Why CX Operations and the Contact Center are the Place for Your Company’s AI Investment

Your company wants to spend money on AI, but there’s only so much budget to go around, of course. Different leaders across the company are vying for a slice of that AI budget, hoping to drive productivity and improve their area of the organization.

The problem, however, is that not all areas of the company can implement and reap the benefits of AI in the same manner, and with the same benefits.

Because of this, there’s an unspoken truth about the current rush toward AI investment — a lot of companies are losing money because they’ve been choosing the wrong places for that investment. Rather than try to invent places where they can plug in an AI tool somewhere they hope will yield a reduction in workforce or drive revenue, they should look for areas of their company where it can deliver immediately.

One of those places is the contact center and CX operations, because AI-powered tools can reduce the time a live agent needs to resolve a customer issue, making your agents more efficient and providing immediate savings.

And, perhaps most importantly, the contact center is already a place where well-deployed, tested, and proven AI solutions are already delivering ROI for companies across industries. In other words, AI in the contact center is not an experiment, as it might be in other places across your company.

Let’s dig deeper into why contact center and CX leaders have an excellent case to bring to their C-Suite when it comes to petitioning for a larger slice of their organization’s AI budget.

Why do Most Enterprise AI Investments Fail?

MIT’s Project NANDA study on enterprise AI failure rates showed that 95% of GenAI projects don’t yield a positive ROI. This comprehensive study dug deep into more than 300 publicly disclosed AI projects at 52 different companies. Through interviews with leadership at these companies, researchers at MIT Networked AI Agents and Decentralized Architecture (NANDA) lab found that only 5 percent of AI initiatives have a measurable P&L impact with most never moving beyond pilot stage.

This isn’t to say that generative AI (GenAI) and agentic AI technology isn’t remarkable and becoming increasingly advanced. You likely see news of AI breakthroughs happening on weekly basis. The problem lies with the investment, deployment, and execution of the technology within the company’s workforce.

In fact, when inspecting models, MIT researchers found most of the AI models to be sufficiently advanced. It was in the execution and inability to scale where the failure occurred.

“Stop investing in static tools that require constant prompting, start partnering with vendors who offer custom systems, and focus on workflow integration over flashy demos,” the report states.

When it comes to AI for CX, a trusted provider should have real-world examples of their AI outcomes. They shouldn’t be showing you hypothetical ROI figures or scrolling through a demo of a solution that has yet to yield actual results.

The AI Job Paradox

AI will continue to disrupt the traditional workforce as models improve and use cases widen across different industries. AI isn’t going to replace every office worker, but it’s nevertheless a disruption as business leaders try to cut costs.

In Q1 2026, the Challenger, Gray & Christmas job cuts report says that about 13 percent of all job cuts in the tech sector were cited by leadership as being attributed to AI.

But there’s more to this figure.

Many companies have had to cut head count because they’ve over-invested in AI technologies that aren’t yet yielding returns. You’ll sometimes hear this referred to as “AI-washing,” wherein a firm will tout investments in AI to perhaps seem more technologically advanced, bleeding edge, or forward thinking than the rest of the market.

The problem is that in order to pay for these investments, companies have to cut jobs. Now, in the case of organizations whose AI isn’t performing up to the level of their investment, they’re left with fewer people to tackle the challenges their AI technology failed to solve.

So now, they’re out money and people.

The Square AI Peg

So, this begs the question: Why are companies losing money on their AI investments?

A lot of this is because they’re trying to pound square AI pegs into round areas of the business. While HR costs might be high, for example, deploying an AI hiring tool that requires extensive services and stand-up time might not be the answer. The same goes for other areas of the company, like logistics planning or operations management — there are big holes that are tempting to fill with AI.

At Verint, our Da Vinci AI is designed for quick deployment times within your current CX ecosystems. The idea is to deliver AI that’s already been proven, rather than leave your company with an AI experiment that could be one of those that fails, as we mentioned above.

Verint Da Vinci AI uses the right model for the right use case. Da Vinci’s AI scientists are constantly working to include the latest AI models so your company isn’t left worrying about models becoming obsolete or inefficient. Verint handles all of this behind the scenes, which future-proofs your AI investment and protects it from failure.

So you might be wondering, then, why AI projects can fail.

It’s because companies are not investing the right AI for the right use case. Or perhaps they’re tossing AI at a problem that doesn’t need AI and losing money and valuable employees along the way, all while falling even further behind the technological adoption curve they need to succeed in the future.

