Complete library

The complete collection of all our insights, offering a comprehensive range of articles spanning foundational concepts to advanced strategies, providing guidance and inspiration for every stage of the algorithmic business journey.

Enterprise IT was built for standardization - digital colleagues make that obsolete

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

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Enterprise software is dead(ish) - time to move on

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.

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Sustaining impact Frida Holzhausen Sustaining impact Frida Holzhausen

Beyond deployment: Embracing AI sustainment for lasting value

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.

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Sustaining impact Frida Holzhausen Sustaining impact Frida Holzhausen

Navigating data drift to future-proof your ML models

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.

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Building algorithmic solutions Frida Holzhausen Building algorithmic solutions Frida Holzhausen

Building the algorithmic business: Machine learning and optimization in decision support systems

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.

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