Mention of AI in the contact center isn’t exactly hard to find.
The tech is evident within marketing decks, product sheets, and LinkedIn feeds.
But while the hype is deafening, separating real value from empty promises isn’t getting any easier.
Too often, buyers are told that AI will ‘transform customer experience,’ ‘reduce headcount,’ and ‘cut costs.’
That’s true … sometimes.
But believing AI is a silver bullet is one of the most common (and costly) misconceptions in customer service.
As Simon Adnett, VP of Sales at Enghouse, puts it:
There’s this belief that you can just buy AI and it’ll solve all your problems.
“But without understanding your business and your processes, you could just be automating bad processes and therefore creating bad experiences faster.”
So, how can buyers cut through the noise and find AI that actually delivers?
Here’s your five-step guide.
Step 1: Forget the Hype, Define the Problem
Before falling in love with the latest CX buzzword – be it ‘agentic AI’ or ‘autonomous agents’ – start by asking a simple question: what do we need to fix?
Adnett suggests that organizations should think about AI like they would a job candidate.
“Write a job description,” he says.
“What process are you trying to improve? What’s the expected outcome? And how will you know if it’s working?”
Too many businesses skip this step. They deploy AI because it’s new, not because it solves a specific problem.
The result? Expensive tools that don’t integrate properly, frustrate customers, and drain budgets.
“It’s not about using the shiniest tech,” Adnett warns. “It’s about using the right tech.”
Step 2: Prioritize Process Understanding
Once you’ve defined your goals, the next step is process mapping.
But here’s the catch: just because a process works for a human doesn’t mean it will directly translate to being automated. Existing processes may also not be delivering the customer experience you think they are, as Adnett explains:
Some organizations take a broken process and just automate it. Now it’s just a bad automated process.
Instead, Adnett recommends starting by identifying what ‘good’ looks like.
Enghouse uses AI to analyze every interaction across a contact center, measuring sentiment shifts, uncovering friction points, and finding patterns that manual QA teams miss.
“You have to understand whether you’re delivering the service you think you’re delivering,” Adnett explains.
“That’s where AI becomes powerful; not as a replacement, but as a lens.”
This kind of deep listening, generally referred to as ‘Voice of the Customer,’ can reveal high-value areas where automation makes sense – and, just as crucially, where it doesn’t.
Step 3: Don’t Underestimate Integration
Integration isn’t necessarily the most exciting part of AI, but it might be the most important.
If it were a member of a band, it might be the bass guitarist. While it might not deliver any face-melting solos, without it, the song would have no rhythm.
A disconnected AI tool – even one with great NLU (Natural Language Understanding) or sentiment analysis – isn’t going to fix CX if it can’t talk to your CRM, ticketing system, or knowledge base.
“You can have amazing AI, but if it can’t access the right data or escalate correctly, the customer suffers,” says Adnett.
“We’ve all had that experience: ‘I’ll transfer you to an agent,’ followed by a long hold and the dreaded question: ‘How can I help you today?’”
A true CX-ready AI solution ensures contextual continuity. It doesn’t just hand off; it brings the conversation, history, and sentiment data with it.
According to Adnett, that seamless transition between automation and live agents is a key metric for success.
Step 4: Choose Vendors That Partner, Not Just Sell
AI isn’t a plug-and-play tool. It requires fine-tuning, monitoring, and iteration.
So, choosing a vendor that offers and understands more than just software is critical – they need to guide you through the journey.
Adnett outlines the danger of what he calls “the fire-and-forget model.”
“These tools can drift. They need guardrails, updates, and ongoing performance checks, just like human agents.”
Enghouse tackles this by combining its analytics capabilities with consultative support.
The vendor helps customers analyze sentiment and behavior at scale, validate automation ideas, and avoid the trap of blindly rolling out bots that no one wants to use.
Sometimes, their platform reveals surprises even the customer didn’t expect.
“They think they know the problem – but when we run the data, we find a completely different driver,” Adnett says.
“That’s when real progress happens.”
Step 5: Measure Impact Beyond Efficiency
Many AI projects are justified by metrics like reduced average handle time or agent workload. However, focusing too narrowly on efficiency can backfire.
If customer satisfaction drops or AI misfires in critical moments, your brand may take the hit.
That’s why Enghouse encourages organizations to measure outcomes, not just outputs.
Did sentiment improve? Was the issue resolved on first contact? Did the customer leave the interaction happier than when they started?
“In some cases, a complaint call that turns positive is a better experience than a neutral one. That shift in sentiment, that’s the gold.”
Spotting AI that Delivers
The AI hype machine isn’t slowing down, and neither is the complexity of customer expectations. But savvy CX leaders know that flashy demos aren’t the goal; results are.
So, how do you spot AI that delivers?
Look for vendors who ask about your processes, instead of just telling you about their features.
Demand transparency around integration, handoff, and measurement.
And remember that successful AI isn’t about replacing humans; it’s about helping them deliver better, faster, and more empathetically.
Visit the company’s website to learn more about Enghouse and how it helps provide AI solutions that truly deliver.
You can also hear directly from Simon Adnett, VP of Sales at Enghouse, by checking out this exclusive interview with CX Today.
