Improving franchise operations with purpose-built LLMs and AI agents

The franchising industry is an influential, yet often overlooked, part of the global economy that is forecasted to grow to a staggering $5 trillion in value by 2028. This growth will be fueled by many factors, not least of which will be AI’s ability to simplify the complexities of operating franchise networks.

Like so many other industries, franchising is overburdened by a multitude of small, intricate tasks that are governed by unstructured data. These tasks include tracking territories, deadlines, and renewals across hundreds of locations, ensuring fees and rebates follow different versions of agreements, and more. Completing this ever-growing list of requirements demands a high degree of precision, making franchise operations an ideal candidate for ‍ solutions and automation. Using AI effectively for franchises, however, requires purpose-built LLMs and AI agents developed by a cross-functional team that prioritizes technical flexibility, system security, and cost management.

Tackling Unstructured Data with LLMs

Unleashing the power of AI for franchises starts by corralling the large volumes of unstructured data trapped within legal documents, local laws, and financial reports. Large Language Models (LLMs) are an excellent tool for accurately extracting, analyzing, and organizing critical business data from these sources. However, LLMs need to be thoughtfully designed for the franchise industry before being deployed, with an eye on three main factors:

1. Security: Privacy must be at the forefront of every LLM architectural decision and all systems should be deployed within an isolated private cloud environment. Without dedicated, purpose-built AI systems, franchises risk employees uploading sensitive data for one-off tasks to unapproved, unsecure AI systems such as ChatGPT or Claude. Providing properly built, domain-specific AI technology is essential to improving efficiency without compromising franchises’ confidential data and intellectual property.
2. Schema Design: Building franchise-specific solutions to guide LLMs demands deep domain expertise. For example, mapping out complex domain-specific relationships between franchise documents and extracting relevant deadlines, obligations, and conditions requires techniques like graph RAG (Retrieval-Augmented Generation) and the use of libraries like Pydantic. This capability is one of AI’s greatest powers, unlocking downstream processes that were previously out of reach.
3. Repeatability: Achieving consistent results for franchises as diverse as quick service restaurants and real estate agencies requires large training datasets and a product-minded development team with deep domain knowledge. One way to achieve this is by maintaining extensive synthetic data that mimics real business datasets and investing heavily in building comprehensive testing libraries that continuously simulate different business scenarios to ensure high-performance.

Once the unstructured data surrounding a franchise has been unpacked and organized, AI systems can be built and deployed for advanced reporting and process automation, saving franchises countless hours of manual labor and contributing to strong profit margins.

Scaling with AI Agents

Operating a franchise is incredibly complex and governed by countless requirements and tasks. For years, many attempts to deploy traditional software solutions were made, but none offered the flexibility needed to fully automate these workflows. The intricacies of franchise operations, however, are perfect for a flexible suite of AI agents.

Consider territory allocation in the franchising industry — an important concept that prevents self-competition by defining which region every franchise owner can operate within. While mapping software exists to track assigned territories, the true source of information lies in the contracts signed with each franchise owner. The manual process of inputting this data often leads to discrepancies between legally contracted territories and what the mapping software displays, causing significant conflicts when these get out of sync.

AI-powered solutions, on the other hand, can monitor territory allocation with ease. By deploying LLMs on franchises’ contracts, AI identifies which regions each franchise owner has been assigned. When new contracts are signed, AI agents immediately check for conflicts with existing territories, ensuring perfect synchronization without any manual oversight.

When building and scaling these agentic workflows, three considerations should be kept in mind:

1. Engineering Frameworks: While AI is a newer and rapidly evolving technology, core engineering principles are still essential. For example, starting by building a proprietary framework can make continuously integrating new technologies as they emerge much easier and faster.
2. Production-readiness: Treat every environment being built like its a production environment. This design principle ensures that all systems can handle hundreds of thousands of executions per day, which franchise businesses require.
3. Monitoring & Debugging: Maintaining integrity as systems scale requires the continuous use of monitoring and debugging tools like Langfuse and Langsmith. Without this around-the-clock commitment, solutions can quickly break, resulting in countless hours of reactive repairs.

Overcoming Common Technical Challenges

Building scaled AI solutions for any industry is challenging, and doing so for franchises is no exception. These systems must be designed for easy updates and robust testing, costs must be carefully managed to avoid ballooning, and cross-functional teams must work collaboratively together to design solutions purpose-built for franchises. Overcoming these challenges is achievable by following some best practices:

• Evaluability: The AI landscape is changing daily. It’s critical that systems are designed to be easily updated from the very beginning, all while maintaining or improving quality. Automated testing and evaluation are key, as LLMs are non-deterministic and single points of failure can appear unexpectedly.
• Open-Source Solutions: Staying at the forefront of innovation requires active participation with the open-source ecosystem, as open-source solutions are often more advanced than current in-market offerings. By developing proprietary solutions on top of open-source foundations and being an active steward of these underlying frameworks, engineering teams can create highly specialized tools tailored to franchises’ specific needs.
• Cost Management: The underlying costs of LLM-based systems can balloon quickly due to multiple calls, re-evaluations, and extensive testing. Careful optimization is necessary to keep costs under control.
• Product Flexibility: Building AI solutions requires a careful balance of adapting best practices from traditional software development, incorporating cutting-edge AI technologies, and designing custom solutions that are maintainable and adaptive.
• Cross-Functional Expertise: Creating domain-specific AI solutions requires a lean team with a unique blend of skills and expertise. Teams should be composed of business experts who are tech-savvy and technical experts who are business-savvy, enabling the collaboration needed to succeed in this fast-paced environment.

Unleashing the Power of AI for Franchises

The application of LLMs and AI agents to solve industry-specific problems at scale represents a new frontier in business operations. For franchise brands, this technology offers unprecedented opportunities to streamline complex processes, ensure accuracy, and unlock new insights from unstructured data.

For businesses looking to explore AI solutions, the key is to start with a deep understanding of their needs, invest in a cross-functional team, and be prepared to iterate rapidly. The rewards of successfully implementing these systems — increased efficiency, accuracy, and insights — are well worth the investment.

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This article originally appeared on AiThority here.