Build vs. Buy: Leveraging Generative AI for Conversation Intelligence

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�s hard to be a citizen of the internet without running into another article hyping the promise of generative artificial intelligence (AI) to revolutionize a certain field. Truth be told, AI has been used for decades in customer service applications. However, the promise of a new chat interface and large language models (LLMs) under the hood have renewed the industry’s fervor and hype around AI. As a result of generative AI’s consumer-friendly chat interfaces, many teams think that they can build their own in-house conversation intelligence solutions using publicly available LLMs. At the same time, according to the 2023 CallMiner CX Landscape Report, 45% of companies acknowledge the compliance and security risks that come along with adopting AI technology. Not to mention, generative AI solutions have been known to hallucinate, or make up facts, and generate biased outputs. Further, 44% of teams admit that they’re unsure what type of AI tech will best meet business needs when collecting and analyzing CX data. Let’s look at some of the most compelling reasons to invest in a proven, enterprise-ready conversation intelligence solution that leverages multiple types of AI models, rather than trying your hand at building your own generative AI solution.

1. The Ability to Scale and Augment Human Work

Conversation intelligence is an area of AI that’s already at work at hundreds of thousands of organizations. According to Forrester, “[Conversation intelligence] platforms…play a crucial role in extracting insights from multiple conversational touchpoints across platforms, including sources outside the contact centre such as surveys and reviews.” The goal of this technology is to scale and enhance human capabilities, rather than replace them — which is highly critical when it comes to creating the most memorable customer experience. These capabilities won’t necessarily be available for organizations looking to implement generative AI alone to replace certain contact centre workflows. In other words, certain AI models must be applied to certain use cases. Here’s one practical example. For decades, contact centres have been recording calls for quality assurance (QA) purposes. Without conversation intelligence, humans had to review random calls to ensure compliance with processes, and help agents continuously improve. The problem is, by many industry estimates, only 1-3% of total calls can be reviewed in this manner, missing a vast majority of agent interactions with customers. Conversation intelligence helps to scale manual QA processes by analyzing 100% of omnichannel customer feedback. At this scale, QA teams can detect trends and target more effective, data-driven coaching sessions for their agents. And while AI can never truly replace things like human empathy, conversation intelligence systems can help agents connect on a deeper level with customers’ sentiment and emotions — identifying important signals such as customer vulnerability or other types of dissatisfaction.

2. The Time and Resource Intensity of DIY

Many teams underestimate the scope of work that comes along with building your own conversation intelligence solution based on generative AI models. For example, the development effort is significant if you want to build a comprehensive, scalable conversation intelligence system that involves continuous mining workflows, complex data processing, real-time analysis, and integration with other systems. Maintaining this software over time is another important consideration. A do-it-yourself (DIY) effort will require dedicated resources to continually evolve your infrastructure, apply new or updated security measures and governance, regularly monitor response quality, expand ecosystem connections, and more. Moreover, as data volumes grow, scalability issues may lead to processing bottlenecks. In other words, having the resources to build your own conversation intelligence solution isn’t enough. You also have to maintain and improve it over time. These costs can add up significantly, especially if engineering and data science resources are needed elsewhere in the business.

3. The Capability to Apply AI to Business Use Cases

If you do choose to create a homegrown solution, one important consideration is the usability of this system. Will various stakeholders throughout the business understand how to use the technology on a day-to-day basis for their jobs? The trouble is, without certain workflows, even the most thoughtful homegrown solution won’t be optimized for specific use cases. You may not even know if the right AI model is being applied to the right use case. To contrast, conversation intelligence providers leverage the latest generative AI models, LLMs, and other types of purpose-built AI models to analyze customer interactions, composing the optimal solutions for specific use cases, such as:

• Contact centre improvements, including contact centre efficiency, the frontline agent experience, compliance and risk outcomes, quality assurance (QA), fraud detection and sales effectiveness.
• Experience management improvements, including CX and customer journey analysis, product intelligence, and brand experience management for marketing teams.
• Vertical use cases taking into account the nuances of specific industries, such as healthcare, retail, financial services, technology, collections, BPO, and beyond.

Your conversation intelligence solution should leverage AI to make it easy for your team to transcribe audio, redact information, summarize and classify customer interactions, provide reporting, and close the loop on important next steps, so you can take action on customer insights. For now, tried-and-true approaches to AI can augment previously manual workflows — ensuring that customer support departments can retain the human element in a tested environment. Rather than risking valuable customer interactions on a homegrown generative AI application, look to apply AI under tight control. Leveraging a trusted conversation intelligence provider ensures that the right AI models can be used responsibly at the right time, with the goals of improving outcomes for both agents and customers alike.