ALICE Research Survey

AI in Logistics: Adoption, Value and Scale-up in Europe

Help us build a clearer picture of how artificial intelligence is being explored, adopted and scaled across European logistics and supply chains. Your contribution will support the update of the ALICE White Paper on AI in Logistics.

10–20 minutes Choose between a Short and Full version of the survey.

✓

No AI expertise required Organisations at every stage of AI adoption are welcome.

Anonymous analysis Responses will be analysed and reported in aggregate.

Before you begin

Please avoid confidential information and personal data in free-text responses. The questionnaire automatically adapts to your answers, so you will only see questions relevant to your organisation's experience and stage of AI adoption.

Step 1 of 12

AI in Logistics: Adoption, Value and Scale-up in Europe

ALICE — Alliance for Logistics Innovation through Collaboration in Europe — is collecting evidence to update its White Paper on AI in Logistics, with support and collaboration from the European Logistics Observatory (ELO). We welcome organisations at every adoption stage. The survey examines needs, barriers and concerns and, where applications have been tested or used, observed value and unintended effects. No AI expertise is required.

Short takes about 10 minutes. Full adds relevant detail and targets up to 20 minutes.

Responses will be analysed anonymously and reported in aggregate with disclosure checks. Please avoid confidential information and personal data in free-text responses. Organisation names and contact details are optional, except an email is needed if you request contact. Named case contributions require separate agreement.

1. Organisation profile

Describe the organisation or business unit you know best. Size and revenue bands support segmentation; no financial evaluation is intended. Names are optional and requested only for selected follow-up.

In which country is the organisation or business unit you are answering for primarily based?(Required)

If you selected Other country, please specify.

What is your organisation’s main role in Logistics and Supply Chain?(Required)

Companies

Logistics Nodes (Ports, Airports, Hubs, Terminals)

Local and regional authorities, cities, European, country governments and agencies

Academia, Research and Technology Centers

Associations, Foundations and Clusters

Civil society, NGOs

Other

Please specify your organisation’s role.(Required)

Energy providers, charging and hydrogen refuelling infrastructure and services

Goods manufacturers, retailers or wholesalers

Information and Communication Technologies (ICT), platforms and consultancy

Logistics Service Providers (3PLs–4PLs), freight forwarders, courier and postal operators

Maritime and Inland Waterways Transport

Multimodal, Intermodal and Combined Transport operators

Packaging, material handling and warehousing, transshipment, transport equipment and related tools and services

Rail Freight Transport

Real Estate and Investors

Road Freight Transport Carriers

Vehicles and transport modes OEMs

Infrastructure Manager

Other type of company

Please specify your organisation’s role.(Required)

Airports

Intermodal Terminals

Logistics Hubs

Ports

Warehouse

Other

Please specify your organisation’s role.(Required)

European Commission / EU Agencies

Funding Agency

Infrastructure Managers

Local authorities / Cities

Regional authorities

Research & Innovation Ministry

Transport ministry / agency

Other

Please specify your organisation’s role.(Required)

Research Institute & Technology Center

University / Business School

Other

Please specify your organisation’s role.(Required)

European / Global Industry Association

European R&I Association

European Technology Platform

Country Logistics Association / Cluster

Regional Logistics Cluster

Local authorities / Cities

Foundations and NGOs

Other

Please specify your organisation’s role in Logistics and Supply Chain.

What is your primary professional responsibility?(Required)

Executive / General Management

Logistics / Supply Chain / Operations

Digitalisation / IT / Data

Innovation / R&I

Strategy / Business Development

Engineering / Automation

Sustainability

Research / Academia

Policy / Public Administration

Other

Please specify Other (optional).

How many employees work in the organisation or business unit you are answering for?(Required)

Organisation size is used for segmentation. You may select Not sure or Prefer not to answer.

Fewer than 10

10–49

50–249

250–999

1,000–4,999

5,000 or more

Prefer not to answer

Not sure

What was the approximate revenue of the organisation or business unit in 2025?(Required)

Employees and revenue bands help us segment organisations and interpret responses; this is not a financial assessment. Results will be anonymised and reported in aggregate, with checks to reduce identification risk. Select Not applicable, Not sure or Prefer not to answer where appropriate.

Less than €10 million

€10–49 million

€50–249 million

€250 million–€1 billion

More than €1 billion

Prefer not to answer

Not applicable to this organisation

Not sure

Is your organisation currently part of either of the following networks?