Here are some other reasons AI deployments might never deliver:

• Again, organizations are buying experiments instead of going with vendors who can quickly deploy.
• They have not addressed their specific business issues that require AI. In other words, they haven’t done their homework prior to investing in AI.
• Companies looking to the wrong part of their company for AI by trying to automate something that either can’t be automated efficiently, or doesn’t yield the right results.
• Compliance, reliability, regulatory concerns were not addressed by leadership, leading to a project being abandoned.
• No clear success metrics were ever established.

Is AI in the Contact Center Still an Experiment?

Because of their very nature, contact center and CX operations have become a desirable place for AI investment. Today’s agentic and generative AI technology can quickly solve problems for customers, speed up processes for live agents, summarize call logs, handle scheduling, and more.

In a space where speed and efficiency are paramount, AI has proven an excellent solution for CX and contact center use cases. Further, customers have embraced this technology to the point that they expect to have an excellent service experience with an AI agent OR a human agent — and companies need to be ready to fulfill that expectation.

Again, this is because:

• Excellent customer service is not an option. It’s a must for a company in order to build loyalty. A customer who has a bad experience will go to a competitor rather than tolerate long hold times or bad information.
• Offshoring your contact center is no longer as efficient as it once was.
• Agentic AI is very good at the routine tasks facing contact center agents.
• Consumers are open to talking with an AI agent instead of a human if they can get results.
• Contact centers are home to massive amounts of data that can be processed with AI to uncover insights and business strategy.

Consumers and Employees Actually Like Using AI for CX

When it comes to AI in the contact center, there’s no longer a need to train your customers to engage with your company through AI-assisted technology. By now, many customers are quite accustomed to speaking or messaging with an AI agent, as this technology has moved beyond the frustratingly basic question-and-answer chatbots of yesteryear.

At Verint, we conducted a survey of 5,000 consumers to see how they view CX, how they prefer to engage with companies, and their comfort with and confidence in using, AI. Here’s what we learned in the Verint State of Customer Experience 2026:

• 42% of respondents said they have higher CX expectations than ever before.
• 69% said they’d use AI solutions as long as it solved their issue.
• 41% of those ages 18—34 said they prefer to engage through AI versus a live agent.

That’s only half the story, though. Our research also shows that AI is vital for contact center employees and serves to improve their experience. The hybrid of AI for self-service and AI for employee efficiencies has proven to deliver the best return on your AI investment. In another piece of research, our State of Employee Experience 2026 report, we learned:

• 45% of calls into the contact center require agents to search for answers to customer questions.
• 61% of agents expect their roles to become more complex or technical because of AI in the next 3 years.
• 9 out of 10 agents say schedule flexibility is important when choosing a job, confirming its critical role in agent retention.

The Verint Approach

At Verint we’ve been helping companies deploy AI within the contact center for years. Our deployments are built around the failure points MIT identified: workflow integration, defined success metrics, and use cases chosen deliberately — and we’re not going to drag you down with pilot programs.

When you go to your C-Suite as a CX or contact center leader asking for funding for your AI solution, you can be sure that you’re not investing in an experiment, but rather proven technologies that have already delivered millions for companies across a number of industries, regions, and use cases.

Adding AI to your contact center shouldn’t feel like a risk — but with things moving as quickly and disruptively as they are in today’s AI landscape, we understand that concern. Here’s how we protect your investment:

• We integrate into your existing systems to give you the right AI where you need it, for the right results. You don’t have to abandon the investments you’ve already made in other platforms that are working well.
• You can start small with a few use cases within your contact center and build from there.
• Our teams of AI scientists are working behind the scenes in Verint Da Vinci labs to update AI models so you’re not left high and dry as model and systems evolve. This way, your solution is using the best models for your needs, without you needing to worry.
• We don’t play the token game. So, we won’t come asking for more tokens mid-project, as other vendors in other areas of your company may do. We handle that on our end.

How Should You Roll Out AI Across Your Contact Center?

As you’ve likely realized at this point, you shouldn’t use a fire hose to spray AI across your entire contact center and hope for the best. That’s not how contact center AI investment works — or at least not how it should work.

There will be, however, CCaaS vendors who try to sell you an entire platform, requiring you to rip and replace all the systems and platforms in which you’ve already invested. That’s not smart AI investment either, and not how we do it at Verint.

Within the contact center, you should look within your CX and contact center operations and see where you need help. There very well might be AI tools that aren’t a fit for your company’s specific CX needs, and that’s OK. Find the problems — like long wrap-up times, long handle times, inability to handle volume spikes, lack of digital self-service options — and work from there.