IIoT World’s sixth annual Industrial Data and AI Readiness Survey is open. Last year, 272 industrial professionals across manufacturing, energy, pharma, and oil and gas responded. Only 7% had AI embedded in core processes, while 44% expected to reach that level within three years. The 2027 edition measures whether the industry has narrowed that distance.
The survey takes about 5 minutes to complete. All participants will receive a free copy of the full research report.
What Did 272 Professionals Reveal About Industrial AI in 2026?
Data quality and availability outranked compute, algorithms, and budget as the primary barrier to scaling AI in manufacturing, energy, pharma, and other industrial sectors. The 2026 Industrial AI Readiness Report made this clear across every category it measured.
54% of respondents identified data quality and availability as their top AI adoption barrier. Legacy system integration and data silos followed at 48%. Budget, skills, and leadership alignment all trailed behind.
Predictive maintenance led AI use cases at 64%, followed by process optimization at 55%. But only 34% had production systems with real-time data streaming in place, raising a question about whether the infrastructure matches the ambition. 53% expected AI to reduce downtime. 52% expected improved OEE (Overall Equipment Effectiveness).
Previous editions of the survey, then called Building IIoT Systems, had flagged similar data readiness challenges. The 2025 edition, 2024 edition, and 2022 results all documented the persistence of data quality and integration barriers across industrial organizations.
What Does the 2027 Survey Measure?
The 2027 survey contains 15 questions that map AI readiness from data infrastructure maturity through autonomous decision-making across manufacturing and industrial operations. Each section builds on what the 2026 data revealed.
Where is your data foundation? The survey maps maturity from fragmented systems to production-ready infrastructure across multiple facilities, and captures the specific integration method: MQTT-based event streaming, Unified Namespace (UNS), Apache Kafka, OPC UA, batch data transfers, point-to-point integrations, or manual exports.
How much do you trust your operational data? Respondents rate confidence and identify obstacles: poor quality, missing context, inconsistent asset naming, schema inconsistencies, governance gaps, different standards across sites, delayed data, or unknown ownership.
Which AI use case has delivered real value? Options span predictive maintenance, quality inspection, process optimization, root cause analysis, energy optimization, digital twins, asset performance monitoring, and engineering knowledge assistants.
Would you let AI make operational decisions? The survey maps the spectrum from AI as analysis tool to AI executing actions autonomously under human oversight, and captures barriers: trust, explainability, safety, governance, cybersecurity, and organizational resistance.
Where will industrial AI be by 2030? Isolated use cases, plant-level decisions, multi-site coordination, or enterprise-wide competitive advantage.
2026 Findings vs. 2027 Survey Focus
| 2026 Finding | What the 2027 Survey Measures |
| 54% cited data quality as top AI barrier | How confident are you in your operational data for AI workloads? |
| Only 34% had real-time data streaming | What is your primary data sharing method? (MQTT, UNS, Kafka, OPC UA) |
| 64% deploying or planning predictive maintenance | Which AI use case has delivered the greatest business value? |
| 7% had AI in core processes; 44% expected it in 3 years | Where do you expect industrial AI to be by 2030? |
| 48% cited legacy integration as a barrier | What is your biggest challenge to a unified data foundation? |
How Do You Participate?
The survey is open to OT engineers, IT/OT architects, plant managers, data leaders, executives, and system integrators across all industrial sectors and company sizes. Responses are anonymized and will be aggregated into the 2027 Industrial Data and AI Readiness Report, distributed free to every participant who provides their email.
The survey closes October 17, 2026.
Take the 2027 Industrial Data and AI Readiness Survey
Frequently Asked Questions
1. Who should take this survey?
OT engineers, manufacturing IT and OT leads, enterprise and IIoT architects, data and analytics leaders, plant managers, digital transformation executives, system integrators, and consultants working in manufacturing, energy, pharma, oil and gas, chemicals, food and beverage, automotive, mining, transportation, and smart infrastructure.
2. How long does it take?
5 to 7 minutes, 15 questions.
3. What did the 2026 report find?
272 professionals responded. 7% had AI in core processes. 54% cited data quality as the top barrier. 44% expect embedded AI within three years. 64% are deploying or planning predictive maintenance. Full findings in the 2026 report.
4. Is the report free?
Yes. Participants who provide their email receive the full 2027 report when it’s live (targeting first quarter of 2027)
