A lot of people in life will try to convince you that bigger is better.
The realtor, the car salesman, the travel agent, even your server at McDonald’s trying to get you to go for the large fries and drink.
And while sometimes it can be true, it’s not the hard and fast rule that some would like you to believe.
Be honest, which concert did you prefer? The arena show where you were stuck up in the stands watching it on a big screen because you could only just make out the people on stage? Or that time you managed to catch your favorite band in a tiny backroom somewhere before they made it big?
When it comes to the customer service and experience space, many of the major vendors have traditionally been in the ‘bigger is better’ camp; more specifically, they have pushed the narrative that larger AI models translate to superior outcomes.
Giant LLMs promised to reinvent customer service, automate complex workflows, and elevate the entire contact center. Yet many scaling enterprises have found themselves stuck in lengthy deployments, rising costs, and unpredictable results.
According to James Scott, Senior Solutions Engineer at Diabolocom, the inflection point often comes only after real-world testing:
“The big moment is when people realize that hefty, large general-purpose models are not the best for customer experience workflows.”
Teams begin to see quality issues, usability challenges, or unexpected cost spikes. And those frustrations lead to a simple but vital question: do large models really serve the needs of CX operations?
Increasingly, the evidence says no.
Why Bigger Isn’t Better in CX
For Scott, frontline environments rely on precision. They require fast, clear, task-specific decisions, classification, routing, QA scoring, call summarization, and compliance checks. Large general models, he explains, simply aren’t optimized for that reality.
“They’re always trying to be a jack of all trades and a master of none,” he says. “In CX, we want models to do very specific things, and we want them to do those specific things very well.”
One of the most visible consequences is hallucination. Scott puts it bluntly: “Large models tend to hallucinate because there is so much data they can pull on, and because they’ve been told to always give an answer – even if that answer isn’t something it’s 100% confident in.”
The result is an accuracy and accountability gap. Leaders buy into ambitious pre-sales visions, only to hit issues once the system must perform inside a live operation.
As Scott describes it, implementation becomes the moment when “you have to cash those checks… and they don’t quite cash.”
This is where Diabolocom takes a different approach. Rather than layering CX workflows on top of general-purpose LLMs, the company builds its own smaller, purpose-built AI models designed specifically for contact center use cases.
These models aren’t wrappers. They’re trained for defined workflows on customer-relevant data, and packaged for real operational deployment.
The Case for Specific: Faster Deployment, Faster ROI
One of the most compelling advantages of domain-specific AI is speed to value.
Diabolocom reports deployment timelines measured in weeks, not quarters.
“Smaller models are easier to operationalize,” Scott explains. With tighter, curated datasets and a focused purpose, they require far less training and tuning.
“It’s easier to compile the training data, easier to understand, and easier to get that model to a place where it’s doing what it needs to do.”
He compares it to onboarding an experienced employee. “It’s like getting somebody who has worked in an industry for a very long time and training them, versus getting someone who’s a quick learner but has never worked in that industry.
“You would always prefer someone with previous experience.”
This familiarity dramatically reduces testing time and shrinks the burden on engineering teams. Enterprises also benefit from lower compute costs and reduced infrastructure demands; an increasingly important factor as AI budgets come under scrutiny.
Why Orchestration Alone Won’t Fix It
The market is now full of AI orchestration platforms promising a single layer over massive LLMs. Yet Scott cautions that many are struggling to deliver practical ROI.