Enterprise Generative Ai Key Challenges And How To Solve Them

Enterprise Generative AI: Key Challenges and How to Solve Them | AI21

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Large Language Models (LLMs) are revolutionizing enterprise operations. Or are they?

While headlines have spent the last year hollering about potential cost savings, efficiency improvements, and customer support, the reality has been much less exciting : countless projects have spun up, only to fizzle out in the face of severe stumbling blocks.

This article delves into the complexities of enterprise LLM adoption that have bogged down LLM deployments. Even better is an exploration of how mature approaches to LLM adoption address these challenges – and achieve the operational efficiency once promised – through a task-clad approach.

What is a Large Language Model, Anyway?

While AI has lit the first half of the 2020s ablaze, it’s easy to forget that machine learning has existed since the 1940s. Traditionally, machine learning models were essentially algorithms: given an input and an expected output, the path from A to B is built and updated by the model. Updating its own approach, the machine figures out a way to achieve its goals through trial and error. Chief of this are hyperparameters – parameters that cannot be learned, and need to be tuned by the user.

This process was supercharged by the development of deep learning. In order to add AI complexity without too much cost, deep learning breaks down an infinitely complex subject – such as language – into its relevant layers of complexity. Envision a pyramid – at the bottom, the basic letters of the alphabet, followed by the common assembly of these letters into words, followed by the grammatical laws that dictate which word goes where. Enter the final catalyst for today’s AI boom: transformer models. To best describe this, think of map coordinates where latitude and longitude are both referenced (Paris, for example, can be found at 48.864716, 2.349014). LLM transformer models essentially do this with words – but instead of 2 dimensions, they include thousands. This way, all words in the English language can be mapped in relation to one another.

Transformer architecture is how LLMs deal with homonyms, or words that have more than one meaning. The same word can be represented with different vectors, depending on the context. There’s one vector for bank (as in, the building) and another for bank (of a river), for instance.

Trained on vast datasets, the statistical engines that power these AI models are now able to take input data (that is, an end-user’s query), and generate streams of relevant sentences in response. Thus, the conflagration of interest in how these can be applied within an organization.

Why Use LLMs in Your Enterprise?

Given the sheer flexibility of LLM use cases, their enterprise potential is defined by where they’re needed most. See our rundown for a comprehensive list of enterprise AI use cases.

In customer service, time matters – customers expect rapid responses to support issues and purchase inquiries. However, automated replies can frustrate rather than help – when falling short of the one-on-one support required, customers can often find themselves in conversational loops with automated chatbots misidentifying the issue and presenting them with useless results. Cue unhappy customers, missed sales, and bad reviews. LLMs promise an approach to automation that actually acknowledges and responds to a customer’s input, streamlining your support staff’s workflow.

Customer-facing applications are only one of many; looking internally can identify some even heftier time savings. IBM is one company already realizing these, having deployed an AskHR app to its quarter of a million employees. This rapidly answers employees’ questions on HR matters, saving hours of employees trawling through obscure documentation or hassling HR itself.

While LLMs are starting to achieve some momentum, let’s discuss the elephant in the algorithm: LLM models are plagued by a few stubborn issues that have hamstrung many early adoption attempts.

The Key Challenges in Deploying Enterprise LLM

Concerns around enterprise LLM adoption are numerous, and many are worth delving into. Surface level concerns revolve around the application and return on investment offered by these resource-intensive models: after all, while LLMs can perform many tasks, not all of them have business value. Deeper concerns, however, sit at the very heart of how LLMs handle and generate language.

The Headache of Hallucinations

Traditional software relies on unambiguous data. Ask a computer to multiply 2 by 3, and it encounters no issues at all. Natural language, on the other hand, is full to the brim with gray areas. Humans resolve this by looking at the context around each word – and even then, we’re not always successful.

To dig a bit deeper into how LLM models do and don’t handle this, let’s return to the idea of words being transformed into vectors. Remember how the word ‘bank’ was given 2 different codes, depending on whether it was the building or the river feature? AI models must also handle the opposite; identifying different terms that refer to the same underlying thing. This process is called normalization. ‘Joe Biden’, ‘President Biden’, and ‘Biden’ are all commonly interchangeable in day-to-day conversations, but an LLM is left struggling. There are no deterministic rules for knowing all of these refer to one person – rather, it requires pre-existing knowledge of the world outside.

In order to tackle this, a number of foundational models have chosen to add another layer of understanding, that requires name variants to be grouped under one identity code. Google’s Enterprise Knowledge Graph takes this approach, mapping “Joe Biden”, “Biden” and “President Biden” to a common identity code of “/m/012gx2”.

The problem with this approach is that it requires a continually updated knowledge base of information about the world’s entities. Thanks to the incredibly wide focal lens of today’s publicly available LLMs, their underlying knowledge bases are required to be just as massive. Not only does this make LLM deployments incredibly time- and resource-intensive, but further leaves them fairly vulnerable. Consider the fact that OpenAI’s GPT-3 relies on word vectors with 12,288...