Enterprise Ai Definition Challenges And Solutions

Enterprise GenAI: Definition, Challenges, and Solutions | AI21

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Already one year old, mainstream AI is this decade’s major disruptor: whether it’s the International Monetary Fund predicting that AI will affect nearly 40% of all jobs, or Meta incurring significant controversy by building a fully open-source general intelligence model. While 2024 promises to be the year of AI adoption for those pushing for a competitive edge , the sheer variety of use cases can make general AI difficult to visualize and control – not to mention more prone to error.

This article will outline some of the concerns facing organizations today, and how enterprise AI is evolving to meet them.

How Small-Scale Models Are Defining Enterprise AI

Enterprise AI is the focused application of artificial intelligence to improve efficiency, enhance performance, and drive innovation across teams. In the backdrop of general-purpose AI’s large-scale models, enterprise AI is increasingly pulling ahead in favor of hyper-focused, task-specific models (TSMs).

General-purpose AI, in this context, refers to the mainstream large-scale models that have been dominating headlines since OpenAI’s ChatGPT became the breakout industry leader. Open AIs foundational model works by analyzing each word inputted and sequentially predicting subsequent words – in this way, complete responses are created. Trained on an extensive array of Wikipedia entries, books, and internet resources, these types of LLMs are able to break language down into a series of probability exercises. This technique involves an immense volume of training data, exemplified by Google’s latest AI model using nearly 3.6 trillion tokens.

All this data is necessary for general-purpose AI models as they operate as probability engines, assigning likelihoods to potential responses. However, if the ingested information is biased, incomplete, or problematic, the outputs can be unreliable at best, and offensive at worst. These hallucinations – fabricated data that appear authentic – occur because LLMs lack any internal understanding of the world they’re describing. As the datasets of LLMs increase, so too does their capacity to act in unusual ways. Microsoft’s Bing, powered by GPT-3, not only utilizes the LLM but also integrates search engine results. Within this high-input approach, it’s been found that LLMs can transcend their initial programming and generate content in languages they weren’t explicitly trained on. This emergent behavior, while not fully understood, highlights the inherent unpredictability of large-scale, unrefined AI engines.

The high-volume, spray-and-pray approach of general-purpose AI has become a source of real disappointment. Faced with underwhelming ROI and an unacceptably high risk of inaccuracies, organizations are increasingly applying their approach to more tightly-focused datasets. Enter, enterprise AI.

Instead of a single jack-of-all-trades, enterprise AI offers task-specific models that identify the natural language capability required – for example, context-rich answers – and the team it will be deployed to, such as customer support. This core focus is then supported by further verification mechanisms. By segmenting NLP into its core tasks and relying on internal datasets, teams are able to build their own customized integration of TSMs that provide reliable and grounded results – at a fraction of the cost.

General Purpose AI vs. Enterprise AI: A Direct Comparison

Enterprise AI is increasingly proving itself to be the high-impact solution that regular AI promised to be. To understand why, both approaches deserve a breakdown of scope, deployment, and integration potential.

General-Purpose AI Enterprise AI Goal Models are built to extract and utilize as much information as possible, encompassing research, writing, mathematics, translation, and coding. Models are built to target specific, high-value use cases within a team or project. Data sources and transparency Web scrapers incorporate the entirety of text and images on the internet; other sources include books and articles. Generative AI is increasingly being used to create synthetic data to train other models. Minimal transparency for end-users. Models are trained from internal documents and therefore hyper-focused on relevant context. Complete transparency when inadequate data is provided, rather than guesswork. Implementation within a team/project A team must self-select their own issues, before spending significant time fine-tuning answers with prompt engineering. Recognizes that organizations require more than raw AI engines. Support is offered to identify the highest-ROI use case in your organization. Time to production Inhouse projects must go through a lengthy five-stage process of Problem Scoping, Data Acquisition, Data Exploration, Modeling, and Evaluation. Only afterward can the project start realizing ROI. Pre-tuned solutions allow for rapid integration and testing, skipping the headaches of production and data handling. Executive buy-in is immediately on the table. Security Input data is either handled by a fully in-house model – or, in the case of completely third-party tools, submitted to a model with no transparency. Users have no idea whether sensitive data may reappear as outputs. Data is kept internal to the organization and only accessed on an as-needed basis. Integrations Demands a degree of technical proficiency and understanding of your enterprise’s backend. The risk of infrastructural disruption is high. As a result, the integration process can suffer from significant delays in complex organizational structures. Offers deep integration with existing enterprise systems such as GCP and AWS. Plug-and-play APIs interact seamlessly with other components of a tech stack.

Revealing LLM’s Challenges – and Enterprise AI’s Solutions

Without the further streamlining of enterprise-specific training, foundational AI models are left woefully unprepared for real-world applications. In 2023, the now-infamous Mata v. Avianca legal case took a turn for the unexpected when the claimant’s lawyer – tasked with proving his client’s injury during an Avianca flight – used ChatGPT in his legal research. As a result, the court was presented with some of the following judicial decisions: Varghese v. China...