A blueprint for GenAI use case identification in CX

Implementing generative AI (GenAI) in your customer experience (CX) is a lot like building a house. Neither can be built all at once. And, in both cases, to arrive at something safe, reliable and fit for purpose, it's all about planning and incremental progress. What you need is a measured, methodical approach that follows a blueprint for a long-term vision.

Pacing, in this case, is everything. The idea is to progress one room, or even one closet, at a time and maintain momentum from that point forward. For GenAI in CX, that means thinking through manageable use cases that fit your blueprint.

A manageable use case will be specific, measurable and something you have a high degree of confidence in implementing effectively. That could be implementing GenAI that summarizes call logs for your agents, or that trains your team by using voice to roleplay customers in different moods or with different needs. It could be any number of things, but the point is, you need a use case and it needs to be manageable. Simultaneously trying to work generative AI into every process or customer touchpoint is likely to create too much complexity and risk compromising the foundation of customer affinity your CX operation has built thus far.

"This is where we're seeing GenAI work in CX for brands today. Trying to do everything at once is too complicated and the pace ends up being way too slow," says Kory Laszewski, vice president of global CX solutions at TELUS Digital. "Brands that succeed are led by need." Again, think of building a house. Being led by need gives you the ability to determine where you need a closet and where you need a set of stairs. In the customer experience context, "You've got to find your problem first," explains Laszewski. As a leader, you'll want to think about your goals and associated target metrics, and determine GenAI use cases that can make a meaningful difference in those contexts.

The opposite approach would be to be led by the solutions that exist. This is a surefire way to build a house of disconnected rooms without hallways. You might have checked the boxes in terms of the kitchen, bathrooms and bedrooms, but if they're not joined together, you don't have a functional house. Remember your blueprint and remember your needs. Today, there is no shortage of generative AI applications that you could plug into your customer experience. The artificial intelligence market is immense — Statista reports that it grew beyond $184 billion in 2024, up nearly $50 billion compared to 2023. The sheer number of available options magnifies the need for critical thought. Will the applications create silos in your data and your organization? Will you experience vendor lock-in? Will you be left with technical debt, which describes the cost of redeveloping, reconfiguring or otherwise reimagining your tech stack? Do the applications fit into your long term plans?

Everest Group survey results: Enterprise readiness for generative AI adoption in customer experience management

Everest Group, supported by TELUS Digital, surveyed 200 customer experience leaders from around the world to determine their enterprise readiness for the adoption of generative AI (GenAI). Discover the results.

Access the report

How to identify use cases for GenAI in CX

Identifying use cases for generative AI in your customer experience begins with an understanding of the aspects of your operation that are exceeding expectations, as well as the aspects that leave room for improvement.

An example of GenAI implementation being led by need

Maybe your average handle time (AHT) is great, but your first contact resolution is not. This could, for instance, suggest that your agents are motivated to reach a quick conclusion when speaking with your customers, leading to incorrect or insufficient guidance and the customer needing to call back to have their needs fully addressed.

Although this is a simplistic example, you could imagine accepting this as a starting point and thinking through how generative AI could be used to help customers and agents. Perhaps you would use a GenAI-powered conversational bot to gather and summarize all of the necessary information from the customer prior to the call, enabling the agent to give prompt and correct direction on the first attempt. Or, conversely, GenAI could be deployed strictly on the agent-facing side, taking in the spoken conversation and surfacing accurate information for the agent in real time.

With generative AI, the possible solutions to a given problem are vast, but do not let that distract you. Don't forget: Your focus is on needs, not solutions.

Sources of generative AI use cases

No comprehensive list of use cases for generative AI in your unique customer experience exists. This is no bad thing, however. By applying human creativity and ingenuity to your needs, you can derive suitable use cases from a number of sources. Just remember to begin with narrow use cases, a recommendation that is echoed in Forrester's Budget Planning Guide 2025: Customer Experience as they explain the risks associated with AI experimentation that is too broad or lacks adequate forethought.

You can find narrow use cases for GenAI in:

