Assessing your contact center’s readiness for AI is a big topic that we’ve previously written about here. We continue our analysis with three more questions contact center leaders will need to address. Implementing AI requires more than a regular technology refresh and may bring as many challenges as questions.

Before asking any questions, contact center leaders need to recognize that AI is transformational by nature. In this regard, you can never ask too many questions. Here are three more foundational questions to ask whether you’re doing a small pilot deployment or making a major investment to jump-start your efforts to modernize quickly.

Question 1: Are you planning to deploy AI just in the contact center?

This is a fundamentally important question given the shift for businesses to now view customer experience (CX) as strategic. Historically, contact centers have been on their own technology island, investing in purpose-built solutions that had limited interaction or integration with systems across the organization. The current focus on CX is more holistic and has largely been defined by what happens in the contact center.

With the rise of digital channels, customers now have many more points of engagement, not just with the contact center but across other business functions, such as Sales, Marketing, Accounting, and Shipping. These interactions impact CX, and if the contact center continues to operate in isolation, the organization will never have a complete picture of the customer.

This disconnect will only become further exacerbated with AI since its effectiveness depends on having access to data from as many sources as possible. Whereas legacy technologies have limited ability to integrate data across multiple platforms, this is a defining characteristic of AI. These technologies can scale in ways not previously possible, so investments in AI really need to take a broad view, rather than being a collection of siloed applications.

Given that AI is typically being deployed across the organization now and not just in the contact center, contact center leaders need to consider how their deployment will align with these other initiatives. Most organizations lack an overarching AI strategy. In the absence of an overall plan, contact center leaders will likely have to do this legwork on their own. There is no proven blueprint here, but the main takeaway is the need to think beyond the contact center realm when deploying AI for CX.

Question 2: What are your specific CX and contact center use cases for AI?

Whatever use case you have for AI, there will be a cause-effect relationship. A chatbot use case, for example, would enhance existing self-service options. Sentiment analysis would be an entirely new capability, so it’s not replacing or enhancing something else. Each AI-based application will have a distinct impact, and if it’s new, you need to consider how it fits within your current environment.

AI-based applications can be highly integrated with other elements across the organization. Rather than being deployed in a standalone fashion to address one step in a workflow, these applications can connect across entire workflows and, in some cases, automate them end-to-end. This represents a key use case for AI in contact centers that will drive operational efficiencies and even reduce costs through new forms of automation. Other use cases – especially customer-facing – create new capabilities that can improve both agent and customer experience.

Consider using AI for more intelligent call routing to find the best match between the customer and agent. Finding the right match isn’t the end game, it’s just the start, as once that interaction begins, other AI applications will be triggered to help the agent provide highly personalized CX. AI can sift through massive datasets in real-time, so agents can say the right things at the right time to drive the best possible outcome.

Whether planning to use AI in contact centers to enhance or create new capabilities, the applications should not be viewed in isolation. AI can deliver incremental benefits for each individual use case, but the bigger payoff comes from a holistic approach, where these new applications can have a domino effect across the entire customer journey. By thinking this through fully, contact center leaders will minimize effort as well as unintended consequences that could arise from not considering how AI impacts each element across the spectrum of customer care.

Question 3: How will you evaluate AI offerings from vendors?

The questions posed in this series address internal considerations for your contact center’s and business’s state of AI readiness, providing a sound foundation to fully understand the impact AI can have on your contact center to support the business case and drive better customer outcomes.

Once you have worked through these, attention can shift externally to the vendors who will be shaping your AI journey. To determine the right fit, many questions need to be asked and prepared in advance. This means having a clear set of objectives and an understanding of how you anticipate AI fitting into your environment. Otherwise, vendors may view this as a clean slate, where they end up driving solutions based on what works best for their offerings.

With AI, the key to making good buying choices is determining what capabilities the vendor can truly deliver. Aside from still being early in the hype phase with AI, the underlying technologies are complex, and most contact centers lack the data science expertise to assess the technology.

Contact center leaders should also get a baseline understanding of their native AI capabilities. The more extensive those in-house capabilities, the more seamlessly third-party applications will integrate with yours. Since AI in contact centers is constantly evolving, this will be an ongoing requirement, and it may be a red flag if third-party applications are primarily sourced from external partners that you haven’t worked with before.

On a more technical level, you’ll want to understand which Large Language Models (LLMs) they’re based on, like OpenAI, Meta, Amazon, Google, Anthropic, etc. And how will they integrate with your internal, proprietary datasets? If the LLMs are too generic, they may not align well with the parlance of your industry or the terminology your customers use.

You should also assess a vendor’s practices around responsible AI to ensure your customer data is not used to train public LLMs that could be used by your competitors. It will also be important to know what guardrails are in place to prevent inappropriate interactions between their bots and your customers. These are just a few examples that make AI in contact centers new territory for leaders as they seek to modernize, and hopefully, this will trigger others as you get closer to engaging with vendors.