3 Essential Steps to Scaling Conversational AI for Customer Engagement

Conversational artificial intelligence is at a tipping point. Beyond customer support, these solutions are being embedded across marketing, sales, IT, and even on search platforms — hinting at a soon-to-be ubiquity. In 2023, conversational AI’s market share was valued at $5.8 billion. By 2028, it’s projected to hit $31.9 billion.

These are not the stilted chatbots or frustrating IVR (interactive voice responses) you may remember. This conversational AI can glean tone and sentiment from a conversation, pull on a person’s historical data for sharper context, and answer increasingly complex queries … if it’s implemented correctly.

There’s a misconception that AI alone is this self-scaling panacea: you plug it into your tech stack and off it goes. However, the success or stagnation of conversational AI depends on the invisible architecture underneath: the real-time data flows, flexible architecture, and continuous cycle of iteration. As businesses rush to implement conversational AI for better customer engagement, it’s their foundation that will make or break their ability to meaningfully scale.

Ensure Real-Time Data Pipelines for Split-Second Decisions

When AI is working as intended its output is instantaneous, accurate, and specific to the end user. But the ease of these exchanges belie the complex data architecture underneath.

With every incoming query an AI agent is pulling data from several sources: your CRM, data warehouse, customer profiles, social media feeds. It needs to simultaneously analyze historical data (e.g., previous customer support logs or purchase histories) alongside incoming text or audio to formulate the best response.

Then there’s the complicated combination of structured and unstructured data, with voice being the perfect example. Phone calls are synchronous, requiring a quick back-and-forth. Ever been on a call when you mistook a long pause for being disconnected? It’s not the hallmark of a great experience.

Since audio is unstructured data, during a phone call an AI agent needs to:

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1. Convert audio to text (Speech-to-Text), taking into account different accents or speech patterns.
2. Stream the text to the AI application for analysis.
3. Convert responses from text to audio (Text-to-Speech) with natural-sounding speech.
4. Do this all in a matter of milliseconds.

Real-time data pipelines are essential for these low-latency responses. They also make it possible for an AI agent to simultaneously query and enrich a person’s profile during a conversation, paving the way for preternaturally good personalization. For example, if an AI agent is helping someone rebook their flight, it can simultaneously access their frequent flyer status and secure an upgrade.

Future-Proof With Flexible, Composable Architectures

As organizations focus on choosing between large language models (LLMs) and debating build-vs-buy decisions, they're often overlooking another crucial component: how to actually weave conversational AI across the entire business ecosystem.

Even the most sophisticated language models are limited when trapped inside technical silos, keeping AI agents stuck in a loop of static, shallow responses.

We know that the accuracy of conversational AI depends on its real-time access to data from multiple sources. It also depends on a seamless integration with different channels and workflow systems (e.g., an ERP or CRM). This is how an AI agent is able to execute tasks, like filing a support ticket or even booking a doctor’s appointment at a user’s request.

The fact that tech stacks are constantly evolving complicates things. Organizations often find themselves navigating a complex mix of legacy and cloud-based systems, or struggling to maintain custom integrations.

“Future-proof” has become a favorite buzzword in the face of this, but chasing the newest LLM isn’t the answer to staying ahead. There are new, more advanced models coming out every minute. Rather, future-proofing is an exercise in adaptability.

To scale conversational AI, focus on building a flexible, composable architecture and leveraging standardized APIs and webhooks. This way you can switch out and upgrade components with ease without disrupting AI's access to data sources and systems.

Audit, Analyze, Repeat 

What happens when an AI agent gets it wrong? The answer can range from customer frustration to legal liability in the worst-case scenarios.

While AI has made huge advances when it comes to understanding and mimicking natural speech, different channels have different cadences and styles of communicating. Everything from punctuation to verbal acknowledgments like “got it” can influence how natural the AI sounds in each medium.

For conversational AI to advance quickly, every interaction needs to be treated as a learning opportunity.

Let’s use the example of an AI agent misinterpreting a query. This should trigger a chain of intelligent responses: the error’s flagged, the conversation is handed off to a human agent (if necessary), and the tone and speech patterns are analyzed to fine-tune future responses.

This tight cycle of audits and analytics is what will accelerate your AI’s learning. Eventually, the AI should advance to the point of anticipating and preventing misunderstandings before they occur based on the subtlest of conversational cues.

Conclusion

As conversational AI becomes more pervasive, organizations face two challenges: to adopt these systems quickly and to ensure their AI stands out from the competition. As always, the limitations of what AI can do are tied to the limitations of its underlying infrastructure.

Real-time data orchestration, seamless integrations, and continuous learning loops are fundamental pieces in scaling AI’s intelligence and capabilities. The biggest constraint companies will come up against is the foundation they failed to build.

Andy O’Dower is a vice president of product at Twilio, leading the product team for Twilio Voice and Twilio Video products, helping customers build the next generation of customer engagement.