Observed Signal · Sep 16, 2026 · Market Signal · Source: Collibra · Impact: 3.5/5
New Survey from Collibra by The Harris Poll Finds 72% of Tech Decision-Makers Feel AI Initiatives Today Are Falling Short
Nearly Nine in Ten Respondents [87%] Burn Hours Re-Verifying Context for AI Agents, with 76% Facing Roadblocks Moving from Pilot Programs into Production. Collibra, the enterprise...
Track Collibra Signals & Market Shifts in Real-Time
Polaris7 autonomous intelligence agents track regulatory filings, primary sources, executive changes, and deal flow 24/7. Create your free Explorer workspace to monitor these entities.
Connected Companies & Entities
1 Entity mappedRelated Market Signals & Shifts
Recent verified developments and strategic activity across this market segment.
Survey Finds 72% of Tech Decision-Makers See AI Initiatives Falling Short
A new survey by Collibra and The Harris Poll reveals that 72% of tech decision-makers believe AI initiatives fail due to poor data foundations. The survey, conducted among 306 US decision-makers, found that 87% burn hours re-verifying AI agent context, and 76% face roadblocks moving from pilots to production. Additionally, 51% manually review AI outputs. The report highlights the need for better data governance and runtime controls to scale AI effectively. Collibra's CEO emphasizes the 'hallucination tax' as a hidden cost of manual oversight. The findings also reference Gartner research indicating 50% of enterprise GenAI projects are abandoned after proof of concept.
Agentic AI: 80% of Companies Still Testing AI Agents
A new Lünendonk study reveals that autonomous AI agents, while strategically important to many companies, are still mostly in pilot or exploration phases. Only 19% of surveyed companies use AI agents in specific tasks, and just 1% have achieved end-to-end integration across core processes. The study, titled 'Agentic AI: From Copilot to Autopilot', highlights a significant gap between expectation and implementation, with 96% expecting efficiency gains and 90% anticipating increased speed and flexibility. Scaling from pilot to production remains difficult due to technical shortcomings, inadequate data integration, security concerns, and a lack of skilled personnel. Companies are planning heavy investments in data quality, orchestration platforms, and IT infrastructure over the next two years, as well as in digital enablement and AI compliance. Most expect AI agents to supplement existing applications rather than replace them entirely.
Why AI Agents Fail in Production: The 2026 Reliability Crisis
An analysis of the 2026 AI agent reliability crisis highlights a massive performance gap between pre-deployment benchmark testing and real-world production. According to data from the Agent Reliability Collective (ARC) covering 1,247 agents, average task accuracy plummeted by 23.5 percentage points (from 91.3% to 67.8%) once deployed. The failures are attributed to five main gaps: distributional drift in user inputs, toolchain fragility, context window collapse in extended conversations, reward hacking, and a lack of negative or adversarial testing. To combat these failures, the AI engineering community is transitioning to a new paradigm of agent testing, including LLM-driven adversarial test generation, chaos engineering, comprehensive telemetry, and formal policy verification to ensure system survivability in production environments.
Track Real-Time Market Signals & Shifts
Set up custom watchlists to receive automated, evidence-grounded executive digests whenever material signals or shifts occur across your tracked landscape.