AI is moving deeper into business communications, and pressure is rising fast. Boards want strategy. Customers expect smarter service. Employees want tools that reduce daily friction.
Yet many mid-market companies face a difficult choice. Move too fast, and projects may fail. Move too slowly, and rivals may improve service first.
Mark Sher, SVP of Product Marketing at Intermedia, says adoption is not uniform. Some businesses rush ahead. Others demand clear returns before investing.
“Some say, ‘I’ve got to have it right now. I don’t care,’” he says.
“Others are more pragmatic – they want to understand the ROI, they want to understand what they’re going to get in return. And there are others still crossing their arms and saying, ‘I’m not ready.’”
That pragmatic group may have the strongest position. They often start with practical tools. Then they expand once teams understand the impact.
The lowest-risk use cases remain simple and familiar. Meeting summaries can capture decisions and action items automatically. Call summaries can update customer records after each interaction.
For contact centers, this can save agents valuable time. It can also improve customer history and follow-up quality.
“I don’t think there’s a ton of risk with that,” says Sher. “And there’s a lot of upside – around productivity, efficiency, a better employee experience and a better experience for the customer.”
However, more advanced AI needs stronger preparation. Voice agents can answer calls, book appointments, and route requests. But they need accurate business information to perform well.
“To make that work right, you have to invest in infusing it with the right kind of business data,” says Sher.
This point matters for VoIP engineers and IT leaders. AI success often depends less on the engine itself. It depends more on clean data and documented workflows.
“There’s a saying with computer programs – garbage in, garbage out,” Sher says.
That warning remains highly relevant. If policies, customer data, or product details are outdated, AI will struggle. Poor answers can damage trust and increase support workload.
Another key decision involves platform design. Many companies add separate AI tools onto existing systems. This may look simple at first.
In practice, it can create new management problems. Each tool may need its own data source. Teams then maintain several versions of the same information.
A native AI platform can reduce that burden. It can keep communications, customer data, and automation closer together. This helps teams avoid conflicting answers across different tools.
For telecom and unified communications teams, the lesson is clear. AI should not become another disconnected layer. It should support the core communication environment.
Intermedia describes its model as AI teammates. These assistants support employees during routine tasks. Some agents can also complete specific actions independently.
In contact centers, real-time guidance may deliver visible value. AI can listen during conversations and suggest relevant answers. Agents then respond faster and with greater accuracy.
Still, businesses should not treat AI as a shortcut. They need clear use cases, reliable data, and measurable goals.
The winners will likely move with discipline. They will start small, measure results, and expand carefully. That approach turns AI from hype into operational improvement.
