EPFL_Survey_Qualitative_Coding.csv

Respondent_ID,Industry,Q,Original,Code,Type,RQ
1,Education / Research,Q5,Cost,Cost & TCO,Lever,RQ1
1,Education / Research,8,"No AI in use, so no motivation yet.",No keyword match,NA,NA
1,Education / Research,9,None yet.,No keyword match,NA,NA
1,Education / Research,10,"Lack of in-house AI expertise, limited budget.",No keyword match,NA,NA
1,Education / Research,11,nan,No keyword match,NA,NA
1,Education / Research,12,"Would weigh cost, ease of integration, and student data privacy against performance and vendor reliability.",Data privacy & control,Gate,RQ2
1,Education / Research,12,"Would weigh cost, ease of integration, and student data privacy against performance and vendor reliability.",Cost & TCO,Lever,RQ3
1,Education / Research,12,"Would weigh cost, ease of integration, and student data privacy against performance and vendor reliability.",Performance sufficiency,Lever,RQ3
2,Education / Research,Q5,Customization,Customization & PEFT,Lever,RQ1
2,Education / Research,8,"Open-source is chosen for flexibility, transparency, and fostering innovation in the academic environment.
Proprietary solutions are used where specialized capabilities or vendor support are necessary.",Support & SLAs,Lever,RQ3
2,Education / Research,9,"Hugging Face models have been utilized in projects involving natural language processing courses, student research on AI ethics, and prototype development of AI-assisted tools in education.",No keyword match,NA,NA
2,Education / Research,10,"Technical difficulties in integrating diverse open-source tools into educational platforms.
Ensuring alignment with institutional data protection and compliance policies.",Compliance,Gate,RQ2
2,Education / Research,11,Specialized documentation and tutorials for academic use cases.,Documentation completeness,Gate,RQ2
2,Education / Research,12,"Evaluation weighs the educational value, flexibility, and community support of open-source against the reliability and ease of use offered by proprietary solutions.",Support & SLAs,Lever,RQ3
3,Education / Research,Q5,Performance,Performance sufficiency,Lever,RQ1
3,Education / Research,8,"Open-source offers flexibility, cost savings, and freedom from vendor lock-in, making it attractive for experimentation. Proprietary tools are often faster to deploy, have stronger support, and require less maintenance, which matters for our small team. The decision will depend on how well open-source can meet our needs without creating operational bottlenecks.",Cost & TCO,Lever,RQ3
3,Education / Research,8,"Open-source offers flexibility, cost savings, and freedom from vendor lock-in, making it attractive for experimentation. Proprietary tools are often faster to deploy, have stronger support, and require less maintenance, which matters for our small team. The decision will depend on how well open-source can meet our needs without creating operational bottlenecks.",Support & SLAs,Lever,RQ3
3,Education / Research,8,"Open-source offers flexibility, cost savings, and freedom from vendor lock-in, making it attractive for experimentation. Proprietary tools are often faster to deploy, have stronger support, and require less maintenance, which matters for our small team. The decision will depend on how well open-source can meet our needs without creating operational bottlenecks.",Deployment architecture,Lever,RQ3
3,Education / Research,9,"Experimental use of Hugging Face NLP models to summarize cohort session transcripts. While promising, we need to assess whether we can support this at scale.",Support & SLAs,Lever,RQ3
3,Education / Research,10,"Ongoing maintenance and updates to models, content accuracy and avoiding bias in learning materials.",No keyword match,NA,NA
3,Education / Research,11,Step-by-step guides for SaaS integration and deployment.,Deployment architecture,Lever,RQ3
3,Education / Research,12,"We weigh the flexibility, cost-effectiveness, and innovation potential of open-source AI against the stability, vendor support, and ease-of-deployment of proprietary solutions.",Cost & TCO,Lever,RQ3
3,Education / Research,12,"We weigh the flexibility, cost-effectiveness, and innovation potential of open-source AI against the stability, vendor support, and ease-of-deployment of proprietary solutions.",Support & SLAs,Lever,RQ3
3,Education / Research,12,"We weigh the flexibility, cost-effectiveness, and innovation potential of open-source AI against the stability, vendor support, and ease-of-deployment of proprietary solutions.",Deployment architecture,Lever,RQ3
4,Education / Research,Q5,Ability to improve learner engagement and outcomes.  Ease of integration with minimal engineering overhead.  Affordability and scalability for a startup.  Compliance with educational data privacy standards.  Reliability and vendor support.,Compliance,Gate,RQ1
4,Education / Research,8,"Open-source AI is valued for experimentation, and avoiding vendor lock-in.
Proprietary AI is preferred for speed, reliability, and ease of deployment, especially given the small team size.",Deployment architecture,Lever,RQ3
4,Education / Research,9,Piloting Hugging Face NLP models for automated summarization of cohort sessions and personalized course recommendations.,No keyword match,NA,NA
4,Education / Research,10,"Limited internal AI engineering resources.
Ongoing maintenance and updates for models.
Ensuring AI-generated content is accurate, unbiased, and appropriate.
Integrating AI outputs without disrupting user experience.",No keyword match,NA,NA
4,Education / Research,11,More education-specific AI templates,No keyword match,NA,NA
4,Education / Research,12,"Balances innovation, cost-effectiveness, and flexibility of open-source AI against the stability, support, and ease of use offered by proprietary solutions.",Cost & TCO,Lever,RQ3
4,Education / Research,12,"Balances innovation, cost-effectiveness, and flexibility of open-source AI against the stability, support, and ease of use offered by proprietary solutions.",Support & SLAs,Lever,RQ3
5,Education / Research,Q5,Cost,Cost & TCO,Lever,RQ1
5,Education / Research,8,"We prioritise transparency, reproducibility, and cost control. Open source AI let us fine tune for our domains and keep research data on institutional infrastructure.",Cost & TCO,Lever,RQ3
5,Education / Research,9,Literature triage and summarisation for researchers using HF models with a small institutional knowledge base. It reduced screening time and helped surface relevant papers.,No keyword match,NA,NA
5,Education / Research,10,"Budget for compute, standardising data governance across labs, and keeping evaluation practices consistent.",Governance readiness,Gate,RQ2
5,Education / Research,11,Improved model cards.,No keyword match,NA,NA
5,Education / Research,12,Cost for research workflows.,Cost & TCO,Lever,RQ3
6,Education / Research,Q5,"Cost, Transparency, Collaboration",Cost & TCO,Lever,RQ1
6,Education / Research,8,Accessibility and community,No keyword match,NA,NA
6,Education / Research,9,Course material summarisation,No keyword match,NA,NA
6,Education / Research,10,Infrastructure limits,No keyword match,NA,NA
6,Education / Research,11,Institutional SLAs,No keyword match,NA,NA
6,Education / Research,12,Openness > Proprietary cost,Cost & TCO,Lever,RQ3
7,Education / Research,Q5,"Transparency, Cost, Ecosystem",Cost & TCO,Lever,RQ1
7,Education / Research,8,Reproducibility,No keyword match,NA,NA
7,Education / Research,9,Research assistant chatbot,No keyword match,NA,NA
7,Education / Research,10,Compute resources,No keyword match,NA,NA
7,Education / Research,11,Academic deployment credits,Deployment architecture,Lever,RQ3
7,Education / Research,12,"Open first, cost later",Cost & TCO,Lever,RQ3
8,Education / Research,Q5,"Cost, Learning Value",Cost & TCO,Lever,RQ1
8,Education / Research,8,Freedom to experiment,No keyword match,NA,NA
8,Education / Research,9,Student-facing tutoring bot,No keyword match,NA,NA
8,Education / Research,10,Lack of skilled ops staff,No keyword match,NA,NA
8,Education / Research,11,Step-by-step deployment tutorials,Deployment architecture,Lever,RQ3
8,Education / Research,12,Feasibility → Learning gain,No keyword match,NA,NA
9,Energy / Utilities / Oil & Gas,Q5,Data control / privacy,Data privacy & control,Gate,RQ1
9,Energy / Utilities / Oil & Gas,8,"Managed services with SLAs and EU-region hosting simplified GDPR and NIS2 compliance and accelerated go-live, while we keep sensitive OT and customer data in a private cloud.",Compliance,Gate,RQ2
9,Energy / Utilities / Oil & Gas,8,"Managed services with SLAs and EU-region hosting simplified GDPR and NIS2 compliance and accelerated go-live, while we keep sensitive OT and customer data in a private cloud.",Customization & PEFT,Lever,RQ3
9,Energy / Utilities / Oil & Gas,9,"A pilot RAG system using HF embeddings and a local vector index over safety manuals and maintenance SOPs cut lookup time for field engineers, with all content kept inside our network.",No keyword match,NA,NA
9,Energy / Utilities / Oil & Gas,10,"The main issues are organisational and legal, technical friction was low in pilots.",No keyword match,NA,NA
9,Energy / Utilities / Oil & Gas,11,/,No keyword match,NA,NA
9,Energy / Utilities / Oil & Gas,12,"Data residency first. If a workload can stay in our VPC with clear auditability we consider open components; otherwise we prefer proprietary services with SLAs, then compare performance and TCO",Performance sufficiency,Lever,RQ3
10,Energy / Utilities / Oil & Gas,Q5,Data control / privacy,Data privacy & control,Gate,RQ1
10,Energy / Utilities / Oil & Gas,8,Better performance and transparency,Performance sufficiency,Lever,RQ3
10,Energy / Utilities / Oil & Gas,9,"Distributed optimization, optimization solvers",No keyword match,NA,NA
10,Energy / Utilities / Oil & Gas,10,"Concerns about support, legal in terms of licenses or data privacy.",Licensing clarity,Gate,RQ2
10,Energy / Utilities / Oil & Gas,10,"Concerns about support, legal in terms of licenses or data privacy.",Data privacy & control,Gate,RQ2
10,Energy / Utilities / Oil & Gas,10,"Concerns about support, legal in terms of licenses or data privacy.",Support & SLAs,Lever,RQ3
10,Energy / Utilities / Oil & Gas,11,It’s already good and easy to use models. Nothing to add.,No keyword match,NA,NA
10,Energy / Utilities / Oil & Gas,12,"Data privacy, quality/performance and concerns about longer term support play more important role than costs.",Data privacy & control,Gate,RQ2
10,Energy / Utilities / Oil & Gas,12,"Data privacy, quality/performance and concerns about longer term support play more important role than costs.",Cost & TCO,Lever,RQ3
10,Energy / Utilities / Oil & Gas,12,"Data privacy, quality/performance and concerns about longer term support play more important role than costs.",Performance sufficiency,Lever,RQ3
10,Energy / Utilities / Oil & Gas,12,"Data privacy, quality/performance and concerns about longer term support play more important role than costs.",Support & SLAs,Lever,RQ3
11,Energy / Utilities / Oil & Gas,Q5,Data control / privacy,Data privacy & control,Gate,RQ1
11,Energy / Utilities / Oil & Gas,8,"Open-source: flexibility, control over models, ability to deploy on-prem for sensitive data, cost efficiency at scale.
Proprietary: faster deployment for certain NLP and summarisation tasks, vendor support, cutting-edge capabilities.",Cost & TCO,Lever,RQ3
11,Energy / Utilities / Oil & Gas,8,"Open-source: flexibility, control over models, ability to deploy on-prem for sensitive data, cost efficiency at scale.
Proprietary: faster deployment for certain NLP and summarisation tasks, vendor support, cutting-edge capabilities.",Support & SLAs,Lever,RQ3
11,Energy / Utilities / Oil & Gas,8,"Open-source: flexibility, control over models, ability to deploy on-prem for sensitive data, cost efficiency at scale.
Proprietary: faster deployment for certain NLP and summarisation tasks, vendor support, cutting-edge capabilities.",Deployment architecture,Lever,RQ3
11,Energy / Utilities / Oil & Gas,9,nan,No keyword match,NA,NA
11,Energy / Utilities / Oil & Gas,10,"Technical: aligning open-source models with our internal cybersecurity and compliance frameworks.
Organisational: upskilling engineers to work with model training pipelines.
Legal: ensuring license compliance for commercial deployment.",Compliance,Gate,RQ2
11,Energy / Utilities / Oil & Gas,10,"Technical: aligning open-source models with our internal cybersecurity and compliance frameworks.
Organisational: upskilling engineers to work with model training pipelines.
Legal: ensuring license compliance for commercial deployment.",Licensing clarity,Gate,RQ2
11,Energy / Utilities / Oil & Gas,10,"Technical: aligning open-source models with our internal cybersecurity and compliance frameworks.
Organisational: upskilling engineers to work with model training pipelines.
Legal: ensuring license compliance for commercial deployment.",Security posture,Gate,RQ2
