Over the last ten years, ecommerce platforms and other related technologies, such as order management and content management systems, have embraced MACH (Microservices, API-first, Cloud-native SaaS, Headless) architecture due to its flexibility, scalability, and ease of integrating technologies. MACH architecture allows applications to be structured of smaller components and independently deployable services in place of creating one monolithic application. Knit together, these components provide all the capabilities, functions, and business logic needed to deliver a solution that is easy to update and adapt over time as innovations become available.
How MACH Supports GenAI Applications
Given its ability to easily integrate new components, MACH architecture is well suited to integrating generative AI (GenAI) applications that provide benefits to online customers, the eCommerce back office, and the fulfillment chain:
- Robust Scalability: GenAI algorithms require large amounts of computing power and data pipelines that learn and publish AI models as they evolve through training. These needs are supported by the scalability of the cloud and microservices architecture.
- Microservices Components: Developers can separate granular AI functions into dedicated microservices that are updated and run separately. This isolation promotes the ease of development and maintenance.
- Ease of Deployment: GenAI models change regularly, but their ease of deployment in a MACH environment allows frequent releases without affecting other components in the ecosystem. In this way, MACH architecture directly supports the demands and integration of GenAI services.
- Management and Monitoring: The infrastructure developed to monitor, manage, and secure a microservices architecture can be leveraged to support GenAI applications.
GenAI Tech Stack
GenAI requires a sophisticated technology stack that includes new tools, technologies, and techniques. Cloud platforms, deployment pipelines, API integrations, and other components rely on MACH architecture to deliver GenAI solutions.
| Application Interface | User Interface to one or multiple LLMs |
| Data Management | Ingestion, Data Pipelines, Data Quality, Storage |
| LLMOps | Deployment, Monitoring |
| Optimized Models | Model Training, Domain Models |
| Foundation Models | Closed Source, Open Source |
| Cloud Platforms | Cloud Hosting |
| Computation | Memory, GPU, Networking |
Working from the bottom up:
- Computation: Computational hardware is the foundation required for model training and inference. Companies that build their own Foundation Models or customize existing ones to address specific domains or use cases will need to work with GenAI hardware accelerator firms that combine hardware and software for a lower total cost of ownership. Other companies will leverage existing Foundation Models hosted by cloud providers.
- Cloud Platforms: Infrastructure vendors support the required computing power required to analyze huge data sets, and they also provide models, tools, and services needed to build GenAI applications.
- Foundation Models: Companies can use existing open-source and closed-source Foundation Models that meet their needs. They can access the Foundation Models through APIs or host them on company infrastructure. If the constraints of an existing Foundation Model, such as the number of tunable parameters, inference speed, security and privacy needs, or output quality, do not meet a company’s needs, they may opt to build their own.
- Optimized Models: When needed, companies can tune existing Foundation Models to train them using data sets related to a specific use case or domain.
- LLMOps: Large Language Model Operations (LLMOps) for GenAI are complex due to the large scale and size of the models and become increasingly important for Optimized Models.
- Data Management: More than anything else, GenAI relies on data quality, processing, and storage to create effective outcomes. Unlike other data strategies, Gen AI can use both structured and unstructured data as inputs. Companies need an unstructured data strategy to support GenAI efforts.
- Application Interface: GenAI application interfaces integrate GenAI models into user experiences that allow company users to interact with the models and solve problems.
GenAI Use Cases for Ecommerce
So, once you have the architecture, what use cases make sense for leveraging GenAI? GenAI solutions are already supporting and have infinite potential to improve customer experience and reduce costs for ecommerce businesses. In a recent Gartner executive poll, 38% indicated that customer experience and retention will be the primary purpose of their GenAI investments, followed by revenue growth (26%) and cost optimization (17%).
Many product companies are adding GenAI features to their offerings to optimize specific experiences or reduce human effort. Where those do not exist, companies can create bespoke solutions that leverage a larger, proprietary data set to deliver competitive advantage.
GenAI applications can improve customer experience:
- Personalized product recommendations: Companies can leverage GenAI to analyze customer browsing and purchase history to recommend highly relevant product suggestions for selection, cross-sell, and upsell, creating a more satisfying shopping experience and a higher likelihood of conversion.
- Visual search: GenAI can support customers searching for products using images to find more exact matches.
- 3D product views: GenAI can use images to generate 3D product visualizations that offer a more comprehensive view to the customer.
- Price optimization: AI can ingest customer information and competitor pricing, then automatically revise product pricing for specific customers to improve purchasing likelihood.
Likewise, companies can streamline internal operations by leveraging the power of GenAI:
- Virtual sales associate: AI-powered chatbots can provide real-time customer support, answer questions, and offer advice. They can also refer customers to a real person after reaching a certain threshold of knowledge or customer interest.
- Content generation: Marketers and merchants can employ AI to generate content, such as emails, product descriptions, marketing copy, and infographics. This content can be informed by the vocabulary and brand voice used in previously human-generated content as well as information about the target audience in order to address specific customer segments, improving engagement and conversion rates.
- Demand forecasting: Using inputs such as sales data and market trends, AI can predict future purchase demand with greater accuracy, allowing businesses to adjust stock levels and placement in various fulfillment centers to optimize inventory and delivery costs.
GenAI is fundamentally changing how companies approach eCommerce by enabling highly personalized customer experiences through targeted product recommendations, dynamic content creation, and automated customer service. As GenAI is leveraged to improve both customer engagement and back office efficiency, companies will optimize operations based on real-time data analysis, ultimately leading to increased sales and customer satisfaction.
