Demigod vs. Specialists: How Custom AI Models Are Reshaping the Age-Old Debate on Generalization vs. Specialization

To get a comprehensive grasp of what is happening in the ever-evolving world of artificial intelligence (AI), let's first take a journey through the evolution of human intelligence. In this article, we'll dive into some common questions, like:

• How do you efficiently train AI Models?
• What does the future of AI-centric products look like?
• Can we use ChatGPT/Gemini for all our workflows?
• Where does AI fit in for retail Order Management Systems (OMS)?
  
  

And pose the age-old question, “Why can’t we have just one solution for everything we need?”

A Journey Through Human Knowledge

The Polymaths of History
Early human knowledge was centralized. History’s polymaths, like Leonardo da Vinci and Michelangelo, weren’t just artists; they were also engineers, anatomists, and philosophers. These individuals, proficient in diverse fields, contributed to multiple disciplines and, in some cases, established entirely new areas of study. Leonardo's "Vitruvian Man," a fusion of art, mathematics, and anatomy, demonstrates this beautifully.

This centralized model of broad foundational learning is mirrored in our educational system today, where children learn all the general subjects through elementary and middle school. This is very important and downright necessary to establish a baseline knowledge, which we modernly refer to as, “Common Sense.”

The Saturation Point
As human knowledge grew; it became impossible for one person to master every field. With the saturation of the human mind, further efforts to learn more only generated negligible, diminished returns. This led to a shift in specialization, bringing a rise in distinct professions like scientists, doctors, engineers, and more. The Industrial Revolution, with its complex machinery and advanced chemistry, is a clear example of this trend, demanding the expertise of specialists like mechanical engineers and chemists.

From Monolithic to Specialization in Technology

Monolithic Systems
Early software systems were built as monolithic architectures, meaning that all components and functionalities were tightly integrated into a single, unified codebase. These systems handled everything from order processing and inventory management to user authentication and reporting within the same structure. For simpler systems having few components, this was preferable due to reduced networking requirements. Once more and more functionalities (such as order orchestration, inventory management, and fulfillment) started being integrated, it became one bloated system. 

Legacy Order Management Systems (OMS), such as AS400 or Oracle OMS, initially relied on monolithic architectures. These came with characteristics like:

• Scalability Issues: Scaling required provisioning more resources for the entire system, even when only one component (e.g., order processing) needed more capacity.
• Rigid Updates: Updates or bug fixes in one module often required testing and redeploying the entire application.
• Downtime: A failure in one component (e.g., inventory management) could impact the entire system.
• Customization Challenges: Adapting the system to unique client needs was difficult due to the tightly integrated architecture.

The Microservices Approach

A modern OMS is designed with a modular, microservices architecture, leveraging modern Cloud technologies. Each core functionality (e.g., order orchestration, inventory visibility, fulfillment, etc.) is built as an independent microservice and can be deployed independently of each other while also having the capability to be integrated when required. This led to characteristics like:

• Improved Scalability: Services can scale independently based on demand. For instance, during peak sales, the order orchestration service can scale without affecting inventory or other services.
• Rapid Deployment: Individual services can be updated, tested, and deployed without impacting the rest of the system.
• Resilience: A failure in one microservice doesn't bring down the entire application. Other services continue to operate seamlessly.
• Customizability: Each service can be tailored to specific client requirements, offering flexibility for diverse business needs.

Struggling with how to transform your OMS application, which is responsible for numerous upstream and downstream processes, into a set of microservices without disrupting operations?

Read more to explore how to seamlessly transition to microservices while maintaining system integrity and performance.

Artificial Intelligence: Following a Similar Path

The All-Knowing Demigod Models
The development of massive AI models, like GPT-4 or GPT-3, mirrors the polymaths of history. These models attempt to solve everything with a single architecture. GPT-4’s 1.7 trillion (yes, that’s 12 zeroes) parameters, are designed to handle multiple tasks like text generation, translation, coding, and more. This makes a model costly to train and maintain, and their generality often leads to inefficiencies in solving highly specialized problems. 

Specialized AI Models
The rise of smaller, task-specific models demonstrates how specialization can be more efficient and cost-effective. Models like AlphaFold for protein folding or Qwen Coder for coding focus on excelling at a single domain. Studies show that smaller models with 500 to 700 billion parameters and fewer active parameters (like 50 billion) can outperform larger, generalized models in specific tasks while being significantly more efficient. 

A smart approach to achieving this efficiency is model distillation, where knowledge from a larger general model is compressed into a smaller, more specialized version while retaining high performance. This technique enables medium-to-large models to operate with the efficiency of smaller specialized models. For example, DeepSeek leverages model distillation to refine its AI capabilities, allowing it to perform targeted tasks with lower computational overhead while maintaining high accuracy.

• Singular Large Model (GPT-4 example considered): 
  For Training GPT-4, it would require up to 10,000 GPUs running 24/7 for weeks, costing $100M+ for a single training run. For only being Hosted for Inference (i.e. Running the model to generate answers without training it) on popular Cloud Providers such as AWS, Azure, GCP, it would cost approximately $3.7M per month for Compute, Storage, and Data Transfer.
• Specialized Small Model (codeBERT example considered):
  For training codeBERT, it would require 16 GPUs running for 7 Days, with a total estimated cost of $12,000 per training run. Fine-tuning would cost approximately $40-$100 for a typical fine-tuning task. For only being Hosted for Inference on Cloud, it would cost approximately $80-$100 per month.