11,Energy / Utilities / Oil & Gas,10,"Technical: aligning open-source models with our internal cybersecurity and compliance frameworks.
Organisational: upskilling engineers to work with model training pipelines.
Legal: ensuring license compliance for commercial deployment.",Deployment architecture,Lever,RQ3
11,Energy / Utilities / Oil & Gas,11,Pre-built domain-specific models.,No keyword match,NA,NA
11,Energy / Utilities / Oil & Gas,12,"Total cost of ownership, data sensitivity, performance benchmarks, and long-term vendor lock-in risk.",Cost & TCO,Lever,RQ3
11,Energy / Utilities / Oil & Gas,12,"Total cost of ownership, data sensitivity, performance benchmarks, and long-term vendor lock-in risk.",Performance sufficiency,Lever,RQ3
12,Energy / Utilities / Oil & Gas,Q5,Data control / privacy,Data privacy & control,Gate,RQ1
12,Energy / Utilities / Oil & Gas,8,"Open-source: Flexibility to customize solutions to specific energy utility needs, transparency of models and code, lower total cost of ownership, rapid innovation from community contributions, and faster deployment cycles.
Proprietary: Access to enterprise-grade support, validated compliance certifications, specialized advanced AI capabilities, and service-level agreements (SLAs) for mission-critical requirements.",Compliance,Gate,RQ2
12,Energy / Utilities / Oil & Gas,8,"Open-source: Flexibility to customize solutions to specific energy utility needs, transparency of models and code, lower total cost of ownership, rapid innovation from community contributions, and faster deployment cycles.
Proprietary: Access to enterprise-grade support, validated compliance certifications, specialized advanced AI capabilities, and service-level agreements (SLAs) for mission-critical requirements.",Cost & TCO,Lever,RQ3
12,Energy / Utilities / Oil & Gas,8,"Open-source: Flexibility to customize solutions to specific energy utility needs, transparency of models and code, lower total cost of ownership, rapid innovation from community contributions, and faster deployment cycles.
Proprietary: Access to enterprise-grade support, validated compliance certifications, specialized advanced AI capabilities, and service-level agreements (SLAs) for mission-critical requirements.",Support & SLAs,Lever,RQ3
12,Energy / Utilities / Oil & Gas,8,"Open-source: Flexibility to customize solutions to specific energy utility needs, transparency of models and code, lower total cost of ownership, rapid innovation from community contributions, and faster deployment cycles.
Proprietary: Access to enterprise-grade support, validated compliance certifications, specialized advanced AI capabilities, and service-level agreements (SLAs) for mission-critical requirements.",Customization & PEFT,Lever,RQ3
12,Energy / Utilities / Oil & Gas,8,"Open-source: Flexibility to customize solutions to specific energy utility needs, transparency of models and code, lower total cost of ownership, rapid innovation from community contributions, and faster deployment cycles.
Proprietary: Access to enterprise-grade support, validated compliance certifications, specialized advanced AI capabilities, and service-level agreements (SLAs) for mission-critical requirements.",Deployment architecture,Lever,RQ3
12,Energy / Utilities / Oil & Gas,9,"We used HF for time-series anomaly detection in utility billing data. This application reduced manual billing reconciliation workload by approximately 50%, improving operational efficiency and customer satisfaction.",Customization & PEFT,Lever,RQ3
12,Energy / Utilities / Oil & Gas,10,"Technical: Integration complexity with legacy energy ERP systems; customization of general AI models to handle large-scale, high-frequency time-series billing data.
Organisational: Building in-house expertise for managing open-source pipelines, keeping up with rapid community updates, and aligning multidisciplinary teams.
Legal: Compliance with diverse international energy regulations and privacy laws, ensuring explainability and auditability of AI decisions consistent with regulatory requirements.",Compliance,Gate,RQ2
12,Energy / Utilities / Oil & Gas,10,"Technical: Integration complexity with legacy energy ERP systems; customization of general AI models to handle large-scale, high-frequency time-series billing data.
Organisational: Building in-house expertise for managing open-source pipelines, keeping up with rapid community updates, and aligning multidisciplinary teams.
Legal: Compliance with diverse international energy regulations and privacy laws, ensuring explainability and auditability of AI decisions consistent with regulatory requirements.",Data privacy & control,Gate,RQ2
12,Energy / Utilities / Oil & Gas,10,"Technical: Integration complexity with legacy energy ERP systems; customization of general AI models to handle large-scale, high-frequency time-series billing data.
Organisational: Building in-house expertise for managing open-source pipelines, keeping up with rapid community updates, and aligning multidisciplinary teams.
Legal: Compliance with diverse international energy regulations and privacy laws, ensuring explainability and auditability of AI decisions consistent with regulatory requirements.",Customization & PEFT,Lever,RQ3
12,Energy / Utilities / Oil & Gas,11,Account management.,No keyword match,NA,NA
12,Energy / Utilities / Oil & Gas,12,"The evaluation balances regulatory compliance, customization flexibility, total cost of ownership, performance, vendor support, and data control.",Compliance,Gate,RQ2
12,Energy / Utilities / Oil & Gas,12,"The evaluation balances regulatory compliance, customization flexibility, total cost of ownership, performance, vendor support, and data control.",Cost & TCO,Lever,RQ3
12,Energy / Utilities / Oil & Gas,12,"The evaluation balances regulatory compliance, customization flexibility, total cost of ownership, performance, vendor support, and data control.",Performance sufficiency,Lever,RQ3
12,Energy / Utilities / Oil & Gas,12,"The evaluation balances regulatory compliance, customization flexibility, total cost of ownership, performance, vendor support, and data control.",Support & SLAs,Lever,RQ3
12,Energy / Utilities / Oil & Gas,12,"The evaluation balances regulatory compliance, customization flexibility, total cost of ownership, performance, vendor support, and data control.",Customization & PEFT,Lever,RQ3
13,Energy / Utilities / Oil & Gas,Q5,Regulatory compliance,Compliance,Gate,RQ1
13,Energy / Utilities / Oil & Gas,8,Proprietary AI solutions are preferred for mission-critical applications requiring guaranteed support and compliance.,Compliance,Gate,RQ2
13,Energy / Utilities / Oil & Gas,8,Proprietary AI solutions are preferred for mission-critical applications requiring guaranteed support and compliance.,Support & SLAs,Lever,RQ3
13,Energy / Utilities / Oil & Gas,9,Possible pilot projects leveraging open-source NLP or predictive models for customer service automation.,Customization & PEFT,Lever,RQ3
13,Energy / Utilities / Oil & Gas,10,"Integration complexities with specialized energy and building systems.
Adherence to strict regulatory and security standards.
Internal expertise limitations for maintaining and scaling AI models.
Risk management and operational continuity in critical infrastructure.",Security posture,Gate,RQ2
13,Energy / Utilities / Oil & Gas,11,"Domain-specific industrial connectors, which can be addressed via customized integration.",Customization & PEFT,Lever,RQ3
13,Energy / Utilities / Oil & Gas,12,"Innovation potential and cost advantages of open-source AI against the need for reliability, compliance, and vendor support provided by proprietary solutions.",Compliance,Gate,RQ2
13,Energy / Utilities / Oil & Gas,12,"Innovation potential and cost advantages of open-source AI against the need for reliability, compliance, and vendor support provided by proprietary solutions.",Cost & TCO,Lever,RQ3
13,Energy / Utilities / Oil & Gas,12,"Innovation potential and cost advantages of open-source AI against the need for reliability, compliance, and vendor support provided by proprietary solutions.",Support & SLAs,Lever,RQ3
14,Energy / Utilities / Oil & Gas,Q5,"Compliance, Performance, Support",Compliance,Gate,RQ1
14,Energy / Utilities / Oil & Gas,8,Reliability and regulation fit,No keyword match,NA,NA
14,Energy / Utilities / Oil & Gas,9,Exploring internal anomaly detection,No keyword match,NA,NA
14,Energy / Utilities / Oil & Gas,10,Long procurement cycles,No keyword match,NA,NA
14,Energy / Utilities / Oil & Gas,11,Industry-specific model registry,No keyword match,NA,NA
14,Energy / Utilities / Oil & Gas,12,Compliance > Cost,Compliance,Gate,RQ2
14,Energy / Utilities / Oil & Gas,12,Compliance > Cost,Cost & TCO,Lever,RQ3
15,Energy / Utilities / Oil & Gas,Q5,"Performance, SLAs",Performance sufficiency,Lever,RQ1
15,Energy / Utilities / Oil & Gas,8,Reliability and integration,No keyword match,NA,NA
15,Energy / Utilities / Oil & Gas,9,Predictive maintenance POC,No keyword match,NA,NA
15,Energy / Utilities / Oil & Gas,10,Access to open datasets,No keyword match,NA,NA
15,Energy / Utilities / Oil & Gas,11,Deployment templates,Deployment architecture,Lever,RQ3
15,Energy / Utilities / Oil & Gas,12,SLA before Openness,No keyword match,NA,NA
16,Energy / Utilities / Oil & Gas,8,"Risk mitigation, vendor accountability",No keyword match,NA,NA
16,Energy / Utilities / Oil & Gas,9,Pilot for emissions monitoring,Observability & monitoring,Lever,RQ3
16,Energy / Utilities / Oil & Gas,10,Legal review,No keyword match,NA,NA
16,Energy / Utilities / Oil & Gas,11,Role-based access guides,No keyword match,NA,NA
16,Energy / Utilities / Oil & Gas,12,Security → SLA,Security posture,Gate,RQ2
17,Energy / Utilities / Oil & Gas,Q5,"Cost, Performance",Cost & TCO,Lever,RQ1
17,Energy / Utilities / Oil & Gas,8,Reduce vendor lock-in risk,No keyword match,NA,NA
17,Energy / Utilities / Oil & Gas,9,Evaluating model-based forecasting,No keyword match,NA,NA
17,Energy / Utilities / Oil & Gas,10,Internal compliance gates,Compliance,Gate,RQ2
17,Energy / Utilities / Oil & Gas,11,HF enterprise validation tools,No keyword match,NA,NA
17,Energy / Utilities / Oil & Gas,12,SLA vs Transparency,No keyword match,NA,NA
18,Energy / Utilities / Oil & Gas,Q5,"Performance, Cost",Cost & TCO,Lever,RQ1
18,Energy / Utilities / Oil & Gas,8,Service reliability,No keyword match,NA,NA
18,Energy / Utilities / Oil & Gas,9,Document processing prototype,Documentation completeness,Gate,RQ2
18,Energy / Utilities / Oil & Gas,10,Integration hurdles,No keyword match,NA,NA
18,Energy / Utilities / Oil & Gas,11,Compliance mapping,Compliance,Gate,RQ2
18,Energy / Utilities / Oil & Gas,12,SLA > Cost,Cost & TCO,Lever,RQ3
19,Finance / Banking / Insurance,Q5,Regulatory compliance,Compliance,Gate,RQ1
19,Finance / Banking / Insurance,8,Price and complexity to set up.,No keyword match,NA,NA
19,Finance / Banking / Insurance,9,Quick prototyping. We have not done production facing things yet with hugging face models.,No keyword match,NA,NA
19,Finance / Banking / Insurance,10,"All of the above. Lack of knowledge, compliance, skepticism, resistance to change.",Compliance,Gate,RQ2
19,Finance / Banking / Insurance,11,On premise installations.,No keyword match,NA,NA
19,Finance / Banking / Insurance,12,The support of the vendor and compliance to swiss privacy needs are important. Turnkey solutions are valued.,Compliance,Gate,RQ2
19,Finance / Banking / Insurance,12,The support of the vendor and compliance to swiss privacy needs are important. Turnkey solutions are valued.,Data privacy & control,Gate,RQ2
19,Finance / Banking / Insurance,12,The support of the vendor and compliance to swiss privacy needs are important. Turnkey solutions are valued.,Support & SLAs,Lever,RQ3
20,Finance / Banking / Insurance,Q5,Regulatory compliance,Compliance,Gate,RQ1
20,Finance / Banking / Insurance,8,increase productivity,No keyword match,NA,NA
20,Finance / Banking / Insurance,9,we have SyzGpt to assite all administrative tasks and AI intelligence monitoring,Observability & monitoring,Lever,RQ3
20,Finance / Banking / Insurance,10,banking secrecy,No keyword match,NA,NA
20,Finance / Banking / Insurance,11,training and workshop,No keyword match,NA,NA
20,Finance / Banking / Insurance,12,prioritize proprietary options,No keyword match,NA,NA
21,Finance / Banking / Insurance,Q5,Risk management,Security posture,Gate,RQ1
21,Finance / Banking / Insurance,8,No vendor lock-in and a hedge against sudden pricing or policy shifts.,No keyword match,NA,NA
21,Finance / Banking / Insurance,9,"A fine-tuned, open-source LLM running on our secure cluster now drafts compliance reports by summarising new regulations, cutting analyst time ≈ 45 %.",Compliance,Gate,RQ2
21,Finance / Banking / Insurance,10,"- Regulatory compliance (EU AI Act, Basel guidance). 
- Security & patch cadence for rapidly evolving models.
- Model governance (lineage, bias testing, audit trails).
