A practical framework for separating genuine AI use cases from simpler automation needs and process problems
Start with a measurable business problem: slow response times, missed revenue, rework, errors, or poor visibility. Consider the technology only after the current process is clear
If the output follows fixed rules, conventional automation is usually cheaper, faster, and easier to control. AI becomes relevant when the input is variable or unstructured
AI is useful when the work involves language, documents, speech, classification, fact extraction, controlled generation, or retrieval from approved knowledge. Even then, the system still needs trusted sources, validation, and a fallback
Compare process redesign, conventional automation, and an AI-assisted workflow against the same metric. The best answer may be not to use AI at all
You do not need a detailed brief to begin
An AI opportunity audit can help choose the first use case; for call-based scenarios, see Voice AI solutions
Learn how we assess the process, business case, data security, and quality before development begins
Explore the methodology →Determine whether the task calls for AI, rule-based automation, or a process redesign
Assess a process →Send the current process, constraints, and expected outcome, and we will suggest a practical first step
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