Laying the groundwork: A starter’s guide to AI-ready contact centers

I spent 13 years in the contact center and business process outsourcing space, starting as an agent and working my way up to director of client services. My industry experience gave me the insight to understand how transformative that generative artificial intelligence (AI) will be for contact centers. The emergence of this technology also raises an important question: How can contact centers of all sizes begin and support their journey into generative AI with seemingly daily new advancements? 
 
While generative AI is still in its early phases, it’s crucial to begin preparing and supporting your contact center for the upcoming technology rush in order to maximize potential benefits. These four steps offer a great starting point.

Consider the Move from On-Premise to Cloud

Many IT managers and specialists have seen leaders in tech and information security favor on-premise systems—often due to security concerns, control over their infrastructure, and a perceived lack of certified personnel to handle cloud services such as Amazon Web Services. Moving to a cloud-based infrastructure, however, is a key step toward embracing AI technologies. 
 
The transition might seem challenging—but the contemporary IT landscape provides a wealth of resources for professionals keen on expanding their expertise in cloud technology. This is an opportune moment to build that proficiency, given the increasing importance of cloud services in enabling advanced solutions such as AI.

Many of us have seen attempts to weave AI into existing, somewhat outdated on-premise systems. This approach often reveals a burdensome reality: The costs of expanding hardware capabilities to accommodate extra software and maintaining around-the-clock operations to manage peak workloads can rapidly escalate. More sustainable solutions are often found in cloud services, whose flexibility and cost efficiency provide a compelling alternative.
 
Embracing the cloud greatly simplifies the integration of AI-powered tools and analytics. The advanced cybersecurity measures and data protection features that come with cloud platforms enhance this transition or integration. Cloud-based systems also offer superior access to real-time data—a key component for AI systems to effectively learn and adapt. It’s clear that the long-term advantages of adopting a cloud infrastructure outweigh the challenges.

Objectives and Initial ROI Analysis

To successfully integrate AI into your contact center, you must first pinpoint and articulate your long-term goals. Be explicit about your intended outcomes—such as enhancing call deflection rates, improving the employee experience, boosting Customer Satisfaction or Net Promoter Score, or reducing Average Handle Time. These goals will act as your roadmap, informing the specific features and functions your AI solution should possess. Initiate an ROI analysis once your goals are clear, but remember that this ROI analysis is merely an initial step. Begin by establishing the baseline of your current metrics and identify the areas with the most significant potential for improvement. The AI solution you plan to deploy could incur substantial costs, and focusing on areas with significant room for improvement can help offset these costs.

Complete Call Type Analysis

A “call type analysis” involves categorizing calls based on purpose and complexity. This process assists contact centers in predicting customer requirements and effectively allocating resources. This analysis can help your organization identify areas where agents might need help to provide adequate customer support. 
 
Technologies that assist agents, including features such as “Next Best Action” and automatic summarization of “After Call Work” (ACW) notes, are transforming the operation of contact centers. Even though these agent assist technologies may not fully utilize generative AI, it’s crucial to consider the potential of real-time alerts or assistance for the most impactful call types. The insights derived from such information can be instrumental in training AI systems—including agent assist tools—to recognize call types and respond appropriately. 
 
Contact centers can pinpoint bottlenecks, redundancies, and inefficiencies by analyzing the existing call flow process. Establishing a well-orchestrated call flow can help AI systems identify areas ripe for improvement and automation. 

Reduce Waste

Contact centers should critically assess their operations to locate areas of waste and inefficiency. By identifying these areas, contact centers can start making educated predictions about where AI can offer the greatest impact. Implementing agent assist technologies alone can decrease dependencies on time-consuming knowledge base lookups and nearly eliminate ACW. 
 
Such analysis helps to prioritize AI integration projects, maximizing the return on investment by streamlining and standardizing processes, thereby cutting unnecessary costs. This strategic approach ensures the implementation of AI technology is both cost-effective and beneficial to the overall productivity of the contact center. 

Conclusion

AI is already transforming many industries, and contact centers need to start laying the groundwork to stay competitive and make the most of the opportunities that AI brings. The integration of AI-driven tech has already begun, and it will soon become necessary for contact centers. It’s up to contact center technology teams to quickly implement new and innovative technology — and as I saw during my time in contact centers, success means ensuring clients know how we stand out against our competitors.