- Change-management friction inside legacy risk processes.",Compliance,Gate,RQ2
21,Finance / Banking / Insurance,10,"- Regulatory compliance (EU AI Act, Basel guidance). 
- Security & patch cadence for rapidly evolving models.
- Model governance (lineage, bias testing, audit trails).
- Change-management friction inside legacy risk processes.",Security posture,Gate,RQ2
21,Finance / Banking / Insurance,10,"- Regulatory compliance (EU AI Act, Basel guidance). 
- Security & patch cadence for rapidly evolving models.
- Model governance (lineage, bias testing, audit trails).
- Change-management friction inside legacy risk processes.",Governance readiness,Gate,RQ2
21,Finance / Banking / Insurance,11,Pre-certified model cards mapped to banking risk taxonomies. Integrated guardrail & red-teaming toolkit.,No keyword match,NA,NA
21,Finance / Banking / Insurance,12,"We score each option across risk, speed, cost, performance, talent fit, and lock-in.
If a single strategic partner can meet the risk/speed bar under a tight legal framework, we stay focused to maximise velocity. Otherwise, we diversify or use an LLM gateway to abstract providers, so we keep optionality while containing contractual complexity.",Cost & TCO,Lever,RQ3
21,Finance / Banking / Insurance,12,"We score each option across risk, speed, cost, performance, talent fit, and lock-in.
If a single strategic partner can meet the risk/speed bar under a tight legal framework, we stay focused to maximise velocity. Otherwise, we diversify or use an LLM gateway to abstract providers, so we keep optionality while containing contractual complexity.",Performance sufficiency,Lever,RQ3
22,Finance / Banking / Insurance,Q5,"Productivity enhancement, regulatory compliance, security, and ability to improve hybrid client experience.",Compliance,Gate,RQ1
22,Finance / Banking / Insurance,8,"We chose proprietary AI solutions, particularly Microsoft Azure Open AI Service, motivated by the need for regulatory compliance, enterprise-grade support, security, and productivity gains.",Compliance,Gate,RQ2
22,Finance / Banking / Insurance,8,"We chose proprietary AI solutions, particularly Microsoft Azure Open AI Service, motivated by the need for regulatory compliance, enterprise-grade support, security, and productivity gains.",Security posture,Gate,RQ2
22,Finance / Banking / Insurance,8,"We chose proprietary AI solutions, particularly Microsoft Azure Open AI Service, motivated by the need for regulatory compliance, enterprise-grade support, security, and productivity gains.",Support & SLAs,Lever,RQ3
22,Finance / Banking / Insurance,9,Currently no prominent deployments of Hugging Face solutions.,Deployment architecture,Lever,RQ3
22,Finance / Banking / Insurance,10,Regulatory compliance and data privacy concerns are the major challenges faced when adopting open-source AI.,Compliance,Gate,RQ2
22,Finance / Banking / Insurance,10,Regulatory compliance and data privacy concerns are the major challenges faced when adopting open-source AI.,Data privacy & control,Gate,RQ2
22,Finance / Banking / Insurance,11,"Enterprise-grade regulatory compliance, deployment support, integration capabilities, and assured data security would be critical to making Hugging Face’s open-source AI offerings more attractive to financial institutions.",Compliance,Gate,RQ2
22,Finance / Banking / Insurance,11,"Enterprise-grade regulatory compliance, deployment support, integration capabilities, and assured data security would be critical to making Hugging Face’s open-source AI offerings more attractive to financial institutions.",Security posture,Gate,RQ2
22,Finance / Banking / Insurance,11,"Enterprise-grade regulatory compliance, deployment support, integration capabilities, and assured data security would be critical to making Hugging Face’s open-source AI offerings more attractive to financial institutions.",Support & SLAs,Lever,RQ3
22,Finance / Banking / Insurance,11,"Enterprise-grade regulatory compliance, deployment support, integration capabilities, and assured data security would be critical to making Hugging Face’s open-source AI offerings more attractive to financial institutions.",Deployment architecture,Lever,RQ3
22,Finance / Banking / Insurance,12,"Compliance, security, vendor support, and productivity improvements.",Compliance,Gate,RQ2
22,Finance / Banking / Insurance,12,"Compliance, security, vendor support, and productivity improvements.",Security posture,Gate,RQ2
22,Finance / Banking / Insurance,12,"Compliance, security, vendor support, and productivity improvements.",Support & SLAs,Lever,RQ3
23,Finance / Banking / Insurance,Q5,Cost,Cost & TCO,Lever,RQ1
23,Finance / Banking / Insurance,8,"Open-source AI is attractive for avoiding vendor lock-in, but proprietary solutions are favoured for ease of use, support, and faster implementation given our small team.",Support & SLAs,Lever,RQ3
23,Finance / Banking / Insurance,9,Currently in pilot or evaluation stages; potential to use Hugging Face models for automating invoice processing and financial document summarization to reduce manual work.,Documentation completeness,Gate,RQ2
23,Finance / Banking / Insurance,10,"- Limited in-house AI expertise to implement and maintain models. 
- Data privacy and security compliance with financial regulations.",Compliance,Gate,RQ2
23,Finance / Banking / Insurance,10,"- Limited in-house AI expertise to implement and maintain models. 
- Data privacy and security compliance with financial regulations.",Security posture,Gate,RQ2
23,Finance / Banking / Insurance,10,"- Limited in-house AI expertise to implement and maintain models. 
- Data privacy and security compliance with financial regulations.",Data privacy & control,Gate,RQ2
23,Finance / Banking / Insurance,11,Enhanced compliance-focused resources and security best practices documentation relevant to financial data processing would also be helpful for startups in regulated industries.,Compliance,Gate,RQ2
23,Finance / Banking / Insurance,11,Enhanced compliance-focused resources and security best practices documentation relevant to financial data processing would also be helpful for startups in regulated industries.,Security posture,Gate,RQ2
23,Finance / Banking / Insurance,11,Enhanced compliance-focused resources and security best practices documentation relevant to financial data processing would also be helpful for startups in regulated industries.,Documentation completeness,Gate,RQ2
23,Finance / Banking / Insurance,12,"Reliability, ease of deployment and support.",Support & SLAs,Lever,RQ3
23,Finance / Banking / Insurance,12,"Reliability, ease of deployment and support.",Deployment architecture,Lever,RQ3
24,Finance / Banking / Insurance,Q5,"Compliance, Data Privacy",Compliance,Gate,RQ1
24,Finance / Banking / Insurance,8,"Regulatory fit, risk control",No keyword match,NA,NA
24,Finance / Banking / Insurance,9,Evaluating retrieval over policy docs,No keyword match,NA,NA
24,Finance / Banking / Insurance,10,Legal approval cycles,No keyword match,NA,NA
24,Finance / Banking / Insurance,11,Compliance templates and SLAs,Compliance,Gate,RQ2
24,Finance / Banking / Insurance,12,"Governance first, cost second",Governance readiness,Gate,RQ2
24,Finance / Banking / Insurance,12,"Governance first, cost second",Cost & TCO,Lever,RQ3
25,Finance / Banking / Insurance,Q5,"Cost, Support, Compliance",Compliance,Gate,RQ1
25,Finance / Banking / Insurance,8,Balance cost with risk management,Cost & TCO,Lever,RQ3
25,Finance / Banking / Insurance,9,Pilot sentiment classifier for audit reports,No keyword match,NA,NA
25,Finance / Banking / Insurance,10,"Data residency, approval delays",No keyword match,NA,NA
25,Finance / Banking / Insurance,11,Private deployment guidance,Deployment architecture,Lever,RQ3
25,Finance / Banking / Insurance,12,Feasibility → Cost → SLA,Cost & TCO,Lever,RQ3
26,Finance / Banking / Insurance,Q5,"Data Privacy, Risk Management",Data privacy & control,Gate,RQ1
26,Finance / Banking / Insurance,8,"Minimize exposure, ensure GDPR",No keyword match,NA,NA
26,Finance / Banking / Insurance,9,None yet — early evaluation,No keyword match,NA,NA
26,Finance / Banking / Insurance,10,"Security clearance, policy fit",Security posture,Gate,RQ2
26,Finance / Banking / Insurance,11,Repository trust indicators,No keyword match,NA,NA
26,Finance / Banking / Insurance,12,Compliance gates before comparison,Compliance,Gate,RQ2
27,FMCG,Q5,"Customization, Cost, Performance, Vendor Support, Privacy",Data privacy & control,Gate,RQ1
27,FMCG,8,We are still developing the first solutions. Exploration phase so far.,No keyword match,NA,NA
27,FMCG,9,Not aware.,No keyword match,NA,NA
27,FMCG,10,technical mostly,No keyword match,NA,NA
27,FMCG,11,solving what is in number 5,No keyword match,NA,NA
27,FMCG,12,"Once the solution is proven to provide value implementation support is most important criteria. Since the implementations can be very large scale, support both before and after deployment is extremely important. Future roadmap must exist for the technology.",Support & SLAs,Lever,RQ3
27,FMCG,12,"Once the solution is proven to provide value implementation support is most important criteria. Since the implementations can be very large scale, support both before and after deployment is extremely important. Future roadmap must exist for the technology.",Deployment architecture,Lever,RQ3
28,FMCG,Q5,"Cost, Ease of Use, Support",Cost & TCO,Lever,RQ1
28,FMCG,8,Limited internal expertise,No keyword match,NA,NA
28,FMCG,9,Testing basic sentiment analysis,No keyword match,NA,NA
28,FMCG,10,Lack of internal talent,No keyword match,NA,NA
28,FMCG,11,End-to-end templates,No keyword match,NA,NA
28,FMCG,12,Feasibility before cost,Cost & TCO,Lever,RQ3
29,FMCG,Q5,"Cost, Vendor Reliability",Cost & TCO,Lever,RQ1
29,FMCG,8,"Faster deployment, less engineering",Deployment architecture,Lever,RQ3
29,FMCG,9,Social media monitoring POC,Observability & monitoring,Lever,RQ3
29,FMCG,10,"Skills gap, integration issues",No keyword match,NA,NA
29,FMCG,11,Training and tutorials,No keyword match,NA,NA
29,FMCG,12,SLA > Customization,Customization & PEFT,Lever,RQ3
30,FMCG,Q5,"Cost, Ease of Integration",Cost & TCO,Lever,RQ1
30,FMCG,8,"Simplicity, vendor support",Support & SLAs,Lever,RQ3
30,FMCG,9,Idea tagging pilot,No keyword match,NA,NA
30,FMCG,10,Lack of AI literacy,No keyword match,NA,NA
30,FMCG,11,Turnkey pipelines,No keyword match,NA,NA
30,FMCG,12,ROI vs Risk,No keyword match,NA,NA
31,Government / Public Sector,Q5,Regulatory compliance,Compliance,Gate,RQ1
31,Government / Public Sector,8,"The preference typically leans toward proprietary AI solutions due to assured regulatory compliance, vendor accountability, and risk mitigation. Open-source AI might be considered only for non-critical or experimental projects where flexibility is needed but under stringent controls.",Compliance,Gate,RQ2
31,Government / Public Sector,9,Use cases in government are often limited but could include pilot projects involving NLP.,No keyword match,NA,NA
31,Government / Public Sector,10,"Technical challenges: integrating open-source AI with legacy government IT infrastructure.
Organisational challenges: resistance to change, limited internal expertise, and coordination across departments.
Legal challenges: compliance with strict data privacy, security regulations, and procurement rules.",Compliance,Gate,RQ2
31,Government / Public Sector,10,"Technical challenges: integrating open-source AI with legacy government IT infrastructure.
Organisational challenges: resistance to change, limited internal expertise, and coordination across departments.
Legal challenges: compliance with strict data privacy, security regulations, and procurement rules.",Security posture,Gate,RQ2
31,Government / Public Sector,10,"Technical challenges: integrating open-source AI with legacy government IT infrastructure.
Organisational challenges: resistance to change, limited internal expertise, and coordination across departments.
Legal challenges: compliance with strict data privacy, security regulations, and procurement rules.",Data privacy & control,Gate,RQ2
31,Government / Public Sector,11,Features for transparent auditing and explainability to meet governance requirements.,Governance readiness,Gate,RQ2
31,Government / Public Sector,12,"We prioritize risk mitigation, regulatory compliance, and data governance when evaluating trade-offs. Proprietary AI solutions are preferred for critical and sensitive applications due to guaranteed vendor support, accountability, and compliance assurances.",Compliance,Gate,RQ2
31,Government / Public Sector,12,"We prioritize risk mitigation, regulatory compliance, and data governance when evaluating trade-offs. Proprietary AI solutions are preferred for critical and sensitive applications due to guaranteed vendor support, accountability, and compliance assurances.",Governance readiness,Gate,RQ2
