Unlocking the full power of Generative AI for the Enterprise

The rise of generative AI and large language models (LLMs) has sparked interest in their potential to enhance enterprise decision-making. 

Generative AI, led by powerful language models like GPT, has already revolutionized the analysis of public documents. The next frontier will be the secure integration of natural language proprietary documents within the enterprise. This development will allow for deeper insights into the sensitive, operational data that sits within enterprise data lakes. 

However, there are two significant challenges that need to be addressed before this potential can be realized. Firstly, LLMs lack an understanding of the business dynamics reflected in granular, high-velocity data. Secondly, LLMs generate black-box statements that cannot be validated, which makes it difficult for businesses to make informed decisions.

Developing such a technology is not without its challenges. To overcome the above, a new intermediate layer is needed that can analyze and synthesize data stored in core business operation systems and generate knowledge that can be used as inputs for LLMs. This would include customer relationship management (CRM), enterprise resource planning (ERP), and supply chain management (SCM) systems. 

Once LLMs can process and understand such high-velocity, often-siloed data, their analytics infrastructure can generate insights that are not only more accurate but also more trustworthy. Such an intermediate analytic layer can improve a business’s forecasting accuracy, optimize their operations, and identify new business opportunities.

Integrated solutions that combine generative AI with AI-powered analytics are needed to overcome the challenges of dealing with data overload and unlock the full potential of autonomous business intelligence. 

These technologies will need to seamlessly connect enterprise data sources to generative AI engines and analyze vast amounts of data to identify the most impactful hypotheses for ongoing iteration. 

By integrating data analysis and generative models, familiarizing themselves with the entire connected data schema of the enterprise, and validating statements against operational data, these technologies can empower business decision-makers to make informed, data-driven choices, iteratively, interactively, and independently.

As part of the vision for the future we imagine Autonomous Business Intelligence, an underlying platform that is aware of all aspects of an enterprise, as reflected in its digital footprint, and continuously monitors its KPIs, and how decisions, activities, and external events affect those KPIs. A platform with which business managers interact directly using natural language and business terminology, enabling them to ask questions and get answers, insights, and suggestions, all in the context of their day-to-day tasks and the long-term strategy.

To make this a reality, such a system must integrate data analysis and generative models, be familiar with the inter-connected data schema of the enterprise, and validate its output against the enterprise's operational data.

One of the biggest challenges in realizing this vision is the vast amount of data sources that businesses need to deal with. 

A typical Fortune-1000 enterprise may have hundreds of data sources that are updated daily and contain business data spanning an average of 5-10 years. At least 10-20 of these sources are usually relevant to any given enterprise business KPI. 

An intermediary layer as we describe above must autonomously and continuously look for new emerging patterns, over combinations of data sources, and across all relevant historical time windows. Therefore, a new technology that combines generative AI with AI-powered analytics is required to search through vast amounts of data sources, identify the most impactful hypotheses, and iterate the ideation process on an ongoing basis.

SparkBeyond’s Hypothesis Engine is an example of a technology that has been designed to do just that, and is now uniquely positioned to integrate generative AI in the enterprise space. 

By composing millions of candidate statements (hypothesis) regarding the drivers and factors influencing the multitude of business KPIs, the Hypothesis Engine spans a large space of possible underlying explanations to the business reality. It then generates code for each such statement, and runs the code on the data. 

The results are compared against the observed KPIs, and only those statements (hypotheses) that demonstrate correlation and information gain with respect to the KPIs are kept for further analysis. 

This then produces a live store of validated business insights over time that are explicitly connected to their supporting data for further investigation by data analysts, which ensures transparency and trust in the AI outputs.

The Hypothesis Engine has been used by leading Fortune-500 companies to generate data-validated ideas and uncover novel insights for business use cases such as customer retention, store location optimization, yield optimization, and reduction of fuel consumption. 

The engine has been developed to seamlessly connect enterprise data lakes to an underlying AI infrastructure, which in an ongoing manner, analyzes the factors that are moving the needle on the core KPIs of the enterprise. 

The behavior of the KPIs and the underlying factors is stored into a knowledge repository, which is available for a suite of business applications, empowering business stakeholders to ask questions in their own language and receive answers, insights, and even action recommendations in response.