Cutting Through the Buzzwords of AI in the Contact Center


Do you sometimes feel like you are playing buzzword bingo when reading anything about AI in the contact center? You are not alone. A lot of customers who have spent a lifetime in the contact center and survived a world of acronyms like AHT, CSAT, and PICINC are feeling alienated in their own world. Like an outsider looking in.

When we read the line “Some companies are already making the most of blockchain technologies for more robust security and efficiency within financial transactions and data handling, but digital transformation is vital for brands to unlock all they can deliver for their customers.” We thought that enough was enough.

What does that even mean? Some terms are too broad, while others leave too much for interpretation. Good thing that at InflowCX we don’t serve the industry serving the contact center world, we serve the contact center industry.

We have put together a simple FAQ to help ease the minds of our people who are feeling left out and want to be cutting-edge and take advantage of the AI revolution that we find ourselves in.

What types of AI are contact centers utilizing?

There are many types of AI, however, 95% of AI is being utilized effectively and most of the innovation in the contact center is based on Generative and Analytical.

Generative AI is responsive and can generate high-quality text, images, responses, and content and utilize logic based on how it is trained. This is the foundation for AI agents, conversational AI, AI-assisted IVR, and a lot of the customer-facing AI you hear about.

Analytical AI analyzes large amounts of data and processes quickly, sometimes in real-time, and creates actionable insights from that data. For example, you can take all your historical recorded conversations and current conversations from all channels, create a dashboard, and be able to organize that data by anything you imagine. Geographical, time of day, subject, tenure of relationship, you get the idea.

Analytical AI helps you diagnose what type of generative AI your contact center could benefit from. These two types of AI go hand in hand.

How does Generative AI enhance customer interactions in contact centers?

Generative AI improves customer interactions by powering conversational agents, chatbots, and AI-assisted tools, and anticipating customer needs. This leads to more effective query handling, lowering AHT, hold times, and dead space in conversations.

What role does Analytical AI play in contact centers?

Analytical AI can take large amounts of data and identify trends, patterns, sales opportunities, and cycles in seconds versus days without any bias. As humans, we have a predetermined idea of what to expect whereas AI just analyzes the data for what it is and what it is not.

Analytical AI is the fuel that drives the AI engine for contact centers. The more information you feed it, the better your operations will become. Knowledge management, workforce management, and other technologies critical to today’s contact centers rely heavily on analytical AI.

Why is a layered approach recommended for AI integration in contact centers?

The old model and days of “Rip & Replace” are hopefully a thing of the past. While that is necessary in rare instances, a layered approach allows for gradual AI integration. This minimizes disruption and enables a smoother transition while assessing the impact of each implementation.

Implementing one solution at a time allows for proper calibration of that solution and gives you the ability to feel the full ramifications of that technology without any guesswork. With ROI being one of the biggest drivers, this allows proper time and gratitude for that solution’s true ROI.

How does AI transform self-service in contact centers?

AI-powered self-service options like intelligent FAQs, advanced IVR systems, and AI-powered chatbots enhance customer experience and free up agents for complex issues, improving overall efficiency.

Customers have a reasonable expectation of self-service and what they can accomplish without needing an agent to assist. AI can also anticipate a customer’s next ask or step based on historical data at the account level as well as the overall global level. They are taking the best of the macro and micro to raise the CSAT.

What is the importance of conversational analytics in AI implementation?

Conversational analytics offers deep insights into customer behavior by analyzing interactions across channels, aiding in tailoring services to meet customer expectations.

Taking the macro perspective and analyzing that with other demographics like location, spending habits, and tenure of relationship all in real-time can take sales and the relationship to new levels when considering the overall customer experience.

How can we make a case for implementing AI in our contact center with our CFO?

While the numbers and savings that can be realized through some of the AI solutions available are staggering, it does come down to simple math. Start small with a conservative assumption such as a 10% reduction in call volume. How much of a cost reduction would that mean for your contact center?

While we have seen reductions as high as 50% in call volume, it is important to start small to handle reasonable expectations. There are tangible financial benefits as well as long-term benefits and ROI such as agent retention, agent well-being, less turnover, and much more.

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