31,Government / Public Sector,12,"We prioritize risk mitigation, regulatory compliance, and data governance when evaluating trade-offs. Proprietary AI solutions are preferred for critical and sensitive applications due to guaranteed vendor support, accountability, and compliance assurances.",Support & SLAs,Lever,RQ3
32,Government / Public Sector,Q5,Regulatory compliance,Compliance,Gate,RQ1
32,Government / Public Sector,8,"Proprietary AI is favoured for assured compliance, support, and risk management. Open-source AI may be used cautiously only in pilot or experimental projects that do not impact critical operations.",Compliance,Gate,RQ2
32,Government / Public Sector,8,"Proprietary AI is favoured for assured compliance, support, and risk management. Open-source AI may be used cautiously only in pilot or experimental projects that do not impact critical operations.",Support & SLAs,Lever,RQ3
32,Government / Public Sector,9,Limited use cases primarily involving pilot NLP projects.,No keyword match,NA,NA
32,Government / Public Sector,10,Integration with legacy systems. Ensuring strict regulatory and security compliance. Lack of internal expertise. Resistance to change within the organization. Procurement and governance hurdles.,Compliance,Gate,RQ2
32,Government / Public Sector,10,Integration with legacy systems. Ensuring strict regulatory and security compliance. Lack of internal expertise. Resistance to change within the organization. Procurement and governance hurdles.,Security posture,Gate,RQ2
32,Government / Public Sector,10,Integration with legacy systems. Ensuring strict regulatory and security compliance. Lack of internal expertise. Resistance to change within the organization. Procurement and governance hurdles.,Governance readiness,Gate,RQ2
32,Government / Public Sector,11,Transparency and auditing features for governance.,Governance readiness,Gate,RQ2
32,Government / Public Sector,12,"The organisation prioritizes risk mitigation, regulatory compliance, and accountability.",Compliance,Gate,RQ2
33,Government / Public Sector,Q5,Regulatory compliance,Compliance,Gate,RQ1
33,Government / Public Sector,8,"Open-source AI is valued for transparency, adaptability to local needs, and alignment with European digital sovereignty goals.
Proprietary AI is used for mission‑critical systems where vendor accountability, guaranteed SLAs, and compliance certification are essential.",Compliance,Gate,RQ2
33,Government / Public Sector,9,Pilot projects focused on natural language processing for public health information services.,No keyword match,NA,NA
33,Government / Public Sector,10,"Ensuring strict compliance with GDPR and specific health data privacy laws. Integrating open-source AI tools into complex, multi-agency public health IT ecosystems and legacy systems.",Compliance,Gate,RQ2
33,Government / Public Sector,10,"Ensuring strict compliance with GDPR and specific health data privacy laws. Integrating open-source AI tools into complex, multi-agency public health IT ecosystems and legacy systems.",Data privacy & control,Gate,RQ2
33,Government / Public Sector,11,Regulatory-focused deployment guides.,Deployment architecture,Lever,RQ3
33,Government / Public Sector,12,"Adaptability, transparency, and innovation afforded by open-source AI with the operational stability, vendor accountability, and regulatory assurances offered by proprietary solutions.",No keyword match,NA,NA
34,Government / Public Sector,Q5,Regulatory compliance,Compliance,Gate,RQ1
34,Government / Public Sector,8,"Open-source AI is valued for transparency, adaptability, multilingual capability.
Proprietary AI is chosen for mission-critical, classified, or legally sensitive operations requiring certified vendor support.",Support & SLAs,Lever,RQ3
34,Government / Public Sector,9,In policy research and internal knowledge management pilots.,No keyword match,NA,NA
34,Government / Public Sector,10,"- Confidentiality and security mandate alignment
- Integration with complex, security‑hardened federal systems
- Multilingual performance management and bias mitigation
- Compliance assurance and accuracy consistency in model updates",Compliance,Gate,RQ2
34,Government / Public Sector,10,"- Confidentiality and security mandate alignment
- Integration with complex, security‑hardened federal systems
- Multilingual performance management and bias mitigation
- Compliance assurance and accuracy consistency in model updates",Security posture,Gate,RQ2
34,Government / Public Sector,10,"- Confidentiality and security mandate alignment
- Integration with complex, security‑hardened federal systems
- Multilingual performance management and bias mitigation
- Compliance assurance and accuracy consistency in model updates",Performance sufficiency,Lever,RQ3
34,Government / Public Sector,11,Model cards with more detailed information.,No keyword match,NA,NA
34,Government / Public Sector,12,"Transparency, adaptability, and sovereignty benefits of open-source AI against the guaranteed SLAs, vendor accountability, and certification of proprietary AI.",No keyword match,NA,NA
35,Government / Public Sector,Q5,Regulatory compliance,Compliance,Gate,RQ1
35,Government / Public Sector,8,"Proprietary AI is valued for reliability, turnkey deployment, and vendor accountability in the public education sector.",Deployment architecture,Lever,RQ3
35,Government / Public Sector,9,No direct open‑source model deployment.,Deployment architecture,Lever,RQ3
35,Government / Public Sector,10,Regulatory compliance.,Compliance,Gate,RQ2
35,Government / Public Sector,11,Clear compliance and deployment guides for education-sector AI.,Compliance,Gate,RQ2
35,Government / Public Sector,11,Clear compliance and deployment guides for education-sector AI.,Deployment architecture,Lever,RQ3
35,Government / Public Sector,12,"Reliability, compliance, and vendor accountability, leading to a preference for proprietary systems in national rollouts.",Compliance,Gate,RQ2
36,Government / Public Sector,Q5,"Documentation, Compliance",Compliance,Gate,RQ1
36,Government / Public Sector,8,"Transparency, accountability",No keyword match,NA,NA
36,Government / Public Sector,9,Policy analysis prototype,No keyword match,NA,NA
36,Government / Public Sector,10,Security posture and data isolation,Security posture,Gate,RQ2
36,Government / Public Sector,11,Public-sector audit kits,No keyword match,NA,NA
36,Government / Public Sector,12,Compliance > Cost,Compliance,Gate,RQ2
36,Government / Public Sector,12,Compliance > Cost,Cost & TCO,Lever,RQ3
37,Government / Public Sector,Q5,"Governance, Data Privacy",Data privacy & control,Gate,RQ1
37,Government / Public Sector,8,"Auditability, traceability",No keyword match,NA,NA
37,Government / Public Sector,9,None yet — feasibility study,No keyword match,NA,NA
37,Government / Public Sector,10,Legal review and IT policy,No keyword match,NA,NA
37,Government / Public Sector,11,Public-use compliance tools,Compliance,Gate,RQ2
37,Government / Public Sector,12,GDPR check → SLA,No keyword match,NA,NA
38,Government / Public Sector,Q5,"Performance, Compliance",Compliance,Gate,RQ1
38,Government / Public Sector,8,Balance open vs closed ecosystems,No keyword match,NA,NA
38,Government / Public Sector,9,Retrieval over public open data,No keyword match,NA,NA
38,Government / Public Sector,10,Procurement friction,No keyword match,NA,NA
38,Government / Public Sector,11,Licensing examples,Licensing clarity,Gate,RQ2
38,Government / Public Sector,12,Policy gate → Performance,Performance sufficiency,Lever,RQ3
39,Government / Public Sector,Q5,"Data Privacy, Vendor Dependence",Data privacy & control,Gate,RQ1
39,Government / Public Sector,8,Reduce lock-in,No keyword match,NA,NA
39,Government / Public Sector,9,Not yet piloted,No keyword match,NA,NA
39,Government / Public Sector,10,Integration and internal review,No keyword match,NA,NA
39,Government / Public Sector,11,Government-tailored guidance,No keyword match,NA,NA
39,Government / Public Sector,12,GDPR and sovereignty first,No keyword match,NA,NA
40,Healthcare / Biotech / Life Sciences,Q5,Data control / privacy,Data privacy & control,Gate,RQ1
40,Healthcare / Biotech / Life Sciences,8,Not there yet,No keyword match,NA,NA
40,Healthcare / Biotech / Life Sciences,9,None.,No keyword match,NA,NA
40,Healthcare / Biotech / Life Sciences,10,nan,No keyword match,NA,NA
40,Healthcare / Biotech / Life Sciences,11,nan,No keyword match,NA,NA
40,Healthcare / Biotech / Life Sciences,12,"Security and compliance, performance and accuracy, cost and resource allocation.",Compliance,Gate,RQ2
40,Healthcare / Biotech / Life Sciences,12,"Security and compliance, performance and accuracy, cost and resource allocation.",Security posture,Gate,RQ2
40,Healthcare / Biotech / Life Sciences,12,"Security and compliance, performance and accuracy, cost and resource allocation.",Cost & TCO,Lever,RQ3
40,Healthcare / Biotech / Life Sciences,12,"Security and compliance, performance and accuracy, cost and resource allocation.",Performance sufficiency,Lever,RQ3
41,Healthcare / Biotech / Life Sciences,Q5,Performance,Performance sufficiency,Lever,RQ1
41,Healthcare / Biotech / Life Sciences,8,To balance customization and cost with stringent regulatory compliance and data privacy requirements inherent in healthcare.,Compliance,Gate,RQ2
41,Healthcare / Biotech / Life Sciences,8,To balance customization and cost with stringent regulatory compliance and data privacy requirements inherent in healthcare.,Data privacy & control,Gate,RQ2
41,Healthcare / Biotech / Life Sciences,8,To balance customization and cost with stringent regulatory compliance and data privacy requirements inherent in healthcare.,Cost & TCO,Lever,RQ3
41,Healthcare / Biotech / Life Sciences,8,To balance customization and cost with stringent regulatory compliance and data privacy requirements inherent in healthcare.,Customization & PEFT,Lever,RQ3
41,Healthcare / Biotech / Life Sciences,9,Open-source AI solutions have helped develop clinical decision support tools that enhance diagnostic accuracy and patient care by leveraging natural language processing and machine learning models.,Support & SLAs,Lever,RQ3
41,Healthcare / Biotech / Life Sciences,10,Challenges include ensuring compliance with healthcare regulations and addressing data security and confidentiality concerns.,Compliance,Gate,RQ2
41,Healthcare / Biotech / Life Sciences,10,Challenges include ensuring compliance with healthcare regulations and addressing data security and confidentiality concerns.,Security posture,Gate,RQ2
41,Healthcare / Biotech / Life Sciences,11,Dedicated enterprise support.,Support & SLAs,Lever,RQ3
41,Healthcare / Biotech / Life Sciences,12,"The evaluation focuses on customization and cost benefits of open-source versus reliability, vendor support, and compliance assurances of proprietary solutions.",Compliance,Gate,RQ2
41,Healthcare / Biotech / Life Sciences,12,"The evaluation focuses on customization and cost benefits of open-source versus reliability, vendor support, and compliance assurances of proprietary solutions.",Cost & TCO,Lever,RQ3
41,Healthcare / Biotech / Life Sciences,12,"The evaluation focuses on customization and cost benefits of open-source versus reliability, vendor support, and compliance assurances of proprietary solutions.",Support & SLAs,Lever,RQ3
41,Healthcare / Biotech / Life Sciences,12,"The evaluation focuses on customization and cost benefits of open-source versus reliability, vendor support, and compliance assurances of proprietary solutions.",Customization & PEFT,Lever,RQ3
42,Healthcare / Biotech / Life Sciences,Q5,Data control / privacy,Data privacy & control,Gate,RQ1
42,Healthcare / Biotech / Life Sciences,8,"Scientific progress, reproducibility, and network effects that benefit the entire community.",No keyword match,NA,NA
42,Healthcare / Biotech / Life Sciences,9,"Our flagship model created substantial value by demonstrating our capabilities to potential enterprise customers, generating research collaborations, and establishing our reputation in the industry.",Customization & PEFT,Lever,RQ3
42,Healthcare / Biotech / Life Sciences,10,"Data aggregation, talent, complex data licensing.",Licensing clarity,Gate,RQ2
42,Healthcare / Biotech / Life Sciences,11,Enhanced model cards.,No keyword match,NA,NA
42,Healthcare / Biotech / Life Sciences,12,"Open-source: reproducibility, community validation, and broader adoption.",No keyword match,NA,NA
43,Healthcare / Biotech / Life Sciences,Q5,Regulatory compliance,Compliance,Gate,RQ1
43,Healthcare / Biotech / Life Sciences,8,Innovation speed from open-source and reliability/compliance from proprietary AI.,Compliance,Gate,RQ2
43,Healthcare / Biotech / Life Sciences,9,"We use open-source NLP models as benchmarks and components in our platform for tasks like document analysis, metadata extraction, and literature monitoring. These open-source foundations enable us to rapidly prototype and validate new features before full proprietary implementation.",Documentation completeness,Gate,RQ2
