Large Language Models and Enterprise Software Definition

Introduction

Large Language Models (LLMs) are revolutionizing how organizations understand, interpret, and implement enterprise requirements across industries. As artificial intelligence technologies mature, they offer unprecedented capabilities for analyzing complex business needs and translating them into actionable specifications. This research explores the transformative potential of LLMs in enterprise requirement interpretation and examines their integration across various aspects of enterprise systems.

Understanding Enterprise Systems and Requirements

The Evolving Landscape of Enterprise Systems

Enterprise systems form the backbone of modern organizational operations, integrating and coordinating business processes on robust technological foundations. An Enterprise System is any information system that improves enterprise business functions through integration, typically offering high-quality service and handling large volumes of data. These systems must be accessible across all organizational levels and capable of processing information at relatively high speeds.

Business Enterprise Software, also known as enterprise application software, is specifically designed to satisfy organizational needs rather than individual user requirements. This software handles numerous business operations, from enhancing management reporting to supporting production operations. The enterprise software industry has grown significantly, though its evolution is not well documented in academic literature.

Enterprise Business Architecture Framework

Enterprise Business Architecture provides a comprehensive framework connecting a company’s strategic, structural, informational, technological, and operational elements. This architecture helps align current and future business operations with entrepreneurial goals by integrating IT, digital business processes, and security. The primary purpose of enterprise business architecture is to capture essential business aspects in actionable elements that support organizational objectives.

Enterprise Resource Planning Systems

Enterprise Resource Planning (ERP) systems represent a critical component of modern business infrastructure. These systems provide integrated management of main business processes, often in real-time and mediated by software and technology. ERP systems create an integrated view of core business processes using common databases and track resources while managing business commitments across departments. The global ERP market size was estimated at $35 billion in 2021, with increasing adoption among smaller enterprises.

LLMs for Enterprise Requirement Interpretation

Transforming Requirements Engineering

Large Language Models offer transformative capabilities for requirements engineering tasks. They can revolutionize how organizations handle requirements elicitation, specification, and validation processes. This ability stems from LLMs’ remarkable language understanding and generation capabilities, which can reshape the requirements engineering landscape.

For enterprises working with commercial clients, LLMs represent a strategic asset to build upon, promising a new age of automation and productivity. Their integration into enterprise applications improves natural language processing capabilities, elevates customer experience, increases automation, and enhances decision-making across organizations.

Enterprise LLM Solutions

An Enterprise LLM is a large language model tailored specifically to meet enterprise system needs. Unlike off-the-shelf LLMs, these models can be customized for business contexts, providing more relevant information and adapting to unique organizational environments. Through customization of training data and workflows, enterprise LLMs offer more contextually appropriate information, adapt to specific business scenarios, and support data-driven decision-making at scale.

Enterprise-scale LLMs must meet specific criteria to function effectively within complex organizational environments. They must be scalable to handle increasing workloads, reliable with minimal downtime, secure to protect sensitive data, integrated with existing systems, properly governed, and capable of delivering tangible business value.

Implementation Approaches for Enterprise LLMs

AI Enterprise Integration Strategies

AI Enterprise implementation encompasses the integration of advanced AI-enabled technologies within large organizations to enhance various business functions. This includes routine tasks like data collection and analysis, plus more complex operations such as automation, customer service, and risk management. The application of enterprise AI spans numerous business operations, including supply chain management, finance, marketing, customer service, human resources, and cybersecurity.

When implementing LLMs in enterprise environments, organizations can choose from three approaches: use existing models, customize foundation models, or build custom language models. The selection depends on specific organizational needs, available resources, and strategic objectives.

Open-Source vs. Proprietary Solutions

Open-source LLMs offer several advantages for enterprise implementation compared to proprietary alternatives. These include reduced vendor dependency, code transparency, greater customization and adaptability, access to active development communities, and increased innovation potential. Popular open-source LLMs that enterprises can leverage include Llama 3.3, Mistral, Phi 4, and others.

Despite the benefits of open-source models, proprietary LLMs often provide superior accuracy across various benchmarks and come as part of fully managed services, reducing operational complexity. These enterprise-grade proprietary LLMs offer inherent security and privacy measures integrated from inception, with adaptability for extensive customization to various organizational functions and specific datasets.

