Artificial intelligence (AI) became a core part of contact center technology surprisingly quickly. Tools like chatbots, keyword analysis, and sophisticated automation have engrained AI into customer support, whether they’re being used to assist agents with daily tasks or provide customers with self-service.
There’s a wide array of AI capabilities available for contact centers, each with their own levels of complexity, and each contact center environment will have its own specific needs. So, how do organizations find the right level of technology and complexity for their contact center?
We can get a better understanding of this by breaking down contact center AI into a maturity model, like that created by MiaRec. This model looks at five different levels of complexity and impact, serving as a roadmap to help organizations see through the hype and find the features they need.
The Levels of AI Maturity and Complexity
AI technology is at the core of many of today’s contact center capabilities, enabling agents to help customers more quickly and efficiently, gain new insights into customers and contact center operations, and automate processes. MiaRec’s contact center AI maturity model sorts its various features and benefits into these levels:
- Support: Basic AI-powered features provide small benefits designed to help agents and supervisors with everyday tasks, such as call transcriptions and keyword analysis.
- Automate: Generative and Conversational AI features are added to the contact center process to help with functions such as call scoring, thereby improving efficiency.
- Augment: AI-powered features take a more prominent role, providing suggested replies, notes, and analytics to guide and assist agents.
- Empower: This adds additional layers of intelligence to the AI technology, providing agents with real-time input from AI-powered assistants.
- Transform: The final level uses AI-driven analytics to improve decision-making and transform contact centers, improving revenue as a result.
Contact center managers and decision-makers need to understand what each level brings to their business and find the level of advanced features that works best for them.
Level 1: Support
The first level is built around using basic AI-powered features to support individual agents and improve contact center efficiency. This uses Machine Learning and Large Language Models (LLMs) to provide basic functions, including:
- Transcribing call recordings
- Syntax-based keyword identification
This level provides basic support, but does not transform processes. For instance, the transcriptions can be used to assist with scoring calls, and the keyword identification can help find calls mentioning certain topics. Additionally, it does help improve Average Handle Time, Customer Satisfaction (CSAT), and First Contact Resolution (FCR) by providing agents with helpful features to make them more efficient.
Level 2: Automate
The second level adds more automation to the contact center process. This is the stage that introduces Generative AI, and includes:
- Generative AI-powered automated quality management that can automatically score 100% of calls
- Customizable scorecards for compliance and training
- AI-driven insights for agents and customers
These features are designed to automate QA processes in contact centers, freeing up supervisors for more valuable tasks, including training and coaching. Automation also provides more visibility into agent performance, as it can accurately score every call. Contact center supervisors will see several benefits from this level, as the features here can help them better manage, score, and train their agents.