The question is no longer whether AI belongs in the contact center. The numbers have already answered that. The harder question, the one that determines whether the investment actually delivers, is whether the business has what it takes to sustain it. For Chris Scimone, Director, Solutions Architecture & Engineering at New Era Technology, the breadth of considerations means few organizations can go it alone:
"Keeping track of the technology and the constant wave of change is simply too much for most teams running a contact center."
The gains are available, but the organizations seeing real returns are those that have stopped trying to carry the full weight of it internally. The smarter move is to work with a partner who absorbs the operational complexity so the business can stay focused on what the deployment is actually there to do.
The Right Starting Point, Without the Guesswork
Most organizations arrive at their first contact center AI decision without having made it before. That is the reality of deploying technology that is moving faster than internal experience can accumulate. Yet the starting point needs to be informed by pattern recognition built across different industries, tech stacks, and levels of readiness. Working with a partner like New Era Technology, that pattern recognition is brought to every engagement. Drawing on a experience in CX, AI, digital experience, and digital transformation that most organizations cannot replicate internally, New Era Technology helps businesses determine where best to start based on its knowledge and the business' operations. To ensure the strength of that initial deployment, New Era goes a step further. Its digital experience practice ensures that the data feeding the models is matched to the specific business need before deployment begins, improving accuracy, and reducing hallucinations. Once this foundation is in place, steps are taken to ensure an AI rollout can be deployed while minimizing teething problems. For instance, before AI is put in front of customers, it can be deployed in an assistive capacity for contact center agents. This brings real-time suggestions, automated task execution, and call wrap-up; real efficiencies without risking exposure to the customer. "If it's done really well, it's working behind the scenes, making the customer's and the employee's life a little easier," Scimone says. "That's the goal." The feedback generated from these use cases then helps the company and its partner New Era Technology know exactly what needs to be tuned before the deployment moves further. Existing investments in third-party AI tools outside the CCaaS platform do not need to be abandoned with this approach either. New Era's bring-your-own-AI model ensures they are integrated, allowing custom models operating under stringent GDPR requirements to be incorporated. With this experience behind you, the starting point for AI in a contact center is no longer a guess. AI deployment becomes less trial and error and more test and scale.
Less Time Watching the Stack, More Time Seeing Results
Once an AI deployment is live, the nature of the challenge changes. The platforms it sits alongside keep moving, and without the infrastructure to absorb that movement, even a well-built deployment can start to drift. The CCaaS platform updates. The AI model changes. The CRM pushes a new release. Token costs fluctuate. Security requirements evolve. Most internal teams running a live customer-facing operation cannot stay on top of all of it at the same time. With New Era Technology's Managed Blue, they don't need to. The managed service ensures the infrastructure maintenance needed to keep the deployment stable does not become a burden for the internal team. This management also extends to vendor updates. Every Monday, update notifications from CCaaS platforms, AI providers, and CRM systems are reviewed by New Era Technology's solution architects and customer success managers, tested in demo environments, and distilled into what is actually relevant for each customer's specific setup. The noise gets filtered out before it arrives, and IT teams understand how an update in one platform will affect the broader technology stack. "We serve as the clearinghouse for both communicating and evaluating those changes," Scimone says.
