The GenAI Divide: State of AI in Business 2025
95% of enterprise GenAI pilots fail to reach production; the gap is integration and workflow, not model quality.
Read →The evidence behind the architecture.
Independent papers and reports on the patterns Koheriant deploys.
95% of enterprise GenAI pilots fail to reach production; the gap is integration and workflow, not model quality.
Read →Roughly a quarter of executives report material value from generative AI; the rest remain stalled in pilot.
Read →AI requires re-architecting the operating model; bolting AI onto legacy workflows produces marginal gains at best.
Read →Multi-agent conversation frameworks coordinate complex workflows that single agents cannot complete reliably.
Read →Most production agent systems combine simple, well-scoped workflows; complex autonomy is rarely the right default.
Read →Interleaving reasoning traces with tool actions outperforms either alone — the harness shapes the outcome.
Read →Frontier model capabilities are converging; enterprise differentiation increasingly comes from orchestration and integration, not model choice.
Read →Combining parametric memory with non-parametric retrieval outperforms either approach alone.
Read →Self-reflective retrieval improves accuracy and reduces hallucination over standard RAG.
Read →Graph-structured RAG outperforms vector-only RAG on global summarization across enterprise corpora.
Read →Establishes the govern, map, measure, and manage functions for trustworthy AI deployments.
Read →High-performing organizations treat AI as a workflow rewire, not a tool deployment; data residency and integration sit at the center.
Read →Agents with persistent memory and planning loops produce coherent autonomous behavior over long horizons.
Read →Self-reflection over past trajectories improves agent task completion without parameter updates.
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