ALICE only

European Logistics Observatory only

Both

Neither

Not sure

Optional section notes — is there anything you would like to clarify?

2. Short or Full version

Choose the level of detail you wish to provide. Full helps you explain relevant experience and needs more precisely; irrelevant technical questions are skipped.

Which survey format would you prefer?(Required)

Full gives you more room to explain your adoption of AI, needs and experience precisely. Additional questions appear only when relevant. These completion times are targets to confirm in a pilot.

Short — about 10 minutes

Full — up to 20 minutes

Optional section notes — is there anything you would like to clarify?

3. AI use and routing

We distinguish broad assistance from AI used for a specific logistics task. Generic-tool use is recorded separately. Current pilots, operational and discontinued applications lead to the experience route; no such experience leads to needs and plans.

General-purpose AI assistance means using tools such as ChatGPT, Claude or Copilot to draft emails, summarise text or prepare presentations. This can help daily work, but is recorded separately from AI applied to a specific logistics task.

AI applied to logistics addresses a defined task such as predicting arrival times, planning deliveries or extracting transport-document information. The task and how the output is used matter; automatic integration with another system is not required. Conventional automation alone does not necessarily use AI.

Please include pilots and experiences that were stopped. You do not need to be an AI specialist.

Which situation best describes your organisation’s experience with AI for a specific logistics or supply chain task?(Required)

Generic drafting, summarising and similar assistance alone does not count as a logistics application. Report your organisation’s own experience; client or partner experience can be identified separately in Full.

No pilot or actual use so far

Currently testing or piloting a logistics AI application

Using at least one logistics AI application in operations

Using logistics AI across multiple processes or sites

Tested or used logistics AI in the past, but it is no longer in use

Not sure

Does your organisation currently use general-purpose AI tools for broad work assistance?(Required)

Yes

Not sure

For which activities does your organisation currently use general-purpose Generative AI?

Select all applicable activities. These responses describe broad work assistance, not operational logistics adoption.

Select up to 5 choices.

Drafting documents or emails

Translation

Summarisation

Information search / knowledge assistance

Meeting preparation or summaries

Data analysis / spreadsheet support

Coding / technical assistance

Customer or supplier communication

Preparing reports or presentations

Supporting logistics decisions outside an integrated operational system

Other

Please specify Other (optional).

What benefits have you observed from this general-purpose assistance?

Less time spent drafting or summarising

Clearer documents or communication

Support for ideas, research or learning

No benefit observed yet

Not assessed / not sure

Other

Please specify the other benefit.

How is the use of company data in general-purpose AI tools governed?

Company data is not entered

Defined rules restrict which data may be used

Approved corporate tools and controls are used

Practices vary; no consistent rules

Not sure

Optional section notes — is there anything you would like to clarify?

4A. Needs and plans for logistics AI

This section concerns needs, interest and plans where logistics AI has not yet been tested or used. A concrete problem, curiosity and a plan to test are distinct; no positive expectation is assumed.

Which statement best describes your interest in applying AI to logistics?(Required)

No current interest or intention

Interested in exploring or learning, without a defined application

A concrete operational need exists, but no test is planned yet

We want or plan to test a specific application

Not sure

What motivates your interest?

Addressing an operational problem or need

Curiosity or learning

Developing organisational capabilities or strategy

Other

Not sure

Please specify Other (optional).

If useful, describe the need or application you would like to explore.

When does your organisation expect to start a pilot or implementation of AI specifically integrated into a Logistics or Supply Chain process?(Required)

This means moving beyond generic use of tools such as ChatGPT or Copilot towards AI applied to a defined logistics process.

Within 12 months

Within 1–2 years

Beyond 2 years

Interested, but no timeline has been defined

No current plans

Not sure

In which Logistics and Supply Chain areas could AI create the greatest value for your organisation?

Select up to five.

Select up to 5 choices.

Demand forecasting

Inventory and replenishment planning

Transport planning and routing

ETA prediction and visibility

Disruption prediction and management

Multimodal and synchromodal planning

Warehouse and intralogistics

Load building and consolidation

Fleet and asset management

Maintenance and asset monitoring

Port, terminal and hub operations

Yard management

Customer service, RFQ and order processing

Compliance and documentation

Energy and emissions optimisation

Safety and risk management

Robotics and autonomous operations

System interoperability and information exchange

Other

Please specify Other (optional).

