Prediction markets and AI-enhanced forecasting tools combine crowd-sourced market signals with model-driven analysis to improve probabilistic estimates for events, product outcomes, and competitive moves. This topic covers platforms that host tradable forecasts and the complementary AI stack organizations now use to ingest data, run models, and operationalize predictions across Market Intelligence, Data Analytics, and Competitive Intelligence workflows. Relevance: As of early 2026, organizations face faster information cycles and greater demand for quantified scenario planning. Integrating prediction markets with live data pipelines and LLM-driven agents helps teams surface early signals, calibrate models, and turn probabilistic forecasts into operational actions while also raising new governance and validation needs. Key tool categories and roles: data platforms (e.g., Domo) unify and prepare real‑time sources so market prices and model inputs stay current; developer frameworks (e.g., LangChain) enable building, testing, and deploying LLM agents that synthesize market signals and external research; knowledge workspaces (e.g., Notion) capture hypotheses, evidence, and decision trails; enterprise assistants (e.g., IBM watsonx Assistant) automate multi‑agent orchestration and stakeholder workflows; research and grounding tools (e.g., Researchspace, Perplexity AI) provide sourced evidence to validate signals; model infrastructure (e.g., Together AI) supports fine‑tuning and scalable inference; and developer productivity tools (e.g., GitHub Copilot) accelerate building integrations and workflows. What to evaluate: look for data connectivity and latency, model explainability and calibration, integration between market feeds and internal analytics, governance and audit trails, and scalability of model training/serving. Taken together, these capabilities define how prediction markets and AI forecasting shift from experimental pilots to operational intelligence.