Industry problem statement · October 2026

Why AI stalls between demo and production

Industry-wide pain points across LLM, AI, data and cloud deployments. Every number below is from a third-party source, listed at the bottom of this page.

Pain point 1 · Pilot-to-production gap

Most GenAI pilots never reach production

5%
of custom enterprise GenAI tools reach production; 60% evaluated, 20% piloted
[1]
≥50%
of GenAI projects were abandoned after proof of concept by end of 2025
[2]
39%
report any enterprise-level EBIT impact from AI
[3]
  • About two thirds have not begun scaling AI across the enterprise [3]
  • Pilots die on brittle workflows, not on model quality [1]
  • Why they die: bad data, weak risk controls, cost, unclear value [2]
Pain point 2 · Compute waste and runaway cost

GPUs idle, cloud bills overrun, token costs compound

68%
of companies report peak GPU utilization of 70% or less
[5]
27%
of IaaS and PaaS cloud spend is estimated as wasted
[4]
84%
rank managing cloud spend as their top cloud challenge
[4]
  • Cloud budgets already run 17% over, while cloud spend is expected to grow 28% next year [4]
  • 49% of enterprises say most or nearly all of their compute is inference [6]
  • Per-token cost looks negligible in a PoC, then becomes a TCO problem across thousands of users [2]
Pain point 3 · Vendor lock-in

Rented intelligence, rising switching costs

87%
of enterprise LLM workloads run on closed-source models
[6]
11%
of teams switched LLM vendors in the past year; 66% only upgraded within their vendor
[6]
37%
of enterprises run 5+ models, each with its own API
[7]
  • Three providers hold 32%, 25% and 20% of enterprise LLM usage [6]
  • Agentic workflows tie prompts and QA to one model [7]
  • API wrappers inherit their provider's pricing and roadmap
Pain point 4 · Data readiness and sovereignty

The data is not ready, and it cannot leave the region

63%
of organizations lack, or are unsure they have, the right data management for AI
[8]
60%
of AI projects without AI-ready data will be abandoned through 2026 (forecast)
[8]
$80B
sovereign cloud IaaS spend forecast for 2026, up 35.6% year over year
[9]
  • Sovereignty demand will shift 20% of current workloads from global to local cloud providers [9]
  • Residency and governance now extend to prompts, embeddings, vector stores and inference logs
Pain point 5 · Reliability and observability

Costly incidents, barely observed LLMs

$2M/hr
median cost of a high-impact outage; engineers spend 33% of time firefighting
[10]
41%
find outages via complaints, tickets or manual checks
[10]
233
AI incidents reported in 2024, a record and up 56.4% year over year
[11]
  • LLM observability in production: 7%; still exploring: 47% [12]
  • About 1 in 3 report consequences of AI inaccuracy [3]
Pain point 6 · Security and compliance

AI adoption is outrunning AI security and governance

97%
of organizations with an AI-related breach lacked proper AI access controls
[13]
$670K
added breach cost for organizations with high levels of shadow AI
[13]
€35M / 7%
EU AI Act maximum fine for prohibited practices (Art. 99)
[15]
  • Prompt injection is LLM01, the top OWASP LLM risk for 2025; no single control fully prevents it [14]
  • EU AI Act: Art. 50 transparency applies from 2 Aug 2026; Annex III high-risk duties from 2 Dec 2027 [15] [16]
Pain point 7 · Talent shortage and tool sprawl

Too few platform experts, too many tools to stitch

51%
of tech leaders report an AI skills shortage, up from 28% in the prior report
[17]
8
observability technologies in use per organization on average
[12]
19%
have GPU scheduling that can optimize utilization
[5]
  • IT skills gaps to hit 90%+ of organizations, costing $5.5T [18]
  • Complexity is the #1 observability obstacle, cited by 39% [12]
How Warble responds

Own the stack, keep people in the loop

  • Owned infrastructure: everything runs in your cloud account, so there is no rented control plane.
  • A production path from day one: Murmuration ends in a written plan; FRAKMA-X takes models from data to production through Git.
  • Scoped agent access: Starling-EX gives agents structured, auditable tools instead of raw credentials.
  • Human-approved remediation: Reflexion runs in shadow mode by default.
Sources

Every number on this page, and where it comes from

  1. MIT NANDA, The GenAI Divide: State of AI in Business 2025 (Jul 2025). https://mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Business_2025_Report.pdf
  2. Gartner, Why 50% of GenAI Projects Fail (Jan 2026). https://www.gartner.com/en/articles/genai-project-failure
  3. McKinsey, The State of AI in 2025 (Nov 2025). https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
  4. Flexera, 2025 State of the Cloud Report (Mar 2025). https://www.flexera.com/about-us/press-center/new-flexera-report-finds-84-percent-of-organizations-struggle-to-manage-cloud-spend
  5. ClearML, AIIA & FuriosaAI, State of AI Infrastructure at Scale 2024 (Mar 2024). https://clear.ml/blog/the-state-of-ai-infrastructure-at-scale-2024
  6. Menlo Ventures, 2025 Mid-Year LLM Market Update (Jul 2025). https://menlovc.com/perspective/2025-mid-year-llm-market-update/
  7. a16z, How 100 Enterprise CIOs Are Building and Buying Gen AI in 2025 (Jun 2025). https://a16z.com/ai-enterprise-2025/
  8. Gartner, Lack of AI-Ready Data Puts AI Projects at Risk (Feb 2025). https://www.gartner.com/en/newsroom/press-releases/2025-02-26-lack-of-ai-ready-data-puts-ai-projects-at-risk
  9. Gartner, Worldwide Sovereign Cloud IaaS Spending Will Total $80B in 2026 (Feb 2026). https://www.gartner.com/en/newsroom/press-releases/2026-02-09-gartner-says-worldwide-sovereign-cloud-iaas-spending-will-total-us-dollars-80-billion-in-2026
  10. New Relic, 2025 Observability Forecast (Sep 2025). https://newrelic.com/press-release/20250917
  11. Stanford HAI, AI Index Report 2025: Responsible AI. https://hai.stanford.edu/ai-index/2025-ai-index-report/responsible-ai
  12. Grafana Labs, Observability Survey 2025 (Mar 2025). https://grafana.com/observability-survey/2025/
  13. IBM, Cost of a Data Breach Report 2025 (Jul 2025). https://newsroom.ibm.com/2025-07-30-ibm-report-13-of-organizations-reported-breaches-of-ai-models-or-applications,-97-of-which-reported-lacking-proper-ai-access-controls
  14. OWASP, Top 10 for LLM Applications 2025: LLM01 Prompt Injection. https://genai.owasp.org/llmrisk/llm01-prompt-injection/
  15. European Commission AI Act Service Desk, Article 99 Penalties (Jul 2026). https://ai-act-service-desk.ec.europa.eu/en/ai-act/article-99
  16. Vorp Labs, EU AI Act as of August 2026. https://vorplabs.com/ai-regulatory-updates/reports/2026-08-eu-ai-act-in-force-inventory
  17. Nash Squared / Harvey Nash, Digital Leadership Report 2025 (May 2025). https://www.nashsquared.com/dlr-2025/dlr-2025-press-release-usa
  18. IDC, IT skills shortage forecast (May 2024). https://www.businesswire.com/news/home/20240514939927/en/