43,Healthcare / Biotech / Life Sciences,9,"We use open-source NLP models as benchmarks and components in our platform for tasks like document analysis, metadata extraction, and literature monitoring. These open-source foundations enable us to rapidly prototype and validate new features before full proprietary implementation.",Observability & monitoring,Lever,RQ3
43,Healthcare / Biotech / Life Sciences,10,Technical: adapting general-purpose models for highly specialized medical and regulatory content. Legal: we must ensure all AI components meet stringent healthcare data privacy requirements and regulatory standards across multiple jurisdictions.,Data privacy & control,Gate,RQ2
43,Healthcare / Biotech / Life Sciences,11,More robust enterprise-grade security features and audit trails.,Security posture,Gate,RQ2
43,Healthcare / Biotech / Life Sciences,12,"Regulatory compliance requirements, performance for specialised medical tasks, and total cost of ownership.",Compliance,Gate,RQ2
43,Healthcare / Biotech / Life Sciences,12,"Regulatory compliance requirements, performance for specialised medical tasks, and total cost of ownership.",Cost & TCO,Lever,RQ3
43,Healthcare / Biotech / Life Sciences,12,"Regulatory compliance requirements, performance for specialised medical tasks, and total cost of ownership.",Performance sufficiency,Lever,RQ3
44,Healthcare / Biotech / Life Sciences,Q5,Performance,Performance sufficiency,Lever,RQ1
44,Healthcare / Biotech / Life Sciences,8,"Open-source to accelerate research into single-cell workflows and enable rapid prototyping of biomarker discovery pipelines. Proprietary for validated, production-grade deployments that meet regulatory requirements and guarantee data security for client studies.",Security posture,Gate,RQ2
44,Healthcare / Biotech / Life Sciences,8,"Open-source to accelerate research into single-cell workflows and enable rapid prototyping of biomarker discovery pipelines. Proprietary for validated, production-grade deployments that meet regulatory requirements and guarantee data security for client studies.",Deployment architecture,Lever,RQ3
44,Healthcare / Biotech / Life Sciences,9,"We evaluated an open-source variational inference model for single-cell data as a foundation for our multimodal integration pipeline. It reduced dimensionality and batch-effect correction time by 60%, enabling faster identification of disease-specific cell populations for our diagnostic partners.",No keyword match,NA,NA
44,Healthcare / Biotech / Life Sciences,10,"- Harmonizing heterogeneous single-cell datasets with varying formats and quality.
- Ensuring reproducibility and traceability in regulated studies (audit trails, data lineage).
- Recruiting talent with combined expertise in deep learning and advanced biology.",No keyword match,NA,NA
44,Healthcare / Biotech / Life Sciences,11,"Turnkey, life-sciences-specific model card templates with provenance and validation sections.",No keyword match,NA,NA
44,Healthcare / Biotech / Life Sciences,12,"We prioritize open-source AI for research and pilot phases to benefit from community innovations and low entry-cost experimentation. For client-facing products, we layer proprietary tooling on top of open-source models or choose enterprise-grade vendors to ensure regulatory compliance and consistent performance.",Compliance,Gate,RQ2
44,Healthcare / Biotech / Life Sciences,12,"We prioritize open-source AI for research and pilot phases to benefit from community innovations and low entry-cost experimentation. For client-facing products, we layer proprietary tooling on top of open-source models or choose enterprise-grade vendors to ensure regulatory compliance and consistent performance.",Cost & TCO,Lever,RQ3
44,Healthcare / Biotech / Life Sciences,12,"We prioritize open-source AI for research and pilot phases to benefit from community innovations and low entry-cost experimentation. For client-facing products, we layer proprietary tooling on top of open-source models or choose enterprise-grade vendors to ensure regulatory compliance and consistent performance.",Performance sufficiency,Lever,RQ3
45,Healthcare / Biotech / Life Sciences,Q5,"Compliance, Data Control",Compliance,Gate,RQ1
45,Healthcare / Biotech / Life Sciences,8,"Data protection, patient privacy",Data privacy & control,Gate,RQ2
45,Healthcare / Biotech / Life Sciences,9,Prototype de-identification workflow,No keyword match,NA,NA
45,Healthcare / Biotech / Life Sciences,10,Documentation and traceability,Documentation completeness,Gate,RQ2
45,Healthcare / Biotech / Life Sciences,11,Health-specific compliance templates,Compliance,Gate,RQ2
45,Healthcare / Biotech / Life Sciences,12,"GDPR first, then performance",Performance sufficiency,Lever,RQ3
46,Healthcare / Biotech / Life Sciences,Q5,"Compliance, Documentation",Compliance,Gate,RQ1
46,Healthcare / Biotech / Life Sciences,8,"Transparency, traceability",No keyword match,NA,NA
46,Healthcare / Biotech / Life Sciences,9,Planning for medical text summarisation,No keyword match,NA,NA
46,Healthcare / Biotech / Life Sciences,10,License ambiguity,Licensing clarity,Gate,RQ2
46,Healthcare / Biotech / Life Sciences,11,Legal templates and clearer model cards,No keyword match,NA,NA
46,Healthcare / Biotech / Life Sciences,12,"Governance filter, no shortcuts",Governance readiness,Gate,RQ2
47,Healthcare / Biotech / Life Sciences,Q5,"Cost, Support, Time to Value",Cost & TCO,Lever,RQ1
47,Healthcare / Biotech / Life Sciences,8,Fine-tuning capability,No keyword match,NA,NA
47,Healthcare / Biotech / Life Sciences,9,Annotating patient feedback for triage,No keyword match,NA,NA
47,Healthcare / Biotech / Life Sciences,10,Model reliability,No keyword match,NA,NA
47,Healthcare / Biotech / Life Sciences,11,Enterprise SLAs,No keyword match,NA,NA
47,Healthcare / Biotech / Life Sciences,12,Feasibility check → Compare cost,Cost & TCO,Lever,RQ3
48,Hospitality and Tourism Industry,Q5,Performance,Performance sufficiency,Lever,RQ1
48,Hospitality and Tourism Industry,8,"Reliability, brand safety, client compliance needs, and integration with content and production systems.",Compliance,Gate,RQ2
48,Hospitality and Tourism Industry,9,Currently none,No keyword match,NA,NA
48,Hospitality and Tourism Industry,10,"Governance/compliance, brand-safety controls for generated content, and integration with production/logistics/content systems.",Compliance,Gate,RQ2
48,Hospitality and Tourism Industry,10,"Governance/compliance, brand-safety controls for generated content, and integration with production/logistics/content systems.",Governance readiness,Gate,RQ2
48,Hospitality and Tourism Industry,11,Enriched model cards.,No keyword match,NA,NA
48,Hospitality and Tourism Industry,12,"Reliability, brand safety, compliance, and support (proprietary) against flexibility and cost (open-source).",Compliance,Gate,RQ2
48,Hospitality and Tourism Industry,12,"Reliability, brand safety, compliance, and support (proprietary) against flexibility and cost (open-source).",Cost & TCO,Lever,RQ3
48,Hospitality and Tourism Industry,12,"Reliability, brand safety, compliance, and support (proprietary) against flexibility and cost (open-source).",Support & SLAs,Lever,RQ3
49,Hospitality and Tourism Industry,Q5,"Cost, Time to Value",Cost & TCO,Lever,RQ1
49,Hospitality and Tourism Industry,8,Ease of entry,No keyword match,NA,NA
49,Hospitality and Tourism Industry,9,RAG chatbot for guest FAQs,No keyword match,NA,NA
49,Hospitality and Tourism Industry,10,Lack of skilled staff,No keyword match,NA,NA
49,Hospitality and Tourism Industry,11,Simple deployment SDKs,Deployment architecture,Lever,RQ3
49,Hospitality and Tourism Industry,12,Feasibility before ROI,No keyword match,NA,NA
50,Hospitality and Tourism Industry,Q5,"Support, Compliance",Compliance,Gate,RQ1
50,Hospitality and Tourism Industry,8,Data ownership and flexibility,No keyword match,NA,NA
50,Hospitality and Tourism Industry,9,Internal knowledge base assistant,No keyword match,NA,NA
50,Hospitality and Tourism Industry,10,Limited documentation,Documentation completeness,Gate,RQ2
50,Hospitality and Tourism Industry,11,Step-by-step guides,No keyword match,NA,NA
50,Hospitality and Tourism Industry,12,Security and GDPR checks first,Security posture,Gate,RQ2
51,Hospitality and Tourism Industry,Q5,"Data Privacy, Performance",Data privacy & control,Gate,RQ1
51,Hospitality and Tourism Industry,8,Better customer experience,Customization & PEFT,Lever,RQ3
51,Hospitality and Tourism Industry,9,Pilot chatbot in sandbox,No keyword match,NA,NA
51,Hospitality and Tourism Industry,10,Integration complexity,No keyword match,NA,NA
51,Hospitality and Tourism Industry,11,Clearer hosting options,No keyword match,NA,NA
51,Hospitality and Tourism Industry,12,Privacy and cost,Data privacy & control,Gate,RQ2
51,Hospitality and Tourism Industry,12,Privacy and cost,Cost & TCO,Lever,RQ3
52,Manufacturing / Industrial,Q5,Regulatory compliance,Compliance,Gate,RQ1
52,Manufacturing / Industrial,8,We have not adopted either yet. Compliance and supplier-assurance gates come first,Compliance,Gate,RQ2
52,Manufacturing / Industrial,9,Currently none.,No keyword match,NA,NA
52,Manufacturing / Industrial,10,Technical and organisational.,No keyword match,NA,NA
52,Manufacturing / Industrial,11,Not sure yet.,No keyword match,NA,NA
52,Manufacturing / Industrial,12,"Compliance and safety first, then security and support",Compliance,Gate,RQ2
52,Manufacturing / Industrial,12,"Compliance and safety first, then security and support",Security posture,Gate,RQ2
52,Manufacturing / Industrial,12,"Compliance and safety first, then security and support",Support & SLAs,Lever,RQ3
53,Manufacturing / Industrial,Q5,Data control / privacy,Data privacy & control,Gate,RQ1
53,Manufacturing / Industrial,8,Productivity increase,No keyword match,NA,NA
53,Manufacturing / Industrial,9,"No production use yet. A small on premises pilot summarised maintenance SOPs and incident reports with HF models, reducing lookup time for shift leads while keeping data inside our network.",No keyword match,NA,NA
53,Manufacturing / Industrial,10,Fear about data confidentiality,No keyword match,NA,NA
53,Manufacturing / Industrial,11,nan,No keyword match,NA,NA
53,Manufacturing / Industrial,12,"Data confidentiality, IP protection, and GDPR compliance first. If those pass, we assess security and support, then task performance and total cost of ownership.",Compliance,Gate,RQ2
53,Manufacturing / Industrial,12,"Data confidentiality, IP protection, and GDPR compliance first. If those pass, we assess security and support, then task performance and total cost of ownership.",Security posture,Gate,RQ2
53,Manufacturing / Industrial,12,"Data confidentiality, IP protection, and GDPR compliance first. If those pass, we assess security and support, then task performance and total cost of ownership.",Cost & TCO,Lever,RQ3
53,Manufacturing / Industrial,12,"Data confidentiality, IP protection, and GDPR compliance first. If those pass, we assess security and support, then task performance and total cost of ownership.",Performance sufficiency,Lever,RQ3
53,Manufacturing / Industrial,12,"Data confidentiality, IP protection, and GDPR compliance first. If those pass, we assess security and support, then task performance and total cost of ownership.",Support & SLAs,Lever,RQ3
54,Manufacturing / Industrial,Q5,Regulatory compliance,Compliance,Gate,RQ1
54,Manufacturing / Industrial,8,"Regulatory and safety constraints drive the use of proprietary AI, while open-source is adopted for non-safety-critical R&D and predictive-maintenance analytics.",No keyword match,NA,NA
54,Manufacturing / Industrial,9,We used open-source NLP models to analyze maintenance logs and service bulletins. This automated readability scoring and anomaly detection reduced manual triage time by 40%.,No keyword match,NA,NA
54,Manufacturing / Industrial,10,"Compliance with EASA certification requirements, intellectual-property and export-control restrictions, aligning cross-functional processes between R&D, engineering, and safety authorities.",Compliance,Gate,RQ2
54,Manufacturing / Industrial,11,Built-in audit trails.,No keyword match,NA,NA
54,Manufacturing / Industrial,12,"Innovation speed and flexibility (open-source) against the need for formal certification, traceability, and vendor accountability (proprietary).",No keyword match,NA,NA
55,Manufacturing / Industrial,Q5,Performance,Performance sufficiency,Lever,RQ1
55,Manufacturing / Industrial,8,Open-source: customization of anomaly-detection models. Proprietary AI: enterprise-grade security.,Security posture,Gate,RQ2
55,Manufacturing / Industrial,8,Open-source: customization of anomaly-detection models. Proprietary AI: enterprise-grade security.,Customization & PEFT,Lever,RQ3
55,Manufacturing / Industrial,9,We used Hugging Face on time-series sensor data to detect pump anomalies that reduced unscheduled downtime and maintenance costs.,Cost & TCO,Lever,RQ3
