Abstract
Enterprises have adopted artificial intelligence (AI) faster than they have converted it into value. This paper examines why most enterprise AI projects stall between proof of concept (PoC) and production, and proposes a framework for crossing that gap. It presents a structured narrative review of nine major studies and industry surveys published between July 2024 and February 2026, including work by RAND, MIT Project NANDA, McKinsey, Gartner, IDC, S&P Global Market Intelligence, PwC and Dell Technologies, with findings coded thematically to identify recurring failure mechanisms.
Across sources, between roughly 30% and 95% of AI initiatives fail to reach production or deliver measurable value, depending on how failure is defined. IDC found that only 4 of every 33 PoCs reached production, and S&P Global reported that 42% of companies abandoned most of their AI initiatives in 2025, up from 17% in 2024. Six mechanisms explain most stalls: problem–value misalignment, data not fit for production, missing production infrastructure, unchanged workflows, governance and risk controls added too late, and weak ownership and incentives. Technical model capability is rarely the binding constraint.
The paper synthesizes this evidence into the Pilot-to-Production Readiness Model (P2P-RM), a five-dimension, gate-based framework, and extends the analysis to African enterprises, where skills, infrastructure and data-governance gaps intensify the same mechanisms. It concludes that leaders should fund fewer, production-intended pilots, define value metrics before the build, invest in shared data and deployment platforms, redesign workflows rather than bolt AI onto them, and embed governance from the first sprint.
Keywords
enterprise AI; pilot-to-production; AI scaling; generative AI; MLOps; AI governance; organizational readiness; Africa
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