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What is an algorithm?
An algorithm is a precise, finite sequence of instructions that produces a desired output from an input.
The term originates from the 9th-century Persian mathematician al-Khwarizmi and is now one of the central building blocks of computer science. Every program, every app, and every AI model ultimately consists of algorithms. An algorithm must be deterministic (each step is unambiguous), finite (completed after a finite number of steps), and executable.
We encounter algorithms constantly in everyday life: a recipe is an algorithm, as are directions. In IT, algorithms range from simple sorting methods and encryption to complex machine learning processes.
For businesses, the concept of an algorithm is important because it bridges the gap between business requirements and technical implementation—and serves as the foundation for confidently working with AI.
Best Practices for Working with Algorithms
These six principles will help you choose and implement algorithms correctly:
- Clearly define the problem: Before choosing an algorithm, the business requirement must be clear.
- Choose the right algorithm: No single algorithm is suitable for everything. The right fit for the problem is crucial.
- Consider data quality: Even the best algorithm will fail if the input is poor.
- Assess the effort required: Consider runtime and memory usage (Big-O) early on.
- Test and validate: Systematic testing reveals edge cases.
- Document: Record the decisions and limitations of the algorithm in writing.
Common Mistakes When Working with Algorithms
These pitfalls are particularly common in practice:
- Overengineering: A complex algorithm when a simple one would suffice.
- Ignored edge cases: Empty inputs, duplicates, or outliers are not taken into account.
- Incorrect data types: If numbers are stored as text, the result can go awry without anyone noticing.
- Black-box effect: With AI algorithms, it’s difficult to trace how the result was derived.
- Lack of monitoring: Even algorithms in production need to be monitored.
Classic Algorithm vs. ML Algorithm
Two worlds with different strengths—and optimal areas of application:
- Classical: Rules defined by the developer, high explainability, significant programming effort. Ideal for clear, rule-based problems.
- Machine Learning: Rules learned from data, often limited explainability, data and training effort. Ideal for complex patterns.
- Hybrid: Combined in modern software—classical for structure and security, ML for tasks that are difficult to regulate.
The Right Algorithm for Your Task
In a free initial consultation, we’ll take a look at your specific task and identify the right algorithm—classical, ML, or hybrid.
As a software and AI partner for small and medium-sized businesses, we select algorithms based on your needs, not on buzzwords—and bring them into production.
What We Offer
- Software Consulting — Selecting the right algorithm for your requirements.
- AI Consulting — Integrating ML and deep learning algorithms into your processes.
- Implementation — Agents & RAG — algorithms combined with your expertise.
- AI Training — A practical approach to using algorithms for your teams.
