# Enterprises Struggle to Scale AI PoCs into Production > Source: > Published: 2026-05-29 17:24:57.549899+00:00 # Enterprises Struggle to Scale AI PoCs into Production Forbes contributor Arun Goyal writes that many enterprise AI pilots stall before reaching production because integration with day-to-day operations is harder than building models. Forbes cites Gartner, reporting that, by the end of 2025, approximately **50%** of projects were abandoned at the proof-of-concept stage, blaming weak data quality, inadequate risk controls, and rising costs. The article uses **McDonald\'s** as an example, noting the company ended a 2024 drive-thru voice AI pilot after accuracy problems emerged in real-world conditions. Editorial analysis: Companies commonly underestimate variability across business-unit data standards and process flows, which raises operational maintenance and compliance burden as systems scale. ### What happened Forbes contributor **Arun Goyal** reports that many enterprise AI pilots do not reach production because the hard work begins after the demo. Forbes cites **Gartner**, saying that, by the end of 2025, about **50%** of projects were abandoned at the proof-of-concept stage, with causes attributed to weak data quality, insufficient risk controls, and escalating costs. Forbes also cites **McDonald\'s** as an example, noting the company ended a 2024 drive-thru voice AI pilot amid accuracy and real-world variability issues. ### Editorial analysis - technical context Companies running comparable pilots frequently encounter three technical frictions when moving to production: inconsistent input data schemas across business units, unmodeled edge-case distributions in operational traffic, and increased needs for monitoring and human-in-the-loop review. These frictions turn modest model error rates into operational cost through extra manual reviews, escalations, and compliance checks. ### Industry context Observed patterns in similar transitions: enterprises often validate models under controlled test sets that do not capture production distribution drift, data-entry differences across systems, or rare but high-impact failure modes. Industry reporting frames poor upstream data hygiene and immature MLOps practices as recurring causes of PoC abandonment. ### What to watch For practitioners and engineering leaders, signals that a pilot faces production risk include fragmented data standards across teams, lack of automated monitoring and rollback, and unclear error-handling workflows. Observers should also track vendor and platform offerings that package data-quality tooling, model monitoring, and compliance controls together, since those product directions respond to the friction points documented by Gartner and described in the Forbes piece. ### Practical takeaway Editorial analysis: For teams aiming to move beyond demos, prioritizing end-to-end data contracts, robust monitoring, and defined escalation paths typically matters more than incremental model-architecture gains when the objective is reliable, scalable production deployment. ## Scoring Rationale The story highlights a common and practically important barrier to AI value capture: operationalizing pilots. It is notable for engineering and product teams but does not introduce a new technology or regulation, so its practitioner relevance is moderate to high. Practice interview problems based on real data 1,500+ SQL & Python problems across 15 industry datasets — the exact type of data you work with. [Try 250 free problems](/problems)