Retail and
consumer goods

The rules of enterprise software are being rewritten. For decades, the strategy was clear: standardize processes to cut costs and streamline operations, paving the way for ERP, CRM, and other rigid systems to dominate. These legacy systems belong to an era of fixed processes and centralized control - a model designed for uniformity and efficiency that ultimately locked businesses into inflexible structures

Enterprise software systems were built for a different era - one where businesses operated on fixed processes, structured data, and centralized control. That world no longer exists. Today, companies need real-time adaptability, work with fragmented and unstructured data, and demand flexibility that traditional systems can’t provide.

Imagine a marketing agent that recalls every interaction your business has had with each customer. It understands each customer’s unique interests and knows exactly which language or imagery will capture their attention, allowing it to automatically generate hyper-personalized marketing content tailored to individual preferences. This is already a reality today, using AI marketing agents, which is reshaping the marketing landscape, enabling businesses to deliver highly personalized experiences to their customers. By utilizing advanced data analysis and machine learning techniques, AI empowers marketers to create messages and content that resonate on a personal level, significantly boosting engagement and driving sales in unprecedented ways.

In e-commerce, visual search is becoming the "little black dress" of online shopping technology - timeless, versatile, and indispensable. AI-driven technology enables users to search with images rather than text, instantly identifying visually similar products from a photo or screenshot. Visual search is not just a trend; it is simplifying the way customers find and purchase items, offering an intuitive and efficient alternative to traditional search methods.

Perishable goods present unique challenges for supply chains. With short shelf lives, unpredictable demand, and the need for consistent cold chain management, the margin for error is slim. AI, and AI agents - new digital colleagues in supply chain teams - in particular, offers a way to address these complexities, providing advanced capabilities for improving forecasting accuracy, enhancing visibility, and building resilience. 

Deploying AI systems is just the beginning. To create business impact and realize value, these systems must be sustained to remain reliable, adaptive, and compliant over time. AI sustainment is a strategic approach to extend the lifecycle of AI models, ensuring they are performant, scalable, and aligned with business needs. Algorithma emphasizes a proactive methodology that continuously improves models, manages data effectively, and follows responsible AI guidelines—maximizing value and maintaining a competitive edge.

Companies are increasingly relying on machine learning models to make critical decisions. ML models come with a fundamental assumption: they expect the future to look like the past. In reality, the world is constantly changing, and so is the data it generates. This change, known as data drift, can silently undermine the performance of your models, leading to poor decisions, increased costs, and missed opportunities.

The ability to leverage the combined strengths of machine learning and optimization to enhance decision-making processes can significantly transform business operations. By integrating these technologies, businesses can achieve increased efficiency, reduce operational costs, and improve overall outcomes. This transformative potential is realized through practical applications in decision-making, whether by supporting human decisions or performing them autonomously.