Which benefits would you most expect from AI applied to Logistics and Supply Chain?

Select up to five.

Select up to 5 choices.

Lower operating costs

Faster decision-making

Better planning quality

Higher service reliability

Better asset utilisation

Higher capacity or throughput

Reduced empty kilometres

Lower energy consumption

Lower emissions

Better disruption management

Improved safety

Reduced administrative workload

Higher workforce productivity

Better customer service

New services or business models

Other

Please specify Other (optional).

What are the main barriers to adopting AI for logistics in your organisation?(Required)

Select up to three main barriers.

Select up to 3 choices.

Unclear business case or return on investment

High implementation cost

Lack of relevant data

Poor data quality

Lack of interoperability

Difficulty integrating legacy systems

Lack of internal AI skills

Lack of Logistics and Supply Chain expertise within AI projects

Difficulty selecting suitable AI systems or providers

Limited trust in AI outputs

Cybersecurity concerns

Confidentiality or data protection concerns

Regulatory uncertainty

Lack of management ownership

Organisational resistance or change-management challenges

Lack of suitable partners

Other

No barriers identified

Not sure

Please specify Other (optional).

Barrier rank 1 — most important

Choose among the barriers you selected above. Leave blank if fewer than two substantive barriers were selected.

Barrier rank 2

Choose a different selected barrier.

Barrier rank 3

Optional third priority.

What would most help your organisation move towards operational AI?

Select up to five.

Select up to 5 choices.

Clearer business cases and ROI evidence

Better access to relevant data

Better data quality

Better interoperability and standards

Practical implementation guidance

Access to tested AI solutions

More AI skills

Better combination of AI and logistics expertise

Better integration with existing IT systems

Access to trusted technology partners

Testbeds and demonstration projects

Financial support for first deployment

Regulatory and liability guidance

Other

Please specify Other (optional).

Optional section notes — is there anything you would like to clarify?

4B. Experience with logistics AI

Describe actual experience, including pilots and stopped applications. Separate observed value from expected benefits. Preliminary results and unsuccessful experiences are welcome.

In which Logistics and Supply Chain processes is your organisation currently piloting or using AI?(Required)

Select all that apply. No selection limit.

Demand forecasting

Inventory and replenishment planning

Transport planning and routing

ETA prediction and visibility

Disruption prediction and management

Multimodal and synchromodal planning

Warehouse and intralogistics

Load building and consolidation

Fleet and asset management

Maintenance and asset monitoring

Port, terminal and hub operations

Yard management

Customer service, RFQ and order processing

Compliance and documentation

Energy and emissions optimisation

Safety and risk management

Robotics and autonomous operations

System interoperability and information exchange

Other

Please specify Other (optional).

Which types of AI are currently being piloted or used?(Required)

Predictive AI: analyses data to predict events, outcomes or anomalies. Generative AI: generates content or extracts information, including through natural-language interaction. Agentic AI: executes a workflow towards a goal using data, tools or applications. Embodied AI: is embedded in physical systems such as robots or drones that sense and act in their environment. Categories may coexist. Select all that apply.

Predictive AI

Generative AI

Agentic AI

Embodied AI

Other

Not sure

Please specify the other AI type.

Overall, how have the results of your AI initiatives compared with initial expectations?(Required)

Below expectations

Mostly in line with expectations

Above expectations

Results vary significantly across projects

Too early to assess

No explicit expectations were defined

Not sure

Which types of value has your organisation already observed?(Required)

Select all that apply. No selection limit.

Lower operating costs

Faster decision-making

Better planning quality

Higher service reliability

Better asset utilisation

Higher capacity or throughput

Reduced empty kilometres

Lower energy consumption

Lower emissions

Better disruption management

Improved safety

Reduced administrative workload

Higher workforce productivity

Better customer service

New services or business models

Other

No measurable value yet

Please specify Other (optional).

What are the main barriers to implementing or scaling logistics AI?(Required)

Select up to three main barriers.

Select up to 3 choices.