The Future of AI-based Solutions
Just like software evolved from Monolithic Applications to Microservices-based architectures, AI is moving towards a modular, API-driven AI Services path. This shall allow organizations to mix, match, and integrate lightweight AI models, each fine-tuned for specific requirements without relying on a single all-encompassing system.

For example, AI-powered e-commerce recommendation engines use multiple specialized models for product ranking, fraud detection, and customer sentiment analysis.

Future AI systems will be context-aware, dynamically adapting to users, environments, and business conditions in real-time. Instead of pre-trained static models, continuous learning AI will fine-tune itself based on live user feedback.

This rapid shift toward smaller, specialized AI models also benefits consumers by reducing reliance on industry giants like OpenAI, Meta, and Google, who currently dominate the market due to their ability to afford the exorbitant training costs of massive AI models. These companies recoup their expenses by charging high token fees for API access, effectively limiting affordability and accessibility for businesses and developers. With the rise of lightweight, domain-specific AI models, companies and independent developers can deploy cost-efficient AI solutions without being locked into expensive proprietary ecosystems, fostering greater competition, innovation, and affordability in the AI space.

Where does AI fit in for Retail OMS?
AI-driven solutions in Retail Order Management Systems (OMS) should not aim to reinvent the wheel, nor should their primary focus be on simply embedding chatbots into every feature. Instead, AI should be leveraged to enhance and optimize core OMS functionalities, making existing processes smarter, more accurate, and more efficient.

One critical area where AI can deliver tangible improvements is Available-to-Promise (ATP) calculations. ATP is essential for accurate inventory allocation, helping businesses determine what stock is available, where it is located, and how quickly it can be fulfilled. However, traditional ATP models often struggle with:

• Complex demand forecasting (especially in volatile markets).
• Incomplete or delayed data from multiple fulfillment centers.
• Rigid rule-based logic that fails to adapt to real-time changes.

How AI Enhances ATP Accuracy
This is where Machine Learning (ML) models can significantly improve ATP efficiency:

• Advanced Demand Forecasting – AI models can analyze historical sales data, seasonal trends, and external factors (like weather, market trends, and economic conditions) to provide highly accurate demand predictions, reducing stockouts and overstock scenarios.
• Real-Time Inventory Optimization – AI can dynamically adjust ATP calculations based on live supply chain data, warehouse updates, and real-time customer demand, ensuring more precise inventory commitments.
• Automated Anomaly Detection – AI-powered systems can detect data inconsistencies, supplier delays, or unexpected demand spikes, triggering proactive adjustments to ATP results before they lead to fulfillment issues.

Beyond ATP: AI-Driven Enhancements in Retail OMS 
AI doesn’t just stop at improving ATP accuracy—it can transform multiple facets of Retail OMS to drive efficiency, cost savings, and customer satisfaction:

• AI for Inventory Forecasting: Specialized ML models can predict stock replenishment needs, optimize safety stock levels, and reduce excess inventory carrying costs.
• AI for Delivery Lead Time Optimization: Predictive models can calculate realistic delivery windows by analyzing historical carrier performance, traffic conditions, and fulfillment center processing times—helping reduce missed SLAs and improving last-mile efficiency.
• AI for Intelligent Order Routing: Instead of following static rules, AI-driven order routing can dynamically assign fulfillment locations based on factors like cost optimization, proximity to the customer, warehouse workload, and transit reliability.
• AI-Powered Fraud Detection: By analyzing order behavior, payment patterns, and geographic data, AI can flag potential fraudulent transactions before they impact operations.
• AI in Personalized Promotions & Dynamic Pricing: Retailers can leverage AI-driven insights to optimize pricing strategies and promotions based on customer purchase history, real-time demand, and competitor pricing trends.
  
  

Nextuple demonstrated its AI Expertise by helping North America’s largest specialty jewelry retailer reduce their estimated delivery dates (EDD) by ~4 days and improve drop shipment conversions by 15%. By leveraging historical order and shipping data to train a Predictive Promise ML model, Nextuple accurately predicted EDD at a SKU-vendor level with 98% accuracy, enabling the retailer to streamline operations and provide a better customer experience.

AI Should Complement, Not Replace, Core OMS Logic
While AI introduces powerful optimizations, it should be designed as an enhancement layer—augmenting existing rule-based systems, business logic, and human decision-making rather than entirely replacing them.

• The goal is to increase accuracy, speed, and adaptability in OMS processes, not introduce unnecessary complexity or overhaul proven workflows.
• AI should be integrated where it delivers measurable improvements, ensuring that innovation remains practical, scalable, and ROI-driven.
  
  

Smarter, Not Just More AI
For AI to truly add value in Retail OMS, it should be about delivering smarter, more accurate solutions to existing problems—not about adding AI for the sake of AI. By enhancing ATP calculations, refining inventory accuracy, reducing delivery lead times, and improving order fulfillment, AI can elevate retail operations without disrupting core OMS functionalities.

Harness the power of your data to drive smarter decisions, optimize operations, and unlock new possibilities. Nextuple provides comprehensive AI/ML and data solutions tailored to the unique needs of retailers, grocery businesses, wholesalers, and 3PL providers.

If you're looking to demonstrate the feasibility and value of AI/ML solutions in addressing specific business challenges within the inventory and order management domain, we can help.

We offer a 4-6 week Proof of Concept (POC) where we will develop a prototype using your historical data to train models, validate results, and provide actionable insights. The POC includes key stages such as data collection, preprocessing, model development, and evaluation.