• Your customer data: Customer experience leaders should already be familiar with customer data analysis. Both qualitative and quantitative data can yield potential applications for GenAI. While we covered a quantitative example when discussing AHT, there is also a great deal to glean from qualitative data. In this case, your customers might actually be telling you, with descriptive feedback, what you could improve. This type of feedback can offer a great starting point for a GenAI use case. If, on the other hand, the feedback you've received is more ambiguous or in a far greater volume than you can review, don't overlook the opportunity to use the power of AI to analyze volumes of quantitative feedback and tease out important insights.
• Your frontline team members: Want to know what's working and what isn't? Asking your frontline team members is a good place to start. Interviewing agents will help you to understand what they're hearing, what they're struggling with, as well as their ideas for how things could be improved. Agent-facing applications for GenAI can have a significant positive impact on your customer metrics.
• Your leadership peers: Tap your leadership team for input. If you don't have a sense of the problems your peers across the organization are trying to solve, you run the risk of creating or affirming silos. Much can be learned from other departments like Finance, Sales, Marketing and Human Resources that can inform your customer experience. For example, your Product Development team might be seeking ways to gather more actionable customer feedback. This could inspire you to implement GenAI tools that analyze customer interactions across channels to identify common pain points or feature requests, informing both CX strategies and product improvements.
• Industry benchmarks and case studies: It's a good idea to develop a sense of where brands are enjoying success with GenAI in CX today. In some cases, you might see examples that could be applied in the same manner within your organization (e.g., post-call transcription for call summarization purposes). You might also be able to exhibit lateral thinking by seeing something that is working in one way or in one context, and modifying elements so that it works for you.
• Trusted partnerships: A capable partner can be instrumental in your GenAI journey, bringing expertise and adding scale where you need it. Such a partnership can help you swiftly identify high-value use cases, pilot solutions and scale the implementations that are working for you. Plus, by leveraging your partner's experience and resources, you can navigate complex regulations and minimize risks associated with technology investment.

What to do once you have a use case for GenAI in CX

Once you've made progress mapping use cases to your overall blueprint for generative AI in CX, a new question emerges. To get what you need, should you build or buy?

Building in-house GenAI solutions

Building your own technology means you'll be able to align its capabilities to your needs. As a result, you aren't likely to bump into unforeseen limitations, create silos or need to implement workarounds to make the technology work for your business. Keeping things in-house also means you'll have full control over the security and privacy relevant to the technology.

Of course, many organizations simply don't have the in-house talent to develop their own GenAI solutions. In an Everest Group survey of 200 CX leaders, supported by TELUS Digital, the number one reason that influenced a respondent's decision to outsource or take a collaborative approach to GenAI implementation was "Limited resources and internal expertise for in-house implementation." And, even for those who have the necessary skill sets in their organizations, the experts are likely trying to work through a long queue of requests, meaning that it could be a long time before you have something in production. Indeed, in the same GenAI implementation survey, another top reason why respondents opted for outsourcing was "The need for faster implementation with outsourced expertise."

Buying third-party GenAI solutions

There are absolutely performant generative AI solutions on the market today that could match up with your use cases and give your CX a meaningful lift. Such technology can be deployed relatively quickly and without the same significant demand for in-house expertise. In circumstances where you are trying to replicate use cases and results you've seen elsewhere, buying third-party solutions could provide just the ROI you're looking for.

With that said, if you're opting to buy third-party GenAI solutions, there are at least a few considerations you need to keep in mind. Perhaps most importantly, you need to have a firm grasp of the vendor's ability to deliver security and privacy. In a recent Salesforce survey of 500 IT decision makers, 71% of respondents said generative AI will introduce new security threats to their data. If you're buying GenAI solutions or working with a CX outsourcing partner, it is imperative that you do your due diligence and place your trust only in those who can maintain it. And, to go back to our house building analogy, you'll want to understand how the third-party solution fits with your blueprint. If you end up with several different GenAI solutions to address all of your requirements, there's a risk that they won't sync up and that you'll be left with data silos. In this case, you might end up with technical debt, which could sap your time, energy and hard-earned budget.

Count on a partner to identify and act on GenAI use cases in CX

For those torn between building or buying the generative AI solutions to address their unique customer experience opportunities, there is a third option.

Partnering with a trustworthy firm that has their own in-house expertise in development, artificial intelligence and CX is a real asset if you're looking to build your own bespoke GenAI solution. Such a partner can be held accountable to deadlines and performance targets and set you up for success while you focus on other aspects of your operation. What's more, they'll be able to help you access their own proprietary tools, as well as a range of additional tools through their network of technology partners. This can be a great benefit to brands looking to implement innovative new tech without taking on the risk of hefty technology investment.

TELUS Digital applies nearly two decades of CX experience in our work helping brands identify and act on generative AI opportunities. For example, with our GenAI Jumpstart program, our experts help brands transform their ideas into reality in just eight weeks. With a path-to-production focus, the GenAI Jumpstart accelerator program can identify use cases, build powerful risk mitigation tools and deliver a bespoke prototype to demonstrate the value of AI to your business.

Few houses are built alone, and for good reason. It's a lot of work, it takes a lot of time, it's a big investment and a lot could go wrong. Much the same can be said about implementing generative AI in customer experience. If you're ready to take forward strides with generative AI, and see the value in a partner you can count on, get in touch today.