Why some brands fail to maximize the value of generative AI

If businesses have not yet utilized generative AI (Gen AI) solutions, it’s probably top of mind for 2024. Last year, many companies succeeded with Gen AI, automating processes, minimizing labor costs, and providing dynamic customer support. Unfortunately, plenty of brands – due to unanticipated errors or rushed deployments – did not have positive experiences. There are many reasons why organizations fail to maximize the value of AI, however there are strategies that can be used to ensure a fruitful deployment.

Unrealistic or undefined expectations 

There is an expectation that Gen AI after it gets inserted into a business process, will solve problems on its own. In reality, Gen AI, specifically large language models (LLMs), are not capable of independent reasoning and logic, nor can they serve as sources of authoritative knowledge. To that end, businesses should recognize the limitations of Gen AI and identify realistic use cases that the technology can potentially enhance.

Improving customer experience (CX) in the contact center is an ideal way to maximize the value of Gen AI. First, businesses must strategize accordingly and decide which types of customer service interactions they want to augment with Gen AI. Then, brands can deploy Gen AI-powered customer service tools, such as private knowledge-based question-answering bots and AI-driven user journeys (co-pilot mode). These tools, in particular, will help accelerate resolution times and enrich CX in the contact center.

Poor data quality or management 

Data is vital to any successful Gen AI deployment – nevertheless, random or unstructured data won’t suffice. Indeed, one of the primary ways a Gen AI project fails is because companies collect unnecessary data or have too much unorganized data. (Attri.AI, 2023) Sometimes, a brand’s data is in a difficult-to-process form, meaning that the data is in a format the Gen AI system can’t ingest quickly. (CIO, 2023)  

In addition to ensuring that Gen AI applications use the highest quality data, companies must implement a Retrieval Augmented Generation (RAG) pattern to help their Gen AI interface with enterprise datasets. A vector database or extensions to more conventional data engines can provide the semantically relevant search capability required for RAG-enabled applications. For automated customer service interactions, brands must create a human feedback loop to analyze past interactions and improve the quality of those datasets used for fine-tuning and retrieval augmentation.

Issues with third-party AI tools

There are numerous third-party AI tools at brands’ disposal, from Jasper to Microsoft Bing and Google Brad. In fact, a global report showed that 78% of organizations use third-party AI tools. (MIT Sloan Management Review, 2023) At the same time, the report also discovered that 55% of all AI failures come from these third-party tools. Such tools are not immune to errors – especially if employees don’t have the right training. For instance, should staff insert sensitive customer information into a Gen AI tool without first anonymizing that data, the company risks unintentionally exposing said data, violating various industry-based compliance rules.

As such, business leaders must enforce the best responsible AI programs throughout their organizations. Likewise, companies must evaluate the various third-party AI tools available, noting the vendor’s responsible AI practices (or lack thereof) and observance of regulatory requirements, including PCI and HIPAA. (MIT Management Sloan School, 2023)

Partner with IntelePeer and avoid AI failure 

IntelePeer’s cutting-edge Communications Automation Platform (CAP) utilizes Natural Language Processing (NLP), generative AI, machine learning (ML), and more to elevate your operations across all digital channels.

Contact us today to explore the opportunities that Gen AI can bring to your business.