The Limitations of Generative AI for B2B and the Importance of Structuring Data for Enterprise-Ready AI

Generative AI has become increasingly popular in recent years, but while it continues to dazzle us with its creativity, it often falls short when it comes to meeting B2B requirements. On its own, AI – including generative AI – is not built to deliver accurate, context-specific information oriented to a particular task. However, when properly structured and integrated into a context-oriented, outcome-driven system, generative AI has the potential to deliver real value for B2B enterprises.

How Generative AI Falls Short When It Comes to B2B Requirements

Generative AI typically has the ability to generate highly creative and engaging content but often lacks the necessary context to deliver accurate information for B2B requirements. For instance, generative AI may generate an impressive piece of writing on a particular topic, but it may not be relevant or specific enough to be useful in a B2B context. Additionally, generative AI may generate inaccurate or misleading information if not properly trained or structured.

The Importance of Structuring Data for Enterprise-Ready Generative AI

The key to enterprise-ready generative AI lies in rigorously structuring data to provide proper context. By structuring data in a way that is relevant to B2B requirements, generative AI can be trained to generate accurate and context-specific information. This can be achieved by creating structured datasets that are customized to a particular B2B use case.

The Need for a Balance Between Machine Automation and Human Checkpoints

A well-choreographed balance between polished language models (LLMs), actionable automation, and select human checkpoints forms a strong anti-hallucination framework that allows generative AI to deliver correct results, creating real B2B enterprise value. While automation is a key part of this, human oversight is still crucial to verify model output accuracy and provide feedback if necessary.

The integration of generative AI into a context-oriented, outcome-driven system

Generative AI has an impressive ability to produce beautiful writing, which is most useful when integrated into a context-oriented, outcome-driven system. By incorporating generative AI into such a system, it can be used to produce specific outcomes based on the context of the task. This can be achieved by creating structured datasets customized for the particular B2B use case and integrating automated processes that take into account the desired outcome.

The Use of Technology to Provide Structured Facts and Context

By utilizing various technology tools, any company can provide structured facts and context required to enable LLMs to perform at their best. Technology tools include data analytics, natural language processing (NLP) technologies, and machine learning (ML) algorithms. Employing these tools to structured datasets, generative AI can be trained to generate highly accurate results that are specific to the target dataset.

The Continued Importance of Human Oversight

While automation plays a key role in an enterprise-ready generative AI system, human oversight remains critical to ensuring accuracy. Humans are still necessary to verify the accuracy of model output, provide model feedback, and correct results if necessary. Without this human oversight, generative AI may generate inaccurate or misleading information, potentially harming the B2B enterprise value proposition.

Companies are working to bring clarity to generative AI models

There are now companies working to bring clarity to generative AI models by creating standardized measurements of efficacy. These measurements can help enterprises evaluate the performance of different generative AI models and select the ones that are best suited to their particular requirements. By using standardized efficacy measurements, enterprises can have more confidence in the accuracy and reliability of generative AI systems.

Standardizing Efficacy Measurements and Their Downstream Enterprise Benefits

Standardizing efficacy measurements can have downstream enterprise benefits such as improved productivity, reduced costs, and enhanced ROI. By selecting the best-performing generative AI models through standardized efficacy measurements, enterprises can optimize their use of these systems and achieve better outcomes for their investments.

In conclusion, the limitations of generative AI for B2B requirements can be overcome by rigorously structuring data to provide proper context. By creating structured datasets that are customized to the particular B2B use case and integrating generative AI into a context-oriented, outcome-driven system, enterprises can achieve real B2B enterprise value. While automation plays a key role, human oversight remains critical to ensuring accuracy and reliability. With the help of standardized efficacy measurements, companies can evaluate different generative AI models and select the ones that are best suited to their enterprise requirements.

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