Unclear business case or ROI

High implementation or operating cost

Insufficient data readiness

Lack of interoperability or common semantics

Legacy system integration

Lack of internal AI skills

Lack of Logistics and Supply Chain expertise in AI projects

Difficulty selecting suitable AI systems or providers

Difficulty moving from pilot to operational deployment

Insufficient reliability or accuracy

Limited trust in AI outputs

Lack of explainability

Cybersecurity concerns

Confidentiality or information leakage

Liability or accountability uncertainty

Regulatory uncertainty

Lack of standards

Organisational resistance or change-management challenges

Difficulty scaling across sites or partners

Lack of suitable ecosystem partners

Other

No barriers identified

Not sure

Please specify Other (optional).

Barrier rank 1 — most important

Choose among the barriers you selected above. Leave blank if fewer than two substantive barriers were selected.

Barrier rank 2

Choose a different selected barrier.

Barrier rank 3

Optional third priority.

What approach is your organisation generally taking to transforming processes when introducing AI?(Required)

Consider the general organisational approach. This question appears in both Short and Full.

Introducing AI without changing existing processes

Introducing AI with limited process changes

Introducing AI together with significant process changes

Redesigning processes around AI

It varies across applications

Too early to say

In which logistics or supply chain areas have you observed value from AI?

Select all that apply. No selection limit; exclude value that is only expected.

Demand forecasting

Inventory and replenishment planning

Transport planning and routing

ETA prediction and visibility

Disruption prediction and management

Multimodal and synchromodal planning

Warehouse and intralogistics

Load building and consolidation

Fleet and asset management

Maintenance and asset monitoring

Port, terminal and hub operations

Yard management

Customer service, RFQ and order processing

Compliance and documentation

Energy and emissions optimisation

Safety and risk management

Robotics and autonomous operations

System interoperability and information exchange

Other

No value observed yet

Not assessed / not sure

Please specify Other (optional).

Which unintended or negative effects have actually occurred during your logistics AI experience?

Report experienced effects, not potential fears. Preliminary and discontinued experiences are relevant.

Incorrect or unreliable outputs

Additional work or disruption to processes

Cybersecurity incident or unauthorised access

Data confidentiality or privacy problems

Reduced trust or excessive reliance on AI

No unintended effects observed

Not assessed

Not sure

Other

Please specify Other (optional).

If useful, explain an unintended effect and how it was addressed.

Optional section notes — is there anything you would like to clarify?

5. Reference application and operational evidence

Full only: choose one application you know directly. Use the same reference for sections 5–8. If you cannot describe one, skip the detailed modules. Quantitative evidence is optional; no confidential data is needed.

Can you describe a specific logistics AI application you know directly?(Required)

Yes

Whose application is this?(Required)

Our organisation’s application

A client or partner application I know directly

What is the stage of this reference application?(Required)

Pilot or test

Operational use

Stopped or discontinued

Which AI type best describes this reference application?(Required)

Predictive AI

Generative AI

Agentic AI

Embodied AI

Other

Not sure

In which logistics or supply chain area is this reference application mainly used?(Required)

Demand forecasting

Inventory and replenishment planning

Transport planning and routing

ETA prediction and visibility

Disruption prediction and management

Multimodal and synchromodal planning

Warehouse and intralogistics

Load building and consolidation

Fleet and asset management

Maintenance and asset monitoring

Port, terminal and hub operations

Yard management

Customer service, RFQ and order processing

Compliance and documentation

Energy and emissions optimisation

Safety and risk management

Robotics and autonomous operations

System interoperability and information exchange

Other

Please specify any Other AI type or application area.

What are the main factors preventing this reference application from becoming operational?

Select up to three.

Select up to 3 choices.

Pilot is still ongoing and evaluation is not complete

Insufficient evidence of operational value

Business case / ROI not demonstrated

Data availability or quality

Integration with operational systems

Reliability / accuracy

Governance or internal approval

User acceptance / change management

Cybersecurity

Regulation / liability

Technology or supplier limitations

Funding

Other

Please specify Other (optional).

What level of operational autonomy does the application currently have?

Provides information or analysis only

Provides recommendations; a human makes the decision

Proposes actions requiring human approval

Executes some actions automatically under human supervision

Executes defined decisions autonomously within specified operating limits

Not sure

If available, please provide one concrete KPI or example of the value generated.

Examples: planning time, forecast accuracy, cost, asset utilisation, throughput, empty kilometres, energy consumption, service level or administrative effort.

Optional section notes — is there anything you would like to clarify?

