Optimizing AI for Business: How to Build Composable Solutions that Leverage Machine Learning, Generative AI, and Modular Tools

Artificial intelligence (AI) is no longer a one-size-fits-all solution; it is a toolbox of diverse technologies that work together to drive innovation, efficiency, and growth. Machine Learning (ML) and Generative AI (Gen AI) represent two critical yet distinct pillars in this AI toolkit. ML is powerful for generating objective, deterministic outputs, while Gen AI brings flexibility with subjective, probabilistic outputs. Together with modular tools like web apps, content delivery systems, data pipelines, and calculators, these technologies create robust, scalable solutions that cater to specific business needs. This article explores the practical applications of ML and Gen AI, explaining how composable solution frameworks can streamline processes, boost performance, and enhance customer experiences.

Machine Learning: Objective Outputs for Precision and Efficiency

Machine learning thrives on data to generate accurate, deterministic outputs. ML models are ideal for scenarios requiring reliability, such as:

• Customer Retention: Predictive ML models identify potential churn risk based on user behavior patterns, enabling companies to proactively retain customers.
• Fraud Detection: By analyzing patterns in transaction data, ML can flag suspicious activities with high accuracy, improving financial security.
• Supply Chain Optimization: Using historical and real-time data, ML forecasts demand, optimizing inventory and logistics for efficient operations.

ML requires labelled, structured data and regular training in these applications, making it the preferred choice for businesses needing consistency and repeatability.

Generative AI: Subjective Outputs for Creativity and Adaptability

Generative AI shines in tasks that demand creativity and flexibility. Gen AI models excel at creating new content that mimics human creativity, making it indispensable for industries like marketing, design, and customer engagement. Key applications include:

• Data Labeling and Structuring: Generative AI can streamline the data preprocessing stage by automatically labelling, structuring, and enriching data with metadata. This capability saves time and ensures data quality, especially in fields requiring large volumes of labelled datasets, such as healthcare and autonomous vehicles.
• Personalized Content: E-commerce and media companies leverage Gen AI to create unique, individualized content for consumers, increasing engagement and conversion rates.
• Product Design: Gen AI generates multiple design variations, helping designers in industries like automotive and consumer products innovate faster and test new ideas.
• Dynamic Marketing: Gen AI generates personalized ads and social media content, empowering brands to connect with diverse audiences through tailored messaging.

Generative AI can process structured and unstructured data, making it adaptable to various environments without constant retraining.

Composable Solutions: Building Scalable, Cost-Effective Solutions

The combination of ML and Gen AI, along with other modular tools like calculators and apps, creates a powerful composable solution that businesses can customize for diverse needs. This composability optimizes costs and improves performance by deploying the right AI tool for each specific task. Building composable solutions allows companies to develop better-performing, cost-effective, and future-proofed solutions that take a one-AI-fits-all approach. Here’s how it works:

• Cost Reduction: By using ML for operational tasks and Gen AI for creative processes, companies save on computational resources, as each AI type is applied only where it is most efficient.
• Scalability: Modular tools and composable AI solutions can easily be adapted and scaled, supporting business growth without overwhelming resources.
• Vendor replaceability and redundancy: Composable solutions allow you to incorporate A/B testing of different vendors and resilience so that the failure of a single vendor does not result in the failure of your solution.
• Improved Customer Experience: ML-driven insights improve process efficiency, while Gen AI enables more personalized, engaging customer interactions.

Composable solutions leverage ML’s stability and Gen AI’s adaptability to create end-to-end workflows that streamline both back-end and customer-facing operations, reducing overall expenses and enhancing customer satisfaction

Case Study: Combining ML and Gen AI for Property Insights

Consider an AI Property Advisor aiming to provide smarter and more insightful advice and decision support throughout the property ownership journey. Here’s how a composable AI solution might look:

• Home Value and Mortgage Rate Forecasts: ML algorithms analyze historical market and macroeconomic data, predicting best, most likely and worst case rate and home value scenarios for each property type in each sub-market.
• Personalized Recommendations: Gen AI uses customer preferences to comb through property listing descriptions, data, and photographs, automatically labelling, structuring, and enriching a master list with metadata so that it can recommend the properties that the buyer is most likely to want to view, saving time for the buyer and the real estate agent.
• Interactive Web Apps: These modular tools allow customers to explore scenarios, analyses, and insights using their preferred interface. They can use a dynamic web app that provides interactive data visualization, tools, guides, and calculators, or they can converse with a Gen AI Property Advisor via voice or text. Whereas before, businesses needed to think about UI in the context of Mobile, Tablet or Desktop. Now, they need to add Conversational Text and Voice to the interface. Likely also enabling chat via WhatsApp, SMS, and other popular communication apps.
• Calculators: Calculating what you can afford for a given property type in a specific municipality should provide the same answer every time, given the same inputs. The same goes for calculations like risk assessment models, property ROI calculations, and tax calculators. However, some calculators have many inputs, and even though we might not want AI to be the calculator, ML and Gen AI can help to provide historical, current, and forecast inputs for the calculation, making it more accessible and easier to use.

This synergy between ML, Gen AI, calculators, and web applications exemplifies a scalable, customer-centric model that drives efficiency and innovation.

So what?

In today’s fast-evolving digital landscape, composable solutions empower businesses to solve complex problems with greater flexibility, scalability, and cost-effectiveness. Leveraging the strengths of ML for precision and Gen AI for creativity, companies can address a broader range of needs—from better forecasting to enhancing property ownership experiences. By understanding and implementing these technologies wisely, we plan can future-proof our operations and thrive in the competitive AI era.

Check out these links for further reading on how ML and Gen AI differ and more about composable solutions:

• Machine Learning vs Generative AI: A Detailed Look
• The Differences Between Generative AI and Machine Learning
• Point-of-View: Composable Architecture Solutions with Specialized AI Agents
• Composable architecture x AI: Enabler for optimization & innovation
• Composable Architecture: Definition & How To Harness Its Potential For Enterprises