3 strategic ways to use AI for CX

For every contact center, the big elephant in the room for 2026 is AI. So much possibility, so many unknowns, so much pressure to deploy, so much! No technology is evolving faster, and with so much transformational impact, there is a lot at stake.

It’s far riskier to take a wait-and-see approach than taking on a limited proof-of-concept AI deployment, and wherever you are on that journey, you need a strategy.

Why AI for the contact center?

The starting point for this strategy is to recognize why the contact center has always been a leading use case for enterprises to adopt AI.

Data is the oxygen for AI, and the contact center by its nature is a data-rich environment. No other operation in an organization relies so heavily on long-established KPIs to measure agent performance and operational efficiency. Add CRM and other customer-facing applications, and you have an end-to-end series of datasets to measure CX on a continuous basis.

While the quality, completeness and accessibility of all that data is a separate issue, the contact center provides one of the best environments for deploying AI, as well as generating tangible results to justify the investment. Most all contact centers are struggling to modernize in the face of ever-rising customer expectations, and I would argue that AI presents the best opportunity for CX leaders to close that gap, not just in terms of the greatest impact, but also in the shortest time possible.

What’s the strategy?

This requires a much longer conversation, but based on my industry perspective as an analyst, there are three core drivers for shaping your AI strategy.

1. Know what AI is and is not

Before jumping head-first into AI, CX leaders will generally not have a data science background, and that itself can be a valid adoption barrier. In cases where contact center deployments have been hardened over decades of costly hardware and customized integrations, AI can easily be seen as disruptive and too risky given how complex and new all this is.

A strategic approach needs to begin with a basic understanding of what AI is and is not, especially when vendors are positioning their offering as a silver bullet to all your CX challenges. In order to make good buying and deployment decisions here, CX leaders will need to get up to speed on AI basics, either with upskilling research or by engaging with a consultant with expertise for both AI and the vendor landscape, if possible.

2. Identify clear use cases

AI itself is not a technology, and nor is it a horizontal solution for all your needs. The good news is that many companies are having real success with AI across the CX spectrum, and the common thread is having well-defined use cases. No AI deployment works to plan from the outset, and the best way to mitigate risk is to start with operational use cases.

This typically involves automating a process or a workflow that is not customer-facing. Once the customer journey is mapped out, it becomes easier to identify the pain points where AI-based automation can reduce or eliminate the manual inputs that slow things down and are prone to human error.

Many CCaaS/CX vendors offer purpose-built bots or applications to address these needs, and they represent a fairly safe way to start with AI. The strategy here is to build on successes, one use case at a time, and gradually build to more complex scenarios where AI can have a greater impact on CX.

3. Focus on outcomes, not KPIs

For AI to have a transformational impact, CX leaders will need to think differently than they have with legacy contact center technologies. Success metrics for the latter — KPIs — only reflect a subset of where AI can provide new value for the contact center, as well as the business overall. KPIs can measure agent performance, but with today’s focus on CX, other metrics are needed, and this is where AI supports the bigger picture.

That bigger picture is what makes AI strategic for both CX leaders and senior management. They will be less interested in how AI does what it does, and more interested in driving outcomes that are relevant to them. For CX leaders, examples would be improving agent retention or having bots automate a higher percentage of inquiries.

Similarly, examples for management would be increasing share of wallet from customers, mitigating losses from fraud or increasing compliance levels. These outcomes go beyond KPIs and speak to business-level impact — making AI strategic beyond the contact center.

The takeaway

There is much more to consider for taking a strategic approach with AI, but these drivers will provide a solid foundation. The technology around AI can be transformational in ways never before possible, but it won’t come to fruition unless the right thinking is in place, and that’s why having a strategy is so important.

In my next article, I’ll build on this foundation by examining how to set the right expectations, not just for AI-based technologies, but also for how they should be used by your agents and supervisors.