AI Challenges That Remain examines why enterprise AI pilots often fail to scale, identifying hidden “quiet failures” in processes and infrastructure that impede production deployments. We draw conclusions from observations of real-world deployments across speech recognition, RAG pipelines, evaluation stacks, and infrastructure systems. The paper categorizes obstacles into six major domains. For each domain, it quantifies impact, illustrates failure modes with frontline examples, and proposes targeted engineering remedies. The goal is to provide a practical playbook for embedding reliability, transparency, and ROI into enterprise‐scale AI systems.
Key Highlights
- Model brittleness in low-resource or code-switched languages: Speech recognition accuracy drops sharply in underrepresented languages (e.g., WER > 45% in Swahili) without targeted transfer‐learning and synthetic data strategies.
- Pipeline opacity & latency: Lack of end‐to‐end traceability and GPU cold starts lead to P95 latencies exceeding 3 seconds, quietly driving users away and reducing engagement.
- Misaligned benchmarking: Offline metrics like BLEU/ROUGE often diverge from real‐world performance, necessitating lightweight human feedback loops and gated canary deployments to ensure production uplift.
- Escalating infrastructure costs: GPU compute accounts for ~45% of operational budgets, with storage and bandwidth adding another ~45%, highlighting the need for embedding compression, tiered storage, and dynamic batching.
- Business attribution uncertainty: Confidence in attributing ROI to AI features drops below 50% after two weeks, requiring layered dashboards and causal inference frameworks to isolate feature impacts over time.
