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 Interactive, 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.