55,Manufacturing / Industrial,10,Data quality and consistency across legacy SCADA systems. Model integration into existing OT networks with strict uptime requirements,No keyword match,NA,NA
55,Manufacturing / Industrial,11,/,No keyword match,NA,NA
55,Manufacturing / Industrial,12,"Total cost of ownership and flexibility against operational reliability, SLAs, and integration ease.",Cost & TCO,Lever,RQ3
56,Manufacturing / Industrial,Q5,Vendor support,Support & SLAs,Lever,RQ1
56,Manufacturing / Industrial,8,"We needed rapid deployment with clear support and certifications that fit our quality system. Managed services simplified security reviews, change control, and integration with our OT environment.",Security posture,Gate,RQ2
56,Manufacturing / Industrial,8,"We needed rapid deployment with clear support and certifications that fit our quality system. Managed services simplified security reviews, change control, and integration with our OT environment.",Support & SLAs,Lever,RQ3
56,Manufacturing / Industrial,8,"We needed rapid deployment with clear support and certifications that fit our quality system. Managed services simplified security reviews, change control, and integration with our OT environment.",Deployment architecture,Lever,RQ3
56,Manufacturing / Industrial,9,"Vision based defect detection on the production line using a managed model and pipeline. Reliability, monitoring, and traceability were decisive.",Observability & monitoring,Lever,RQ3
56,Manufacturing / Industrial,10,"Validating open components against safety and quality processes, standing up on premises GPU operations, and ensuring end to end traceability for audits.",No keyword match,NA,NA
56,Manufacturing / Industrial,11,Example change-control records to speed quality reviews.,No keyword match,NA,NA
56,Manufacturing / Industrial,12,"Compliance risk and time to production are the gates. Also, flexibility and total cost of ownership.",Compliance,Gate,RQ2
56,Manufacturing / Industrial,12,"Compliance risk and time to production are the gates. Also, flexibility and total cost of ownership.",Cost & TCO,Lever,RQ3
57,Media & Entertainment,Q5,Performance,Performance sufficiency,Lever,RQ1
57,Media & Entertainment,8,On-prem deployment for PII-heavy content pipelines; full inspectability for legal review; flexible fine-tuning on our storytelling IP.,Deployment architecture,Lever,RQ3
57,Media & Entertainment,9,"Fine-tuning an open-source multilingual model on 100 years of subtitles automatically tags each scene with characters, locations, and emotions, shrinking clip-search from hours to seconds and saving six-figure annual costs.",Cost & TCO,Lever,RQ3
57,Media & Entertainment,10,"Licence clarity, Model-card completeness, Inference cost spikes, change management.",Cost & TCO,Lever,RQ3
57,Media & Entertainment,11,"Built-in policy enforcement hooks (e.g., export-compliance checks), Curated, security-scanned model zoo flagged for commercial use",Compliance,Gate,RQ2
57,Media & Entertainment,11,"Built-in policy enforcement hooks (e.g., export-compliance checks), Curated, security-scanned model zoo flagged for commercial use",Security posture,Gate,RQ2
57,Media & Entertainment,12,"Latency goal, data control, accuracy ceiling, reg-compliance, total cost.",Compliance,Gate,RQ2
57,Media & Entertainment,12,"Latency goal, data control, accuracy ceiling, reg-compliance, total cost.",Cost & TCO,Lever,RQ3
57,Media & Entertainment,12,"Latency goal, data control, accuracy ceiling, reg-compliance, total cost.",Performance & latency,Lever,RQ3
58,Media & Entertainment,Q5,"Performance, Cost",Cost & TCO,Lever,RQ1
58,Media & Entertainment,8,"Creativity, Adaptability",No keyword match,NA,NA
58,Media & Entertainment,9,Caption generation,No keyword match,NA,NA
58,Media & Entertainment,10,IP review,No keyword match,NA,NA
58,Media & Entertainment,11,Model card clarity,No keyword match,NA,NA
58,Media & Entertainment,12,Performance > Cost,Cost & TCO,Lever,RQ3
58,Media & Entertainment,12,Performance > Cost,Performance sufficiency,Lever,RQ3
59,Media & Entertainment,Q5,"Cost, SLAs",Cost & TCO,Lever,RQ1
59,Media & Entertainment,8,Agile iteration,No keyword match,NA,NA
59,Media & Entertainment,9,Metadata tagging for archives,No keyword match,NA,NA
59,Media & Entertainment,10,Compliance,Compliance,Gate,RQ2
59,Media & Entertainment,11,License examples,Licensing clarity,Gate,RQ2
59,Media & Entertainment,12,SLA vs openness,No keyword match,NA,NA
60,Retail / eCommerce,Q5,Cost,Cost & TCO,Lever,RQ1
60,Retail / eCommerce,8,Proprietary solutions were the only one known so we took it,No keyword match,NA,NA
60,Retail / eCommerce,9,It reduce the time we are spending on creating content mainly,No keyword match,NA,NA
60,Retail / eCommerce,10,Understand how AI could help and How it could be helpful to our work,No keyword match,NA,NA
60,Retail / eCommerce,11,Nothing to add.,No keyword match,NA,NA
60,Retail / eCommerce,12,Vendor support,Support & SLAs,Lever,RQ3
61,Retail / eCommerce,Q5,Data control / privacy,Data privacy & control,Gate,RQ1
61,Retail / eCommerce,8,"We handle customer personal data at scale. Proprietary services gave us EU region hosting, SLAs, and audit-ready documentation for GDPR and security reviews, which reduced time to production.",Security posture,Gate,RQ2
61,Retail / eCommerce,8,"We handle customer personal data at scale. Proprietary services gave us EU region hosting, SLAs, and audit-ready documentation for GDPR and security reviews, which reduced time to production.",Documentation completeness,Gate,RQ2
61,Retail / eCommerce,8,"We handle customer personal data at scale. Proprietary services gave us EU region hosting, SLAs, and audit-ready documentation for GDPR and security reviews, which reduced time to production.",Customization & PEFT,Lever,RQ3
61,Retail / eCommerce,9,Not in production.,No keyword match,NA,NA
61,Retail / eCommerce,10,"DPIA preparation, data minimisation and PII redaction, uncertainty over licence terms for commercial use, and hardening self-hosted inference with proper monitoring and patching.",Observability & monitoring,Lever,RQ3
61,Retail / eCommerce,11,Org-level policy enforcement with automatic GDPR and EU AI Act documentation export.,Documentation completeness,Gate,RQ2
61,Retail / eCommerce,12,"Compliance, data residency, and security first",Compliance,Gate,RQ2
61,Retail / eCommerce,12,"Compliance, data residency, and security first",Security posture,Gate,RQ2
62,Retail / eCommerce,Q5,Scalability and cost.,Cost & TCO,Lever,RQ1
62,Retail / eCommerce,8,"Open-source AI gives us agility, cost savings, and access to vibrant collaborative communities. Proprietary AI is preferred for mission-critical systems requiring vendor reliability and compliance guarantees.",Compliance,Gate,RQ2
62,Retail / eCommerce,8,"Open-source AI gives us agility, cost savings, and access to vibrant collaborative communities. Proprietary AI is preferred for mission-critical systems requiring vendor reliability and compliance guarantees.",Cost & TCO,Lever,RQ3
62,Retail / eCommerce,9,AI-powered assistants leveraging a combination of internally built models and external large language models to enhance customer experience.,Customization & PEFT,Lever,RQ3
62,Retail / eCommerce,10,"Model adaptation to unique fashion retail data and customer preferences, brand-aligned content quality and preventing bias in AI outputs, integrating open-source tools within complex proprietary platforms, GDPR compliance in AI data handling and deployments.",Compliance,Gate,RQ2
62,Retail / eCommerce,10,"Model adaptation to unique fashion retail data and customer preferences, brand-aligned content quality and preventing bias in AI outputs, integrating open-source tools within complex proprietary platforms, GDPR compliance in AI data handling and deployments.",Customization & PEFT,Lever,RQ3
62,Retail / eCommerce,10,"Model adaptation to unique fashion retail data and customer preferences, brand-aligned content quality and preventing bias in AI outputs, integrating open-source tools within complex proprietary platforms, GDPR compliance in AI data handling and deployments.",Deployment architecture,Lever,RQ3
62,Retail / eCommerce,11,Nothing here!,No keyword match,NA,NA
62,Retail / eCommerce,12,"Innovation speed, transparency, and community benefits of open-source AI vs. the operational stability, vendor accountability, and legal assurances of proprietary solutions.",No keyword match,NA,NA
63,Retail / eCommerce,Q5,Performance,Performance sufficiency,Lever,RQ1
63,Retail / eCommerce,8,"Open-source AI may be used for experimentation, research, or components to speed up development and tap into community innovation. Proprietary solutions for specialized image generation quality, and scalability.",No keyword match,NA,NA
63,Retail / eCommerce,9,Research and rapid prototyping in content personalization and user interaction.,No keyword match,NA,NA
63,Retail / eCommerce,10,Fine-tuning models to ensure brand-consistent outputs without distortion and maintaining data privacy and compliance especially when dealing with user personalization.,Compliance,Gate,RQ2
63,Retail / eCommerce,10,Fine-tuning models to ensure brand-consistent outputs without distortion and maintaining data privacy and compliance especially when dealing with user personalization.,Data privacy & control,Gate,RQ2
63,Retail / eCommerce,11,"More templates focused on image generation, fashion trend analysis, and personalization workflows.",No keyword match,NA,NA
63,Retail / eCommerce,12,"Proprietary AI is preferred for core product engineering and customer-facing features, while open-source is valuable for pilots and augmenting capabilities.",Customization & PEFT,Lever,RQ3
64,Retail / eCommerce,Q5,"Performance, customization, deployment speed",Customization & PEFT,Lever,RQ1
64,Retail / eCommerce,8,Open-source: prototyping of our social‐media image analysis pipelines. Proprietary: managed infrastructure and vendor support for production scalability.,Support & SLAs,Lever,RQ3
64,Retail / eCommerce,9,"We leveraged open-source models to build our trend‐detection models, reducing prototype time by 50% and improving seasonal trend prediction accuracy by 12%.",No keyword match,NA,NA
64,Retail / eCommerce,10,Data compliance when scraping and processing social media imagery across jurisdictions,Compliance,Gate,RQ2
64,Retail / eCommerce,11,-,No keyword match,NA,NA
64,Retail / eCommerce,12,"We use open-source AI for R&D and proof-of-concepts, then transition successful models to proprietary, managed services when we need enterprise-grade reliability, support SLAs, and compliance assurances for production.",Compliance,Gate,RQ2
64,Retail / eCommerce,12,"We use open-source AI for R&D and proof-of-concepts, then transition successful models to proprietary, managed services when we need enterprise-grade reliability, support SLAs, and compliance assurances for production.",Support & SLAs,Lever,RQ3
65,Retail / eCommerce,Q5,Customization,Customization & PEFT,Lever,RQ1
65,Retail / eCommerce,8,"Proprietary solutions offer managed services and commercial SLAs. Open-source enables customization, transparency, and cost control.",Cost & TCO,Lever,RQ3
65,Retail / eCommerce,8,"Proprietary solutions offer managed services and commercial SLAs. Open-source enables customization, transparency, and cost control.",Customization & PEFT,Lever,RQ3
65,Retail / eCommerce,9,We integrated Hugging Face transformers into our product recommendations engine. The solution increased recommendation click-through rates and boosted average order value.,No keyword match,NA,NA
65,Retail / eCommerce,10,"Model governance and version control across multiple teams, data-privacy and compliance requirements, scaling inference infrastructure cost-effectively under variable traffic patterns.",Compliance,Gate,RQ2
65,Retail / eCommerce,10,"Model governance and version control across multiple teams, data-privacy and compliance requirements, scaling inference infrastructure cost-effectively under variable traffic patterns.",Data privacy & control,Gate,RQ2
65,Retail / eCommerce,10,"Model governance and version control across multiple teams, data-privacy and compliance requirements, scaling inference infrastructure cost-effectively under variable traffic patterns.",Governance readiness,Gate,RQ2
65,Retail / eCommerce,10,"Model governance and version control across multiple teams, data-privacy and compliance requirements, scaling inference infrastructure cost-effectively under variable traffic patterns.",Cost & TCO,Lever,RQ3
65,Retail / eCommerce,11,Advanced monitoring dashboards with custom alerting on model drift.,Customization & PEFT,Lever,RQ3
65,Retail / eCommerce,11,Advanced monitoring dashboards with custom alerting on model drift.,Observability & monitoring,Lever,RQ3
65,Retail / eCommerce,12,"> Total cost of ownership (licensing vs. support)

