Artificial intelligence (AI) is a hot topic across industries as companies and consumers adopt the seemingly new and buzzworthy technology. Contact centers are no exception as leaders have been adopting AI applications like chatbots to streamline interactions and provide better customer self-service. And while use cases like this should be the focus, it’s important to also consider the broader modernization plan.

Well-defined use cases will help contact centers get a good ROI on AI, but this focus is more tactical than strategic. This is especially true in larger contact centers that remain heavily invested in premise-based platforms. These deployments have become fine-tuned over many years, with complex, enterprise-grade integrations that are reliable for large volumes of traffic.

While this serves the contact center well for conventional forms of customer service, there are inherent limitations that don’t align well with today’s shift from customer service to customer experience (CX). The latter requires a new kind of agility that integrates across all channels – both conventional and digital – and all types of network settings. Aside from requiring new adaptability around a variety of technologies, CX expectations are no longer static. Technology keeps evolving, and with that comes greater demands on API-driven applications and customization for more personalized customer experiences.

Contact center leaders should consider how these needs can be addressed at scale. Many large contact centers remain premise-based. A sudden shift to the cloud carries risk and uncertainty, so they need a better strategy to keep pace with evolving CX. AI offers a viable way forward within the constraints faced by larger-scale contact centers. Here are two ways AI can help contact centers become more enterprise-ready.

Future-Proof Customer Experience

The larger the contact center – and the more diverse the customer base – the more challenging it is to provide enterprise-grade CX. Achieving this requires more than handling large call volumes or traffic surges. Customers expect that their data remains private and they are protected from an ever-growing range of fraudulent schemes.

To further protect customers, compliance requirements are becoming more onerous as well as complex. Prime examples include HIPPA for healthcare information, SOC 2 and 3 for financial data, and WCAG to ensure accessible web access. As the volumes of data traversing the contact center keep growing, legacy platforms won’t be able to manage the data at scale and in real time, which is what customers expect.

To stay on the cutting edge of CX, contact centers must be able to innovate. Without new technology adoption, they’ll struggle to maintain the status quo and stay ahead of their competitors. AI has the ability to manage data at scale – well beyond what humans or legacy technology can do – and make it actionable in real time.

Many of the factors that impact CX are now data driven. Being enterprise-ready means being able to make every customer interaction safe, secure, compliant, and personalized. As CX expectations keep evolving, contact centers need to future-proof to meet those expectations, which will be increasingly difficult without AI. Customer service is no longer a static concept, and innovation is now about how contact centers adopt new technologies and how they deliver cutting-edge customer experiences.

Future-Proof Agent Experience

CX is just one half of the equation when it comes to future-proofing the contact center and becoming enterprise-ready. Providing a good agent experience (AX) is just as important as providing good CX – this is where AI provides a different form of strategic value.

AI can help make agents be more effective when dealing with customers. The large volumes of CX data that AI can help manage will ultimately flow through the agent, and AI can play a key role in helping them leverage that data in real time. Otherwise, agents will be overwhelmed, and the data will only get in the way of providing good CX, which also leads to poor AX.

Enterprise-ready means doing this at scale, and no number of supervisors can possibly support all agents effectively. AI, however, can do that in the form of virtual agent assistants, where each agent can be “coached” in real time to ensure they stay on script, remain compliant, provide personalized service, and show empathy.

At a high level, AI is well-suited to enable agent assistants. More specifically, it’s Natural Language Processing (NLP) that enables these assistants to understand intent and context. These capabilities may not be perfect today, but they will keep improving over time, making them more strategic by future-proofing AX.

NLP also drives better self-service. Chatbots are a tactical use case for AI, but this is also strategic when viewed as a way to improve and future-proof AX. Today, chatbots may only have limited utility, but thanks to NLP evolution – and now generative AI – their utility will only increase.

This will certainly improve operational performance by automating customer service and agent workflows, all of which will lead to better AX. When considering the need to be enterprise-ready, there is no better way than AI for contact centers to adapt to changing needs quickly and at scale.