AI is powerful – but only when used correctly. We implement AI in specific, narrow use cases where the technology truly excels. Not as a catch-all solution, but as a precision instrument for well-defined problems with measurable outcomes.
“Through their expertise in data processing and analysis, they help us transform large amounts of information into clear strategic insights that our customers can use for well-founded decisions. Their work in summarizing and visualizing data enables not only faster decision processes, but also better business strategies for our customers.”
Mårten Brandt
CFO, Klarsynt
Trying to solve overly broad problems with AI instead of scoping narrowly
Investing in AI projects without clear, measurable goals
Underestimating the importance of data quality and well-defined processes
The AI hype has created an expectation that the technology can solve everything. Reality is different. AI excels in specific, narrow problems – pattern recognition, forecasting, classification. But for broader, more complex challenges, traditional methods are often both cheaper and more effective.
At Stormyran, we always start with an honest assessment: Is AI really the right solution for your problem? We only implement AI where it demonstrably outperforms alternative methods. That means we sometimes recommend simpler solutions – because our goal is to solve your problem, not to sell AI projects.
The most successful AI implementations share common traits: a clearly scoped problem, sufficient quality data, and measurable success criteria. Document classification, demand forecasting, anomaly detection in transaction data – these are examples of well-defined use cases where AI consistently delivers.
We've seen far too many AI projects fail because they tried to solve problems that were too broad. Our experience shows that success comes from scoping narrowly, focusing, and iterating. Start small, validate quickly, scale what works.
Define a specific, measurable problem
Test if AI actually outperforms alternatives
Implement only if value is proven
Track against defined success criteria
AI works best in narrow problems with clear inputs and outputs. Here are examples of use cases where we've seen consistent success.
Automatically categorize invoices, contracts, and documents
Predict inventory needs based on historical data
Identify unusual transactions or behaviors
Extract structured data from unstructured sources
What these use cases have in common is clear success criteria, sufficient data foundation, and proven AI superiority over manual or rule-based alternatives. We help you identify if your problem fits this category – and if not, we'll suggest a better solution.
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