> Speed of integration (turnkey APIs vs. custom deployments)

> Security and compliance needs

> Long-term roadmap alignment with vendor vs. community innovation trajectory",Compliance,Gate,RQ2
65,Retail / eCommerce,12,"> Total cost of ownership (licensing vs. support)

> Speed of integration (turnkey APIs vs. custom deployments)

> Security and compliance needs

> Long-term roadmap alignment with vendor vs. community innovation trajectory",Licensing clarity,Gate,RQ2
65,Retail / eCommerce,12,"> Total cost of ownership (licensing vs. support)

> Speed of integration (turnkey APIs vs. custom deployments)

> Security and compliance needs

> Long-term roadmap alignment with vendor vs. community innovation trajectory",Security posture,Gate,RQ2
65,Retail / eCommerce,12,"> Total cost of ownership (licensing vs. support)

> Speed of integration (turnkey APIs vs. custom deployments)

> Security and compliance needs

> Long-term roadmap alignment with vendor vs. community innovation trajectory",Cost & TCO,Lever,RQ3
65,Retail / eCommerce,12,"> Total cost of ownership (licensing vs. support)

> Speed of integration (turnkey APIs vs. custom deployments)

> Security and compliance needs

> Long-term roadmap alignment with vendor vs. community innovation trajectory",Support & SLAs,Lever,RQ3
65,Retail / eCommerce,12,"> Total cost of ownership (licensing vs. support)

> Speed of integration (turnkey APIs vs. custom deployments)

> Security and compliance needs

> Long-term roadmap alignment with vendor vs. community innovation trajectory",Customization & PEFT,Lever,RQ3
65,Retail / eCommerce,12,"> Total cost of ownership (licensing vs. support)

> Speed of integration (turnkey APIs vs. custom deployments)

> Security and compliance needs

> Long-term roadmap alignment with vendor vs. community innovation trajectory",Deployment architecture,Lever,RQ3
66,Retail / eCommerce,Q5,"Cost, Performance, Support",Cost & TCO,Lever,RQ1
66,Retail / eCommerce,8,Lower TCO and flexibility,No keyword match,NA,NA
66,Retail / eCommerce,9,Product search enhancement,No keyword match,NA,NA
66,Retail / eCommerce,10,Integration with legacy stack,No keyword match,NA,NA
66,Retail / eCommerce,11,Clearer SDKs,No keyword match,NA,NA
66,Retail / eCommerce,12,Compare ROI and SLA,No keyword match,NA,NA
67,Retail / eCommerce,Q5,"Performance, Time to Value",Performance sufficiency,Lever,RQ1
67,Retail / eCommerce,8,Faster iteration,No keyword match,NA,NA
67,Retail / eCommerce,9,RAG over product manuals,No keyword match,NA,NA
67,Retail / eCommerce,10,Model updates,No keyword match,NA,NA
67,Retail / eCommerce,11,Update notifications,No keyword match,NA,NA
67,Retail / eCommerce,12,Test feasibility → Cost,Cost & TCO,Lever,RQ3
68,Retail / eCommerce,8,SLA reliability,No keyword match,NA,NA
68,Retail / eCommerce,9,Recommendation bot POC,No keyword match,NA,NA
68,Retail / eCommerce,10,Limited engineering,No keyword match,NA,NA
68,Retail / eCommerce,11,Enterprise-tier support,Support & SLAs,Lever,RQ3
68,Retail / eCommerce,12,SLA > Openness,No keyword match,NA,NA
69,Retail / eCommerce,Q5,"Cost, Customization",Cost & TCO,Lever,RQ1
69,Retail / eCommerce,8,Internalization control,No keyword match,NA,NA
69,Retail / eCommerce,9,Classification for returns,No keyword match,NA,NA
69,Retail / eCommerce,10,Model drift,No keyword match,NA,NA
69,Retail / eCommerce,11,Monitoring tools,Observability & monitoring,Lever,RQ3
69,Retail / eCommerce,12,Compare TCO vs reliability,No keyword match,NA,NA
70,Retail / eCommerce,Q5,"Performance, Customization",Customization & PEFT,Lever,RQ1
70,Retail / eCommerce,8,Domain adaptation,No keyword match,NA,NA
70,Retail / eCommerce,9,Copilot for customer queries,Customization & PEFT,Lever,RQ3
70,Retail / eCommerce,10,GPU constraints,No keyword match,NA,NA
70,Retail / eCommerce,11,Fine-tuning templates,No keyword match,NA,NA
70,Retail / eCommerce,12,Compare accuracy vs cost,Cost & TCO,Lever,RQ3
71,Technology / Software,Q5,Data control / privacy,Data privacy & control,Gate,RQ1
71,Technology / Software,8,"Customer zero, using in-house AI solution. Furthermore, security is always a concern.",Security posture,Gate,RQ2
71,Technology / Software,8,"Customer zero, using in-house AI solution. Furthermore, security is always a concern.",Customization & PEFT,Lever,RQ3
71,Technology / Software,9,"Unfortunately not, haven’t used it in the pass",No keyword match,NA,NA
71,Technology / Software,10,No open-source AI adoption,No keyword match,NA,NA
71,Technology / Software,11,Not able to share internal information,No keyword match,NA,NA
71,Technology / Software,12,No part of the decision making process,No keyword match,NA,NA
72,Technology / Software,Q5,Performance,Performance sufficiency,Lever,RQ1
72,Technology / Software,8,"We mainly use Copilot for integrated apps use cases (e.g. Copilot for Outlook, PowerPoint, Excel, search etc...) and OpenAI for custom use cases; both are used because 1) we are producing the product or have a stake in the company developing them and 2) because of the fit for purpose",Customization & PEFT,Lever,RQ3
72,Technology / Software,9,"We have customers heavily using Azure OpenAI for use cases such as chatbots, pre-triage customer service, documentation processing etc.. for example we have a client (an elevator manufacturer) which trains OpenAI on technical documentation of elevators and provides a chatbot for technicians in the field to answer questions about the specific model of elevator they need to work on",Documentation completeness,Gate,RQ2
72,Technology / Software,9,"We have customers heavily using Azure OpenAI for use cases such as chatbots, pre-triage customer service, documentation processing etc.. for example we have a client (an elevator manufacturer) which trains OpenAI on technical documentation of elevators and provides a chatbot for technicians in the field to answer questions about the specific model of elevator they need to work on",Customization & PEFT,Lever,RQ3
72,Technology / Software,10,"We see clients mostly struggle with the following:
1) Technical: AI models need to consume data and give outputs, most of the time it's not plug and play... they need to build interfaces between the AI models and their data (e.g. structured / unstructured databases, documents, SharePoint sites etc...). So putting all of this together can be challenging.
2) Chargeback: a common design pattern is to share an Azure OpenAI resource across different apps, which implies building a chargeback/measurement mechanism around the consumption of the AI models. This mechanism is frequently a source of friction for our clients.
3) Governance: many clients want to control the information their employees share with public AI services (e.g. ChatGPT) so they build their own chatbot internally, also trained on all the available ""public web"" but they run it themselves internally to ensure confidential information is not exfiltrated from the company. This is quite a common approach, and some clients even go beyond and implement some additional governance to verify their employees don't ask offensive questions etc... I personally don't understand the interest of doing this but I've seen a major watch manufacturer build this and it was clearly a source of challenge.

Also on governance: many clients want to control the information that is shared with cloud providers' AI services (e.g. Azure OpenAI service); this control is generally challenging for them to implement.",Governance readiness,Gate,RQ2
72,Technology / Software,10,"We see clients mostly struggle with the following:
1) Technical: AI models need to consume data and give outputs, most of the time it's not plug and play... they need to build interfaces between the AI models and their data (e.g. structured / unstructured databases, documents, SharePoint sites etc...). So putting all of this together can be challenging.
2) Chargeback: a common design pattern is to share an Azure OpenAI resource across different apps, which implies building a chargeback/measurement mechanism around the consumption of the AI models. This mechanism is frequently a source of friction for our clients.
3) Governance: many clients want to control the information their employees share with public AI services (e.g. ChatGPT) so they build their own chatbot internally, also trained on all the available ""public web"" but they run it themselves internally to ensure confidential information is not exfiltrated from the company. This is quite a common approach, and some clients even go beyond and implement some additional governance to verify their employees don't ask offensive questions etc... I personally don't understand the interest of doing this but I've seen a major watch manufacturer build this and it was clearly a source of challenge.

