Energy and utilities

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.

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.

Sweden's major population centers, including Gothenburg, Stockholm, and Malmö, are faces with a looming threat of power shortages due to capacity constraints in the national grid. Property owners, transportation sectors, and heavy industries will face challenges to drive their business. AI is part of the toolbox to solve this - but getting started is key.

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.