The promise of generative AI is vast, offering potential breakthroughs in customer experience through the power of large language models (LLMs). McKinsey estimates that applying it to customer care could increase productivity by 30 to 45 percent. Further, the latest research from CMSWire suggests that organizations who say that they understand their customers well are much more likely to use AI extensively (33%) compared to those who understand their customers either moderately (7%) or poorly (3%).
However, to translate this potential into real business value, it’s important to apply best practices to avoid common pitfalls. While many oversimplify the technology’s capabilities, the opposite is also true: a “generative AI can do it all” mindset fails to address the complexities of real-world CX challenges.
Integrating prompts into solutions without a strategic framework for delivering actionable insights isn’t enough. A comprehensive approach is essential to harness generative AI’s full potential. This includes selecting the best-fit LLMs, using guided prompt building and layering different types of purpose-built AI for CX within a unified platform.
The following best practices will help you maximize the effectiveness, accuracy and value of your investments while creating new opportunities for innovation at the same time.
1. Get a Unified Platform
Disjointed systems hinder organizations’ ability to unify customer communications, preventing them from fully leveraging emerging technologies. To best harness generative AI, it’s imperative to build a near-term plan for unification. Having all interactions in a single unified CCaaS platform vastly enhances your ability to derive value from evolving technologies.
2. Don’t Over-rely on LLMs
With generative AI adoption happening at such a rapid pace, companies can over-rely on LLMs and incur costs associated with prompt engineering trial and error. Running recursive and token-heavy prompts on every interaction for quality evaluations is expensive, slow and often unnecessary.
A more efficient approach is to use narrow-purpose models (such as AI models that score customer sentiment or agent behaviors) to score every interaction. Then, use those scores to narrow down a subset for focused generative AI use.
3. Choose the Right LLM
No single LLM fits every use case. Start by identifying your optimization goals: Does your organization need to deliver greater accuracy? Speed to resolution? Cost savings? Each goal requires a different LLM to deliver value.
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Say, for example, you only need call summaries or transcriptions. A low-cost, low-powered LLM can handle those tasks easily. On the other hand, if you need to capture regulatory data points, then a higher-powered LLM will be necessary.
4. Use Models Specific To Your Industry
One of the downsides to generalized AI models is they lack the depth of knowledge needed for your particular use cases. Industry-specific models deliver far more accurate and relevant insights for your needs, unlike general-purpose generative AI. Using industry-specific models can also reduce AI hallucinations and mitigate the possibility of your generative AI delivering a wrong answer.
5. Develop Excellent Prompt Engineering Guidance
When non-prompt engineers handle prompt engineering, it can lead to costly, time-consuming trial and error processes that expose your organization to risk. These include faulty generative AI insights, compliance failures, bias and hallucinations/rogue LLMs. Guided prompt building mitigates these challenges and delivers greater business value.
6. Act On Your Results
When implementing generative AI technology, avoid thinking about it in isolation. Even narrow-purpose models can have compounding value through other business units if you have AI-infused tools and applications within a unified platform to maximize their outputs.
For example, a customer sentiment AI model to evaluate interactions and agents serves a critical purpose. It can also collaborate with other AI models (such as intent recognition, sales effectiveness) and LLM-powered knowledge retrieval to create a highly effective agent copilot.
Make AI Work For You With NICE CXone
Integrating generative AI into CX processes can transform customer experiences and business outcomes, but only if they’re thoughtfully deployed. As the only CX AI platform, NICE CXone manages and optimizes all modern interactions, from voice to digital messaging to chatbots and social. CXone provides everything brands need to deliver extraordinary AI-driven experiences at scale, including best-of-breed products and capabilities with AI purpose-built for CX on a single platform to orchestrate every interaction.
Read the full guide Closing the Gap Between Generative AI’s Promise and Reality at nice.com.