Democratizing Enterprise Requirement Analysis

Low-Code Platforms and AI Application Generators

Low-Code Platforms provide development environments for creating application software through graphical user interfaces, reducing traditional coding time. These platforms enable accelerated delivery of business applications by operating at high abstraction levels. When combined with LLM capabilities, low-code platforms can dramatically transform how organizations translate requirements into functioning applications.

AI Application Generators like Jotform’s AI App Generator allow businesses to design customized apps without coding requirements. Users can describe the type of app they want to create, customize it through no-code interfaces, and quickly deploy solutions that address specific enterprise requirements. This approach significantly reduces go-to-market time and enables professionals without coding knowledge to create applications that meet organizational needs.

Empowering Business Technologists and Citizen Developers

Citizen Developers are employees who create application capabilities for themselves or others using tools not actively forbidden by IT or business units. They report to business units rather than IT departments and represent a growing force in enterprise application development. With LLMs, citizen developers can more easily interpret and implement enterprise requirements without deep technical expertise.

Business Technologists represent a broader category of employees who leverage technology for business purposes, though not all are necessarily citizen developers. As LLMs become more accessible and integrated with low-code platforms, business technologists can play increasingly important roles in translating enterprise requirements into functional solutions.

LLMs in Digital Transformation and Technology Transfer

Facilitating Enterprise Digital Transformation

Digital transformation represents the fundamental rewiring of organizational operations, aiming to create value through continuous technology deployment at scale. LLMs serve as powerful enablers of this transformation by improving customer experience and lowering costs. Unlike traditional business transformations that end once new behaviors are achieved, digital transformations represent long-term efforts to rewire how organizations continuously improve and change.

Enterprise software solutions are crucial components of successful technology transfer initiatives. Companies like SII offer enterprise software solutions to support digital transformation, helping organizations adopt new ways of working and empowering employees with appropriate tools. LLMs can accelerate this technology transfer by interpreting complex enterprise requirements and translating them into actionable implementation strategies.

Supporting Different Types of Technologists

Various types of technologists benefit from LLM capabilities in enterprise environments. These include data scientists building custom models, chief data officers exploring LLM potential for organizations, and enterprise architects designing systems that leverage AI capabilities. LLMs help these different technologist profiles better understand and interpret enterprise requirements within their respective domains.

AI Assistance in Enterprise Requirements Management

AI Assistance tools like inFeedo’s AI Assist help HR teams boost employee productivity by providing critical information instantly at scale. The system automatically answers about 85% of repetitive queries and ensures timely resolution for bespoke questions through centralized ticketing. This illustrates how LLMs can be applied to interpret and respond to specific requirements within enterprise functional areas.

Enterprise requirement management also benefits from Software Bill of Materials (SBOM) tracking, which lists all components used to build and run applications. LLMs can help analyze and interpret SBOM data, identifying potential vulnerabilities, ensuring compliance with licensing requirements, and supporting overall application security.

Challenges and Considerations

Despite their promise, implementing LLMs for enterprise requirement interpretation presents several challenges. These include ensuring data privacy and security, maintaining model accuracy and reliability, integrating with existing Enterprise Computing Solutions, and aligning with industry-specific regulatory requirements.

Organizations must carefully evaluate whether to develop proprietary LLMs in-house or leverage external Enterprise Products and Business Software Solutions. This “build or buy” decision requires considering factors such as internal expertise, data sensitivity, customization needs, and long-term strategic objectives.

Conclusion

Large Language Models present transformative opportunities for interpreting enterprise requirements across diverse organizational contexts. By leveraging LLMs through various implementation approaches, enterprises can enhance requirements engineering, empower citizen developers, accelerate digital transformation, and improve overall business outcomes.

To maximize LLM potential for enterprise requirement interpretation, organizations should:

1. Define clear strategic objectives aligned with business goals before implementing LLM solutions

2. Assess data readiness, including inventory and compliance considerations

3. Plan appropriate infrastructure for model development and deployment

4. Build internal expertise or partner with external specialists

5. Select and customize models based on specific organizational needs

As LLM technologies continue to evolve, their ability to interpret increasingly complex enterprise requirements will grow, further cementing their role as essential components of modern enterprise computing environments. Organizations that successfully integrate these technologies will gain significant competitive advantages through improved requirement interpretation, faster implementation cycles, and more responsive business systems.