Questionnaire SIG Big Data - AI

Please help to prepare next Programmaraad agenda, by sharing your thoughts on the topic of AI in NAP and filling out this questionnaire.

Questionnaire SIG Big Data - AI

First name*

Last name*

Email address*

If we have any follow-up questions based on your responses, we may reach out to you.

Which SIG name resonates best with you?*

The new name of the Big Data SIG should reflect the AI technology of today (which is more than Big Data only), but should not be too hyped at the same time. Rank the names in order of preference, 1= favourite

Chain AI
Nexus AI
Smart industry
Intelligent industry
AI.Link
nextgenAI
AI@NAP

What topics resonate for the revived Special Interest Group?

1. Foundations Context*

Ranking scale (1 = highest, 4 = lowest)

What AI actually is (and isn't) - cutting through the hype
Current state of AI in the process industry vs. other sectors
AI maturity models: where are NAP members on the curve?
Key AI technologies: machine learning, large language models, computer vision, agents

Comments about the topics and ranking 'Foundation Context'

2. AI in Core Operations (focus Asset Owners, but topics should be explored from a collaboration perspective between parties in the value chain)*

Ranking scale (1 = highest, 5 = lowest)

Predictive maintenance - condition monitoring, failure prediction, reducing unplanned downtime
Process optimization - real-time control loop optimization, yield improvement
Digital twins - AI-enhanced simulation of plants and equipment
Anomaly detection - safety, quality, and process deviations
Energy optimization — reducing consumption, carbon footprint

Comments about the topics and ranking 'AI in Core Operations'

3. AI in Engineering Project Execution (topics to be explored from a collaboration perspective between parties in the value chain)*

Ranking scale (1 = highest, 5 = lowest)

Document intelligence - reading P&IDs, data sheets, specifications automatically
AI-assisted design - generative design, automated checks and clash detection
Requirements management - extracting and linking requirements from large document sets
Schedule and cost forecasting - AI on historical project data
Handover automation - structuring and validating as-built documentation

Comments about the topics and ranking 'AI in Engineering Project Execution'

4. AI in Contract Supplier Management (topics to be explored from a collaboration perspective between parties in the value chain)*

Ranking scale (1 = highest, 4 = lowest)

Contract analysis - extracting obligations, risks, and deviations at scale
Spend analytics and procurement optimization
Supplier risk monitoring - continuous scoring using external data
Claims management - pattern recognition in disputes and change orders
Compliance monitoring - automated tracking against contract terms

Comments about the topics and ranking 'AI in Contract Supplier Management'

5. Collaboration Across the Value Chain*

Ranking scale (1 = highest, 5 = lowest)

Shared data models and interoperability (for example ISO 15926, OSDU)
AI as a translation layer - bridging terminology and formats between owner, EPC, and vendor
Knowledge retention - capturing expertise from aging workforce using AI
Multi-party document workflows - AI-assisted review, markup, and approval
Common operating picture - AI aggregating data across project stakeholders

Comments about the topics and ranking 'Collaboration Across the Value Chain'

6. AI Governance, Risk Trust*

Ranking scale (1 = highest, 5 = lowest)

Explainability - how to trust an AI recommendation in a safety-critical environment
Data quality and data ownership - garbage in, garbage out; who owns the model?
Liability and accountability - when AI is wrong, who is responsible in a contractor relationship?
Regulatory compliance - ATEX, functional safety (IEC 61511), and AI regulation (EU AI Act)
Cybersecurity - AI as both a threat vector and a defense tool

Comments about the topics and ranking 'AI Governance, Risk Trust'

7. Workforce Organizational Change*

Ranking scale (1 = highest, 4 = lowest)

Augmentation vs. automation - what changes for engineers, operators, and project managers?
Upskilling and AI literacy - across different roles
Change management - adoption challenges in conservative, safety-first cultures
New roles emerging - AI product owners, data stewards, prompt engineers

Comments about the topics and ranking 'Workforce Organizational Change'

8. Strategy Implementation*

Ranking scale (1 = highest, 5 = lowest)

Build vs. buy vs. partner AI solutions - especially relevant for smaller firms in the value chain
Where to start - high-value, low-risk use cases to build confidence
Vendor landscape - industry-specific AI platforms vs. horizontal tools
ROI measurement - how to quantify value of AI in engineering and operations
AI in standards bodies - how industry consortia (CFIHOS, AVEVA, Aspentech ...) are moving

Comments about the topics and ranking 'Strategy Implementation'

Data as a shared asset (Score 1=high, 2=medium, 3=low)*

You can add comments about the topics and ranking

Trust between parties (Score 1=high, 2=medium, 3=low)*

You can add comments about the topics and ranking

Legacy systems (Score 1=high, 2=medium, 3=low)*

You can add comments about the topics and ranking

Small dataset problem (Score 1=high, 2=medium, 3=low)*

You can add comments about the topics and ranking

SIG sessions

For whom should SIG sessions be organised?*

Directors (NAP members)
Professionals (from NAP membership firms)
Both / one audience
Both / separate sessions

Explanation of your answer to the question “For whom should SIG sessions be organised?”

How should the SIG sessions be organised?

Workshop (active participation)*

Please outline the main advantages (pros) and disadvantages (cons) as you perceive them

External speakers*

Please outline the main advantages (pros) and disadvantages (cons) as you perceive them

Internal speakers, use-cases NAP members / in combination with company visit*

Please outline the main advantages (pros) and disadvantages (cons) as you perceive them

Training (external facilitated)*

Please outline the main advantages (pros) and disadvantages (cons) as you perceive them

Studium Generale sessions per sub-topic, open to everyone*

Please outline the main advantages (pros) and disadvantages (cons) as you perceive them

Do you have other suggestions?

Questionnaire SIG Big Data - AI