Is ‘almost’ ever good enough?
Now, I know this sounds like the sort of pseudo-philosophical caption you might see over a photo of a brooding Tom Hardy or Cillian Murphy getting shared on Facebook, but when it comes to customer service and experience, it’s a legitimate area of debate.
And this debate has intensified with the introduction of generative AI.
While the tech has swept through the customer support landscape – promising faster responses, reduced costs, and 24/7 service – many enterprise CX leaders are discovering that it isn’t a panacea.
But does it need to be?
In discussing this dilemma, Gintautas Miliauskas, Mavenoid CEO and Co-Founder, stated:
If you have an 80-90% accuracy rate on generative AI, companies are kind of ok with that as long as they have escalation channels into human support.
However, when the same standard is applied to human agents, the margin for error suddenly feels risky.
This distinction underscores why CX leaders must carefully consider the trade-offs between accuracy and scalability before deploying AI-driven support.
“Enterprises are experimenting with generative AI,” says Gintautas, “but the reality is, in production, the accuracy isn’t always there.”
Why Generative AI Alone Often Falls Short
Part of the allure of generative AI is its flexibility. It can handle open-ended queries and engage in conversational flows that feel natural and intuitive.
Yet, as Gintautas points out, it’s becoming increasingly clear that the accuracy isn’t always there, especially for edge cases or multi-step problem-solving.
This probabilistic nature can create brand risk. Hallucinations or non-governed outputs are particularly dangerous in safety-critical industries, while a lack of source control makes compliance difficult.
“There are just some circumstances that purely require a business or a brand to 100% know what it’s going to say every time,” Gintautas explains.
In other words, for many enterprises, the consequences of a wrong response are too high to rely solely on generative AI.
Other practical limitations include user frustration and workflow dead ends.
Customers aren’t prompt engineers. Queries like ‘doesn’t work’ can break generative-only flows, while clarifier tolerance is low.
Generative AI may explain, but without actions or guided workflows, it leaves customers stuck.
Why Deterministic (Curated) AI Also Has Limits
On the other side of the spectrum is deterministic or curated AI.
This approach emphasizes controlled, pre-defined responses and workflows. It’s ideal for high-stakes, repeatable interactions, such as safety-critical instructions, step-by-step troubleshooting, or regulatory compliance.
However, deterministic AI struggles with scalability.
“Every new issue requires content creation and ongoing maintenance,” Gintautas notes.
Rigid experiences and limited coverage mean curated systems often can’t anticipate long-tail or multi-topic queries.
Moreover, they demand continuous investment from support teams to keep content up to date.
The Trade-Off: Accuracy vs. Scalability
For CX leaders, the choice has historically seemed binary: deterministic or probabilistic. But Gintautas argues this is a false choice:
Many people fall into the trap of conflating generative AI with conversational AI. They’re looking for the tool that’s most conversational, rather than what truly resolves queries
The reality, particularly in physical products and complex enterprise environments, is that neither approach alone meets modern customer expectations.
Multi-step troubleshooting, for example, benefits from visual aids like diagrams or images, along with deterministic paths for safety-critical steps.
“With Mavenoid, we focus on what it takes for a user to resolve their problem,” Gintautas explains.
“The technology just simply wasn’t there for generative AI to create a good enough experience. But now we have this new opportunity to blend the two.”
The hybrid approaches that Gintautas refers to combine deterministic precision with generative flexibility.
For brands managing hundreds or thousands of products, this blend enables high-confidence responses where they matter most, while generative AI handles long-tail or less critical queries.
According to Gintautas, Mavenoid’s hybrid method is: “for every new product, there’s a manual.
“From generative AI, you can upload that manual and instantly start responding to known queries.
But if you curate the experience with images or optimize step order, you truly resolve those queries
The result is a system that scales without sacrificing accuracy or customer trust.
It is also a system that enables continuous improvement through the leveraging of analytics.
While you can leave either generative or curated blind, Gintautas advocates for getting as much feedback as possible. “Escalation rates, resolution rates, impacts on average handling times, even your ticket queues,” he says.
Insights like these ensure that the hybrid model adapts and refines itself, rather than leaving CX leaders in the dark.
Advice for CX Leaders Considering AI
For enterprises exploring AI, Gintautas emphasizes starting with clarity:
“Look at the top contact drivers in your business, and what it takes to resolve those queries. Consider the tools – text, images, actions – that enable resolution.”
Rather than chasing the most conversational AI, he champions a problem-first approach.
It is clear from Gintautas’s insights that accuracy, safety, and scalability do not need to be mutually exclusive.
Deterministic AI provides the trust and control enterprises require, while generative AI delivers the scale and flexibility that modern support demands.
As Gintautas concludes: “The era of the magic wand AI – just press a button and it does it all – is over. The blend is more important than ever.”
For CX leaders looking to take advantage of this ‘blend’, understanding the interplay between deterministic and probabilistic AI is essential.
Leaders who strike the right balance will be able to deliver reliable, scalable, and trustworthy support in an era where expectations and consequences are higher than ever.
You can learn more about Mavenoid and its multimodal approach to CX by checking out this article.
You can also discover the company’s full suite of solutions and services by visiting the website today.
