Enterprise Adoption of AI Chatbots: Dystopia or Utopia?

Introdution

AI chatbots are dominating the headlines, with several players vying for prominence in an exploding market. As with any rapidly burgeoning technology, directors of large enterprises are deliberating how to incorporate AI chatbots into their infrastructure. Neglecting to leverage the enormous potential of AI poses substantial risks of lagging behind the wider industry and losing competitive advantage. On the other hand, the adoption of an unreliable or flawed solution can wreak havoc on a well-established enterprise. This dilemma necessitates identifying the major potential causes of lapses in AI chatbot functionality.

Prominent examples of dysfunctional chatbot behavior

One newsworthy example of a lapse in chatbot output accuracy occurred when an inaccurate response by Google’s Bard chatbot during a demonstration caused Google’s parent company, Alphabet, to lose $100 billion in market value.

OpenAI’s ChatGPT, which has been generating considerable widespread interest, has also experienced a major decline in accuracy, according to published reports.

Another newsworthy example is a court holding an airline liable for inaccurate guidance provided by its AI chatbot to a traveler, which the airline then had to honor.

These incidents raise questions about the limitations of advanced chatbots, especially when they falter on seemingly easy challenges.

Under the hood

How do AI chatbots work? What are the factors that determine whether a chatbot can be relied on to perform as expected?

Let’s first get acquainted with the computing capabilities that drive the power of AI chatbots: Machine learning (ML) and natural language processing (NLP). These concepts often appear cryptic to most individuals and can benefit from a concise description in layperson’s terms.

What is machine learning (ML)?

Machine learning is the basic ability of a computer to independently figure out new ways of processing information beyond that which it has been strictly programmed to perform. This ability can be applied to many domains, such as chatbots or image recognition.Machine learning is the basic ability of a computer to independently figure out new ways of processing information beyond that which it has been strictly programmed to perform. This ability can be applied to many domains, such as chatbots or image recognition.

What is natural language processing (NLP)?

Natural language processing is the ability to apply ML in a way that enables a computer or chatbot to comprehend human language. For example, a chatbot will use NLP to correctly identify the intent of a unique question typed by a chatbot user. The chatbot can also use NLP to provide an answer to the question based on processing and understanding the repositories of knowledge available to the chatbot.

Generative AI vs. traditional AI

Generative AI, or GenAI, is the ability to create new content in response to user requests. In contrast, while still analyzing and comprehending user input, traditional AI will only respond with content specified in advance. The power of GenAI does come with increased risks of inaccurate outcomes, as we explained above. Deciding whether to leverage GenAI in a chatbot implementation required a thorough analysis of the specific use case and potential risks and benefits.

Garbage in, garbage out: Data accuracy and chatbot success

Undoubtedly, any defect in AI implementation can cause a chatbot to produce unreliable results. However, an often-overlooked critical underlying aspect is at the core of determining chatbot viability.

The computing capabilities at the heart of all AI chatbots do not guarantee reliable performance, even at their most robust level. This is because the complex processing performed by a chatbot begins with the data the chatbot is utilizing. If the data is inconsistent, incomplete, or lacking in accuracy, any output that incorporates that data will reflect those deficiencies.

For example, a medical chatbot can be designed to answer questions regarding the proper dosage of a specific medication for a patient with a unique set of conditions. The chatbot leverages ML and NLP to understand the questions posed by the patient and calculate the relevant details for the patient’s circumstances based on recommended dosage data provided by the pharmaceutical vendor.

If the chatbot mentioned above incorporates dosage guideline data, which is not 100% accurate, the responses it provides to the patient may reflect that inaccuracy. This could rise to the level of a life-threatening emergency.

With this context in mind, it becomes self-evident that simply adopting a chatbot offered by a prominent vendor will not ensure protection from the substantial risks posed by inaccurate output. It is, therefore, critical for stakeholders to seek out a chatbot implementation partner with solid prowess in data management. This is the only way to establish confidence that an AI chatbot will evolve an enterprise to the next level, rather than erode its reputation or worse.

The role of master data management (MDM)

Master Data Management (MDM) is the collaborative process of ensuring uniformity and consistency of organizational data across the board. The goal of MDM is to establish a fully reliable single source of truth for all enterprise data. MDM is a cornerstone of achieving data modernization and can be a critical prerequisite for a successful chatbot deployment strategy.

Mastech InfoTrellis stands at the forefront of enterprise MDM expertise. You can get up to speed on the latest trends in MDM through our guided webinar.

Success stories

To learn more about some of our successful chatbot implementations for large enterprises, you can explore these case studies:

Revolutionizing E-commerce with a 50% Deflection Rate with AI-Driven Chatbot Solutions

Automated Order Management and Customer Support Chatbot for Popular Sweepstakes Platform

Conclusion

Data quality is the most often overlooked key factor in ensuring robust chatbot performance. Chatbots are only as good as the data they consume, and any inconsistency in relevant data can have catastrophic consequences for chatbot performance, even with advanced machine learning and NLP capabilities.

There is no shortage of chatbot vendors in today’s marketplace. Mastech stands out in this vast landscape with an unmatched formidable track record of implementing MDM and advanced AI-powered chatbots for enterprises across various industries. If you are weighing your options regarding the implementation of an AI chatbot, please reach out to us for a free analysis of your ideal path forward with our best-in-class chatbot architects.