6. Data and interoperability

For the reference application, describe data sources and exchange with other organisations where relevant. We are interested in operational needs, access and interoperability, not technical expertise.

Which data sources does this AI application depend on?

ERP / transactional data

Transport Management System – TMS

Warehouse Management System – WMS

Terminal Operating System – TOS

Port Community System – PCS

Fleet, vehicle or telematics data

IoT, sensors, tracking devices or smart assets

Images / video

External partner data

Public / open data

Regulatory or freight documentation

Digital Twin data

Other

Not sure

Please specify Other (optional).

What are the main data-related barriers for this reference application?

Select up to three main items.

Select up to 3 choices.

Required data is unavailable

Data quality is insufficient

Data is fragmented across internal systems

Legacy systems or interfaces are difficult to integrate

Different meanings, identifiers or units

External partner data is difficult to access

Commercial sensitivity

Legal or contractual restrictions

Cybersecurity concerns

Unclear data ownership or governance

Data is not sufficiently timely

No major limitation

Other

Please specify Other (optional).

Does this AI application require data from other organisations?(Required)

Yes, mainly through bilateral exchange

Yes, directly from several ecosystem partners

Yes, mainly through a shared platform

Yes, mainly through a federated data space or trusted data-sharing environment

Not yet, but external data would improve the application

Not sure

For which operational purposes is external data needed?

Select up to three.

Select up to 3 choices.

End-to-end visibility or asset tracking

ETA prediction

Multimodal planning

Port-hinterland or terminal-rail coordination

Collaborative transport or capacity planning

Network resilience and disruption response

Compliance or documentation

Energy and emissions optimisation

Automation or equipment coordination

Other

Please specify Other (optional).

What is the main limitation in obtaining or using external data?

Data is unavailable

Data arrives too late

Different formats or interfaces

Different meanings, identifiers or units

Processes, events or responsibilities are not aligned

Unclear access or usage rights

Commercial sensitivity

Contractual restrictions

Lack of trust

Cybersecurity concerns

No major limitation

Other

Please specify Other (optional).

Optional section notes — is there anything you would like to clarify?

7. Digital Twins and automation

A Digital Twin is a digital representation of an asset, process or network used for monitoring, simulation or decision support. Automation concerns systems carrying out physical or operational tasks. Answer details only where relevant to the reference application.

Is this AI application combined, or planned to be combined, with a Digital Twin?(Required)

Yes, operationally

Yes, in pilot or development

Planned

Not sure

What does the Digital Twin represent?

Physical asset, vehicle or equipment

Warehouse

Terminal, port or logistics hub

Logistics process

Transport network or corridor

Supply chain network

Other

Please specify Other (optional).

For which purposes is the Digital Twin used or planned?

Select all purposes that apply. No selection limit.

Monitoring current conditions

Prediction

Scenario simulation / what-if analysis

Planning or optimisation

Disruption management

Asset / maintenance management

Energy or emissions optimisation

Automated decision-making

Other

Please specify Other (optional).

Is this AI application connected, or planned to be connected, to operational or physical automation?(Required)

Yes, operationally

Yes, in pilot or development

Planned

Not sure

Which form of automation is involved or planned?

Automated planning or scheduling

Automated document or workflow processing

Warehouse robotics or handling equipment

Yard or terminal automation

Autonomous vehicles or mobile equipment

Drones

Autonomous or remotely operated vessels

Automated maintenance

Human-machine collaboration

Other

Please specify Other (optional).

How are automated actions supervised, or intended to be supervised?

A human approves every operational action

A human supervises and can intervene

Humans handle defined exceptions

Actions run autonomously within predefined operating conditions and escalation rules

Oversight arrangements have not yet been defined

Not sure

Optional section notes — is there anything you would like to clarify?

8. Governance and risks

For the same reference application, describe how risks and responsibilities are managed. Potential concerns and actual incidents are distinct. Answers do not assume that controls exist or that incidents have occurred.

What are the main risks for this reference application?

Select up to three main items.

Select up to 3 choices.

Incorrect or unreliable outputs

Confidential information leakage

Cybersecurity attacks

Lack of explainability

Loss of human control

Unclear accountability

Regulatory non-compliance

Bias or inappropriate decisions

Dependency on external providers

Loss of internal capabilities or know-how

Operational disruption or safety consequences

No significant concern identified

Other

Not sure

Please specify Other (optional).

