The 85% Problem: The AI gap your team can’t bridge

Think of it like this: you can spend months building an in-house AI solution and likely achieve 85% functionality. But that final 15% — the part that separates a decent chatbot from a truly exceptional customer experience — can become a resource black hole, consuming countless hours and draining your budget.

Your customers expect accurate, reliable information, delivered instantly. So what if your AI confidently provides incorrect information or generates nonsensical responses? It’s a surefire way to damage your brand reputation and send customers fleeing to your competitors.

With Crescendo, you gain a trusted partner dedicated to your success. Our white-glove service ensures your AI solution is tailored to your specific business needs and goals. We provide valuable business insights, including automated quality assurance, comprehensive CSAT coverage, and performance KPIs, empowering you to make data-driven decisions and continuously improve your customer experience.

When developing a CX AI solution, one of the biggest challenges is the unpredictable nature of LLMs. Their responses can be unexpected, requiring constant monitoring and fine-tuning to ensure accuracy and prevent misleading hallucinations. That’s why building a robust CX AI solution requires a dedicated team of skilled engineers with expertise in natural language processing, data science, and software development. 

This translates to high upfront costs and a long-term commitment to maintenance and optimization. As your business evolves, so must your AI. New products, messaging updates, or acquisitions mean retraining your AI model and doing meticulous monitoring to avoid outdated information. This process can quickly become a burden, diverting valuable time and budget away from core business objectives.

But let’s say you want to build it anyway — what steps would you take?

The model maze
First, you have to select the right LLM for your CX needs, and they’re changing all the time. Each model has its own strengths and weaknesses. Achieving optimal performance requires significant computational resources and expertise in hyperparameter tuning.

A deluge of data
The best LLMs won’t perform unless you’ve optimized your data. Unlike legacy AI technology of the past, LLMs don’t get better with more data. You’ll want to focus on quality over quantity, so you must focus your project to collect and clean your knowledge content.  Plus, you have to ensure that it accurately reflects your unique brand and nuances of your industry. It’s a serious challenge to curate and prepare a dataset that encompasses the diverse language, tone, and intent of your customer base.

Integrations get messy
Then, integrating your AI solution with existing CX infrastructure, such as CRM systems, live chat platforms, and knowledge bases can invite even more complexity. Ensuring smooth communication and data flow between these disparate systems demands meticulous planning and technical expertise.

Advantages of Augmented AI
The future of CX is Augmented AI, which brings together expert professionals with generative AI. With this method, interactions begin with AI agents, which can seamlessly hand off to CX professionals. The advantage is that you can get started with a discrete set of data that supports a more narrow use case. Then you can use people to cover the remaining contacts. This flexibility gives you permission to only use the highest quality data — and grow that data set over time.

Imagine having customer support that speaks 50+ languages, available 24/7 to resolve inquiries  faster than your best agents, no matter how complex the product or situation. That’s Crescendo. Our platform blends human expertise with advanced AI to deliver an  exceptional omnichannel customer experience, whether through voice, chat, or email. Crescendo ensures fast, accurate, and empathetic resolutions, optimizing operations  while maintaining the personal touch and product quality your customers have come to expect. Choose the path to 100%. Choose Crescendo.

* Note the original version of the article was corrected on November 19th to update the statement on LLMs and data.