment, and auto-populate a QA record. Each is a separate prompt, tuned for its specific job and running automatically across every call.

Collections calls offer another interesting use case. These are tense moments where individuals feel stressed, embarrassed, or defensive. We’ve noticed in these instances that people seem more willing to have an honest conversation with an AI than with a human — likely because the fear of being judged or shamed drops when there’s no live person on the other end. Remove that dynamic and the conversation gets more honest, which is exactly what a collections agent is trying to achieve.

Real-time sentiment analysis is coming into that same space. The technology exists to read a call in progress, flag when tone shifts in a concerning direction, and surface a suggested script adjustment to the agent before things go sideways.

We’re also seeing teams use their LLMs to get better at using the LLM. Asking an AI to write the prompt you’ll use to analyze your transcripts sounds circular until you see the output. It writes a better prompt than most people would draft themselves. That’s the kind of leverage that’s easy to miss when you’re locked into a single tool’s capabilities.

The Case for Not Picking a Side

Every example above depends on something that’s easy to undervalue: the freedom to choose whatever model does the job best and switch on the fly as the technology evolves.

Some platforms are built around partnership agreements with specific AI vendors. That’s fine until a better option appears. And in this market, it will. If your platform can’t connect to it, you don’t get to use it. By then you’re already 12 to 18 months behind, waiting on contract cycles while competitors who stayed flexible have moved on.

The other pressure comes from inside. Large companies are starting to build their own internal LLMs. Some do it to avoid exposure under GDPR, California privacy law, or other state-level regulations. Others do it because their competitive advantage lives in proprietary data they’re not willing to train a public model on. Either way, they need a contact center platform that can connect to whatever they’ve built, not just whatever’s on the approved vendor list.

That’s exactly the position WLC built toward. The platform connects to the AI your team wants to use: public model, third-party agentic AI, internal LLM, or whatever combination makes sense. The connector library grows as new tools prove themselves out.

What Prepared Actually Looks Like

Nobody knows which AI will be most useful to your contact center in six months.

The question worth asking isn’t “which AI should we pick?” It’s “does our platform let us use whatever we need, whenever we need it?”

Teams that locked into single-vendor AI even 12 months ago are already rebuilding. The cost isn’t just technical—it’s lost time, missed capability, and starting over while competitors move ahead.

The longer your platform limits what you can connect to, the more expensive that gap becomes.

WLC was built to remove that constraint from day one.

If you want to see what that looks like in your environment, we’ll walk you through it.