AI's promise of effortless scaling and smarter customer interactions often hits a wall of hidden costs and unmet expectations, industry leaders said at Customer Contact Week Las Vegas last week. Without rigorous planning, contact centers risk burning through budgets and losing team trust, those leaders said.
The four experts shared strategies for moving past vendor hype and making AI investments that actually stick. Their advice: narrow your focus, demand cost transparency, prioritize reliability over flash, and always design around the customer.
Don't get blinded by potential
Brian Cantor, managing director at CMP, said that leaders often chase individual AI features without considering the friction they create. "We have so many different things that functionally make sense as features, but ultimately, if adding a feature creates three more steps for everyone else or creates a new source of friction, that is something we have to start taking into account," Cantor said. He added that each failed AI investment erodes credibility and makes it harder to get buy-in for future tools. "You're not just going to be able to keep convincing your budget holders to keep trying a new AI platform until we find one that works," he said. "Each one puts your credibility on the line, you get your team's buy-in on the line."
Scalability and cost
Marius Maree, SVP of consumer operations at UnitedHealth Group, said that hashing out long-term scalability and cost before launching a pilot is critical. "It's a tough conversation, but it's a partnership, so we try to meet each other in the middle," Maree said. He added that every partner his company has worked with has been willing to find a compromise after the question was raised, but ignoring the issue upfront leads to painful renegotiations later. Structured training like the AI Learning Path for Call Center Supervisors can help managers build the financial due diligence needed for these early conversations.
Look beyond the demo to long-term considerations
Anant Singh, chief sales officer at Sanas, said that flashy demos can mask long-term reliability problems. "Everyone does a great demo. I don't think anyone has a bad demo or a bad meeting," Singh said. "What matters is longevity, repetition, consistency. You have to show up every time for months or years." He said the real work starts after the initial sale, when the vendor must prove its dependability day in and day out.
Investments start with the customer
Melissa Solis, CEO of Inbenta, said that rising customer expectations mean AI solutions that ignore user needs will fail. "Customers are getting really smart, and they know what a good experience looks like, so they're demanding it," she said. "It's driving companies to have to relook, even if they have a solution, and say, 'Okay, is this the right solution?'" Businesses with an older customer base may need to introduce AI more gradually and maintain human touchpoints. Solis advised teams to learn from vendors how similar companies have tailored experiences that balance automation and personal service.
For customer support leaders, grounding AI strategy in real customer needs is essential. Resources like AI for Customer Support offer guidance on building customer-centric implementations that avoid common pitfalls.
Why this matters for customer support leaders
For customer support professionals, the message from CCW Las Vegas is clear: AI investments demand the same rigor as any major operational decision. Define the problem before shopping for solutions. Press vendors on full cost and scalability. And always check whether the technology aligns with what your customers actually want-not just what the demo shows.
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