AI workflow platforms: how to compare and choose

AI workflow platforms serve as the interface between AI reasoning and business operations, offering a layer between raw LLM APIs and current manual workflows. Organizations are increasingly investing in generative AI, with workflow automation being a key use case, as evidenced by a Deloitte report and IDC's projection of AI platform spending surpassing $150 billion by 2027. The guide suggests evaluating AI workflow platforms based on four axes: builder model, AI depth, execution model, and pricing model, highlighting the capabilities of CodeWords, which combines conversational and code-level depth for AI-native workflows. AI workflow platforms are distinguished from traditional automation tools by their ability to handle unstructured input, utilize model-based processing, and adaptively route tasks based on AI reasoning, thus offering more flexible and dynamic solutions compared to traditional rigid structures. As AI workflow platforms evolve, trends such as agentic workflows, multi-modal processing, composable workflows, and enhanced evaluation and observability are emerging, emphasizing the need for strategic infrastructure decisions when selecting a platform.

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