Risk rank 1 — most important

Choose among the risks you selected above. Leave blank if fewer than two substantive risks were selected.

Risk rank 2

Choose a different selected risk.

Risk rank 3

Optional third priority.

Which measures does this reference application currently apply to manage AI-related risks?

Approved AI tools and acceptable-use rules

Restrictions on confidential or personal data

Vendor, contract and security assessment

Access controls

Output verification and human approval

Testing for reliability, bias or explainability

Audit logs and named accountable owners

Staff training and practical guidance

Incident response and fallback procedures

Monitoring and periodic reassessment

Legal / compliance review

No structured measures yet

Other

Please specify Other (optional).

Optional section notes — is there anything you would like to clarify?

9. Common scale-up priorities

Everyone answers this section. Consider priorities for your own stage: exploring, adopting, implementing or scaling. Concerns are recorded separately from experienced effects.

What are the main priorities that would support adopting, implementing or scaling AI in logistics?(Required)

Consider your organisation’s current stage, including needs and plans. Select up to five.

Select up to 5 choices.

Better evidence of operational value and comparable benchmarks

Better data quality and access

Common semantics, identifiers and standards

Better integration with existing logistics systems

Trusted mechanisms for cross-company data sharing

More AI and logistics skills

Process redesign and change-management support

Reliable and explainable AI with appropriate human oversight

Cybersecurity and operational resilience

Regulatory, contractual and liability guidance

Testbeds and demonstration projects

Funding for first deployment

Support for replication across sites and partners

Better access to suitable technology providers

Other

No additional priority identified

Not sure

Please specify Other (optional).

Priority rank 1 — most important

Choose among the priorities you selected above. Leave blank if fewer than two substantive priorities were selected.

Priority rank 2

Choose a different selected priority.

Priority rank 3

Optional third priority.

Which concerns, if any, affect your willingness to adopt or expand AI?(Required)

Concerns do not imply that an incident has occurred.

Reliability of outputs

Cybersecurity

Confidentiality or misuse of data

Loss of control or unclear responsibility

Skills and ability to oversee AI

No particular concerns

Not sure

Other

Please specify Other (optional).

Optional section notes — is there anything you would like to clarify?

10. Optional use case contribution

Contributing a case is optional. You may indicate interest without providing a description. Named publication or sharing requires separate agreement. You can also submit a use case at any time through the ALICE Use Cases & Proof of Concepts initiative. This optional survey section complements that ongoing initiative.

Does your organisation have an AI-related logistics use case or Proof of Concept that could contribute to this White Paper?(Required)

Yes, and it has already been submitted to ALICE

Yes, and we may submit it

Yes, but internal approval would be required

Possibly

Not sure

What is its current maturity?

Concept / planned application

Pilot / Proof of Concept

Operational deployment

Scaled deployment

Discontinued / unsuccessful initiative

Not sure

Please briefly describe the use case.

Where possible, include the logistics problem, AI application, maturity, main value or expected value, and main barrier or lesson learned.

Optional section notes — is there anything you would like to clarify?

11. Follow-up and final comment

You may request follow-up and leave a final comment. Contact details are collected only if you choose contact; naming willingness is distinct from survey analysis.

Would you be willing to participate in a short follow-up interview about AI adoption in Logistics and Supply Chain?

Yes

Possibly, please send me more information

Is there anything else you would like to add about AI adoption, operational deployment or scale-up in Logistics and Supply Chain?

Would you be willing for your organisation to be named in a separately agreed case contribution?

This records willingness only. Any named contribution requires separate agreement; survey analysis is reported in aggregate.

Yes — contact me to discuss

No — keep contributions anonymous

Not sure

May ALICE contact you about your selected follow-up or case contribution?

Yes

Email address for the follow-up you requested.(Required)

Name (optional).

Organisation name (optional).

Optional section notes — is there anything you would like to clarify?

AI in Logistics: Adoption, Value and Scale-up in Europe

1. Organisation profile

2. Short or Full version

3. AI use and routing

4A. Needs and plans for logistics AI

4B. Experience with logistics AI

5. Reference application and operational evidence

6. Data and interoperability

7. Digital Twins and automation

8. Governance and risks

9. Common scale-up priorities

10. Optional use case contribution

11. Follow-up and final comment

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