Also on governance: many clients want to control the information that is shared with cloud providers' AI services (e.g. Azure OpenAI service); this control is generally challenging for them to implement.",Documentation completeness,Gate,RQ2
72,Technology / Software,11,"I've not seen clients use HF yet, only Azure OpenAI",No keyword match,NA,NA
72,Technology / Software,12,"We don't use it ourselves but I know from clients:
1) Governance / data protection
2) Usability
3) Costs",Governance readiness,Gate,RQ2
72,Technology / Software,12,"We don't use it ourselves but I know from clients:
1) Governance / data protection
2) Usability
3) Costs",Cost & TCO,Lever,RQ3
73,Technology / Software,Q5,Performance,Performance sufficiency,Lever,RQ1
73,Technology / Software,8,Ease of use and deployment speed,Deployment architecture,Lever,RQ3
73,Technology / Software,9,Chatbot for software documentation,Documentation completeness,Gate,RQ2
73,Technology / Software,10,/,No keyword match,NA,NA
73,Technology / Software,11,/,No keyword match,NA,NA
73,Technology / Software,12,/,No keyword match,NA,NA
74,Technology / Software,Q5,Regulatory compliance,Compliance,Gate,RQ1
74,Technology / Software,8,"Data privacy, security, customer demand, employee demand",Security posture,Gate,RQ2
74,Technology / Software,8,"Data privacy, security, customer demand, employee demand",Data privacy & control,Gate,RQ2
74,Technology / Software,8,"Data privacy, security, customer demand, employee demand",Customization & PEFT,Lever,RQ3
74,Technology / Software,9,We have an internal AI tool available based on Ollama and Open Web UI. There may be more project usecases from our data and AI business line that I'm not aware of.,No keyword match,NA,NA
74,Technology / Software,10,My guess: providing the hardware necessary and justifying the cost.,Cost & TCO,Lever,RQ3
74,Technology / Software,11,maybe integrated cloud hosting or deployment solutions. Also model training capabilities to speed up processes and access to or pooling of specific training data,Deployment architecture,Lever,RQ3
74,Technology / Software,12,Usually we offer both based on customer demand. We have a big Microsoft solutions division that offers AI services through Azure. We also train models with customer data for specific use cases. It depends largely on cost and demand,Cost & TCO,Lever,RQ3
74,Technology / Software,12,Usually we offer both based on customer demand. We have a big Microsoft solutions division that offers AI services through Azure. We also train models with customer data for specific use cases. It depends largely on cost and demand,Customization & PEFT,Lever,RQ3
75,Technology / Software,Q5,Regulatory compliance,Compliance,Gate,RQ1
75,Technology / Software,8,acceleration of development,No keyword match,NA,NA
75,Technology / Software,9,nan,No keyword match,NA,NA
75,Technology / Software,10,"main challenges are legal and compliances, then integration to existing workflows",Compliance,Gate,RQ2
75,Technology / Software,11,nan,No keyword match,NA,NA
75,Technology / Software,12,compliance and security,Compliance,Gate,RQ2
75,Technology / Software,12,compliance and security,Security posture,Gate,RQ2
76,Technology / Software,Q5,Performance,Performance sufficiency,Lever,RQ1
76,Technology / Software,8,"We chose proprietary models for top performance, mature tooling, and latency SLAs for customer-facing features. We plan to expand open source for fine tuning on internal data and to reduce long-term TCO.",Performance & latency,Lever,RQ3
76,Technology / Software,8,"We chose proprietary models for top performance, mature tooling, and latency SLAs for customer-facing features. We plan to expand open source for fine tuning on internal data and to reduce long-term TCO.",Performance sufficiency,Lever,RQ3
76,Technology / Software,8,"We chose proprietary models for top performance, mature tooling, and latency SLAs for customer-facing features. We plan to expand open source for fine tuning on internal data and to reduce long-term TCO.",Customization & PEFT,Lever,RQ3
76,Technology / Software,9,We used HF models and datasets to benchmark candidate LLMs and to power a RAG prototype over product docs. The open setup let us customise retrieval and iterate quickly without exposing customer data.,Customization & PEFT,Lever,RQ3
76,Technology / Software,10,"License interpretation for commercial use, setting up reproducible eval pipelines, dependency scanning for serving images, and aligning internal OSS policies for contributions and model sharing.",Licensing clarity,Gate,RQ2
76,Technology / Software,11,nan,No keyword match,NA,NA
76,Technology / Software,12,"Security, privacy, and customer commitments.",Security posture,Gate,RQ2
76,Technology / Software,12,"Security, privacy, and customer commitments.",Data privacy & control,Gate,RQ2
76,Technology / Software,12,"Security, privacy, and customer commitments.",Customization & PEFT,Lever,RQ3
77,Technology / Software,Q5,Performance,Performance sufficiency,Lever,RQ1
77,Technology / Software,8,"Hybrid for performance and speed: proprietary for SLA backed features, HF open source to fine tune our own LLMs in cloud for cost control and avoiding lock in.",Cost & TCO,Lever,RQ3
77,Technology / Software,8,"Hybrid for performance and speed: proprietary for SLA backed features, HF open source to fine tune our own LLMs in cloud for cost control and avoiding lock in.",Performance sufficiency,Lever,RQ3
77,Technology / Software,9,Developing our own open source LLMs,No keyword match,NA,NA
77,Technology / Software,10,Integration and Support from model Provider in AI frameworks,Support & SLAs,Lever,RQ3
77,Technology / Software,11,Easier Integration into Azure as Infra stack,No keyword match,NA,NA
77,Technology / Software,12,Performance use case driven,Performance sufficiency,Lever,RQ3
78,Technology / Software,Q5,All of the above with Data control/privacy / Compliance & Risk at the forefront,Compliance,Gate,RQ1
78,Technology / Software,8,Having the broadest possible portfolio of models available to us and our customers.,Customization & PEFT,Lever,RQ3
78,Technology / Software,9,We sometimes (or our customers) use specialized OpenSource  models when specific needs arise.,Customization & PEFT,Lever,RQ3
78,Technology / Software,10,"Quality, security, compliance, etc. of the models. Basically we have our Responsible AI principles and it is very difficult to assess all models at the same level that we do for our 1st party or even 3rd party managed models.",Compliance,Gate,RQ2
78,Technology / Software,10,"Quality, security, compliance, etc. of the models. Basically we have our Responsible AI principles and it is very difficult to assess all models at the same level that we do for our 1st party or even 3rd party managed models.",Security posture,Gate,RQ2
78,Technology / Software,11,We just got it a couple of weeks ago. Hugging Face is not integrated in Azure AI Foundry ;-),No keyword match,NA,NA
78,Technology / Software,12,"Based on our Responsible AI principles, then it will depend on the use case: capability, cost, ease of use, ease of fine tuning, etc.",Cost & TCO,Lever,RQ3
79,Technology / Software,Q5,Customization,Customization & PEFT,Lever,RQ1
79,Technology / Software,8,"Eliminate single-vendor risk, fine-grained control over weights & infra, lower TCO at scale, and stronger data-sovereignty guarantees.",No keyword match,NA,NA
79,Technology / Software,9,"Automated pull-request review summaries. It now drafts ~70 % of review notes, cutting engineer PR turnaround by 32 hours per sprint.",No keyword match,NA,NA
79,Technology / Software,10,Technical: optimising GPU utilisation and memory footprint for 24/7 inference. Organisational: upskilling DevSecOps on model-license nuances. Legal: aligning Apache 2.0 & GPL-derived dependencies with customer redistribution clauses.,Licensing clarity,Gate,RQ2
79,Technology / Software,10,Technical: optimising GPU utilisation and memory footprint for 24/7 inference. Organisational: upskilling DevSecOps on model-license nuances. Legal: aligning Apache 2.0 & GPL-derived dependencies with customer redistribution clauses.,Customization & PEFT,Lever,RQ3
79,Technology / Software,11,A push-time license-compliance gate.,Compliance,Gate,RQ2
79,Technology / Software,11,A push-time license-compliance gate.,Licensing clarity,Gate,RQ2
79,Technology / Software,12,"Total cost of ownership, latency & accuracy benchmarks, licence obligations, roadmap stability, and lock-in risk.",Cost & TCO,Lever,RQ3
79,Technology / Software,12,"Total cost of ownership, latency & accuracy benchmarks, licence obligations, roadmap stability, and lock-in risk.",Performance & latency,Lever,RQ3
80,Technology / Software,Q5,Data control / privacy,Data privacy & control,Gate,RQ1
80,Technology / Software,8,"Open Source for: 1. Customisability & speed-to-experiment: we can fine-tune quickly on small, domain-specific datasets. 2. Deployment flexibility: on-prem, edge or air-gapped for nuclear and defence customers. 3. Cost transparency: no usage-based surprises during large-scale inferencing.
Proprietary services still win when we need state-of-the-art accuracy out-of-the-box or robust vendor SLAs.",Cost & TCO,Lever,RQ3
80,Technology / Software,8,"Open Source for: 1. Customisability & speed-to-experiment: we can fine-tune quickly on small, domain-specific datasets. 2. Deployment flexibility: on-prem, edge or air-gapped for nuclear and defence customers. 3. Cost transparency: no usage-based surprises during large-scale inferencing.
Proprietary services still win when we need state-of-the-art accuracy out-of-the-box or robust vendor SLAs.",Customization & PEFT,Lever,RQ3
80,Technology / Software,8,"Open Source for: 1. Customisability & speed-to-experiment: we can fine-tune quickly on small, domain-specific datasets. 2. Deployment flexibility: on-prem, edge or air-gapped for nuclear and defence customers. 3. Cost transparency: no usage-based surprises during large-scale inferencing.
Proprietary services still win when we need state-of-the-art accuracy out-of-the-box or robust vendor SLAs.",Deployment architecture,Lever,RQ3
80,Technology / Software,9,"Used on 2 M maintenance work-order logs to auto-classify failure modes and recommend remedial actions. Mean-time-to-repair dropped 8 %, saving ≈ US$3 M annually.",No keyword match,NA,NA
80,Technology / Software,10,"Technical, organisational (Upskilling engineers), legal / IP.",No keyword match,NA,NA
80,Technology / Software,11,"Industrial-Protocol Streaming Connectors to enable fast, reliable deployment of AI in industrial environments.",Deployment architecture,Lever,RQ3
80,Technology / Software,12,"Data sovereignty & privacy, total cost of ownership over 3 years, performance on domain benchmarks, regulatory & safety compliance fit, vendor / community support & roadmap.",Compliance,Gate,RQ2
80,Technology / Software,12,"Data sovereignty & privacy, total cost of ownership over 3 years, performance on domain benchmarks, regulatory & safety compliance fit, vendor / community support & roadmap.",Data privacy & control,Gate,RQ2
80,Technology / Software,12,"Data sovereignty & privacy, total cost of ownership over 3 years, performance on domain benchmarks, regulatory & safety compliance fit, vendor / community support & roadmap.",Cost & TCO,Lever,RQ3
80,Technology / Software,12,"Data sovereignty & privacy, total cost of ownership over 3 years, performance on domain benchmarks, regulatory & safety compliance fit, vendor / community support & roadmap.",Performance sufficiency,Lever,RQ3
80,Technology / Software,12,"Data sovereignty & privacy, total cost of ownership over 3 years, performance on domain benchmarks, regulatory & safety compliance fit, vendor / community support & roadmap.",Support & SLAs,Lever,RQ3
81,Technology / Software,Q5,Regulatory compliance,Compliance,Gate,RQ1
81,Technology / Software,8,"Open-source → rapid experimentation, transparent audits, fine-grained weight control.
Proprietary → turnkey scalability, specialised hardware, indemnification.
Running both provides cost leverage, bias reduction, and operational fail-over.",Cost & TCO,Lever,RQ3
81,Technology / Software,9,A module classifies >50 k contractual clauses per hour to flag regulatory risk for European banks. A fine-tuned model from Hugging Face cut review time by 73 % while boosting precision by 11 pp.,No keyword match,NA,NA
81,Technology / Software,10,"OSS license & supply-chain vetting
Silent upstream checkpoint changes (solved by pinning SHAs)
GDPR/data-residency assessments
Change-management for non-technical stakeholders",Licensing clarity,Gate,RQ2
81,Technology / Software,11,Model cards (eg. make it crystal-clear where—and where not—the model should be applied).,No keyword match,NA,NA
81,Technology / Software,12,"Compliance, Cost, Control, Performance, Vendor-Risk. Proprietary APIs lead on latency and ease, open-source wins on transparency and strategic flexibility. We keep two functionally equivalent models (one OSS, one proprietary) in production to hedge outages and policy shifts, revisiting scores quarterly.",Compliance,Gate,RQ2
81,Technology / Software,12,"Compliance, Cost, Control, Performance, Vendor-Risk. Proprietary APIs lead on latency and ease, open-source wins on transparency and strategic flexibility. We keep two functionally equivalent models (one OSS, one proprietary) in production to hedge outages and policy shifts, revisiting scores quarterly.",Cost & TCO,Lever,RQ3
81,Technology / Software,12,"Compliance, Cost, Control, Performance, Vendor-Risk. Proprietary APIs lead on latency and ease, open-source wins on transparency and strategic flexibility. We keep two functionally equivalent models (one OSS, one proprietary) in production to hedge outages and policy shifts, revisiting scores quarterly.",Performance & latency,Lever,RQ3
81,Technology / Software,12,"Compliance, Cost, Control, Performance, Vendor-Risk. Proprietary APIs lead on latency and ease, open-source wins on transparency and strategic flexibility. We keep two functionally equivalent models (one OSS, one proprietary) in production to hedge outages and policy shifts, revisiting scores quarterly.",Performance sufficiency,Lever,RQ3
82,Technology / Software,Q5,Customization,Customization & PEFT,Lever,RQ1
82,Technology / Software,8,"We needed speed to market and long-term freedom. Our abstraction layer means we can swap in open-source models without rewriting pipelines, balancing vendor lock-in risk, cost, and performance.",Cost & TCO,Lever,RQ3
82,Technology / Software,8,"We needed speed to market and long-term freedom. Our abstraction layer means we can swap in open-source models without rewriting pipelines, balancing vendor lock-in risk, cost, and performance.",Performance sufficiency,Lever,RQ3
82,Technology / Software,9,We fine-tuned a Mistral-7B Instruct model on company-specific support chats via Hugging Face. The model now auto-drafts tier-1 support replies with a 67 % reduction in handling time and zero extra API cost.,Cost & TCO,Lever,RQ3
82,Technology / Software,9,We fine-tuned a Mistral-7B Instruct model on company-specific support chats via Hugging Face. The model now auto-drafts tier-1 support replies with a 67 % reduction in handling time and zero extra API cost.,Support & SLAs,Lever,RQ3
82,Technology / Software,10,"Keeping pace with weekly model releases and benchmarks.
GPU scarcity for fine-tuning larger models.
Licensing clarity.",Licensing clarity,Gate,RQ2
82,Technology / Software,11,"Observability & experimentation, optimised infra & cost control.",Cost & TCO,Lever,RQ3
82,Technology / Software,12,"Time-to-value, total cost of ownership, privacy/compliance, and adaptability",Compliance,Gate,RQ2
82,Technology / Software,12,"Time-to-value, total cost of ownership, privacy/compliance, and adaptability",Data privacy & control,Gate,RQ2
82,Technology / Software,12,"Time-to-value, total cost of ownership, privacy/compliance, and adaptability",Cost & TCO,Lever,RQ3
83,Technology / Software,Q5,Cost,Cost & TCO,Lever,RQ1
83,Technology / Software,8,"Open-source models gave us speed and flexibility. We could inspect the code, adapt architectures, and fine-tune without licensing delays. They lowered initial costs and let us experiment broadly.

Proprietary models gave us performance and support guarantees we could not match in-house. For certain workloads they offered higher accuracy, better latency, or compliance features that reduced operational risk.

We use open-source when control, customization, or rapid iteration matters. We use proprietary when reliability, security, or competitive performance justifies the cost. The mix minimizes lock-in and maximizes delivery speed.",Compliance,Gate,RQ2
83,Technology / Software,8,"Open-source models gave us speed and flexibility. We could inspect the code, adapt architectures, and fine-tune without licensing delays. They lowered initial costs and let us experiment broadly.

Proprietary models gave us performance and support guarantees we could not match in-house. For certain workloads they offered higher accuracy, better latency, or compliance features that reduced operational risk.

We use open-source when control, customization, or rapid iteration matters. We use proprietary when reliability, security, or competitive performance justifies the cost. The mix minimizes lock-in and maximizes delivery speed.",Licensing clarity,Gate,RQ2
83,Technology / Software,8,"Open-source models gave us speed and flexibility. We could inspect the code, adapt architectures, and fine-tune without licensing delays. They lowered initial costs and let us experiment broadly.

Proprietary models gave us performance and support guarantees we could not match in-house. For certain workloads they offered higher accuracy, better latency, or compliance features that reduced operational risk.

We use open-source when control, customization, or rapid iteration matters. We use proprietary when reliability, security, or competitive performance justifies the cost. The mix minimizes lock-in and maximizes delivery speed.",Security posture,Gate,RQ2
83,Technology / Software,8,"Open-source models gave us speed and flexibility. We could inspect the code, adapt architectures, and fine-tune without licensing delays. They lowered initial costs and let us experiment broadly.

Proprietary models gave us performance and support guarantees we could not match in-house. For certain workloads they offered higher accuracy, better latency, or compliance features that reduced operational risk.

We use open-source when control, customization, or rapid iteration matters. We use proprietary when reliability, security, or competitive performance justifies the cost. The mix minimizes lock-in and maximizes delivery speed.",Cost & TCO,Lever,RQ3
83,Technology / Software,8,"Open-source models gave us speed and flexibility. We could inspect the code, adapt architectures, and fine-tune without licensing delays. They lowered initial costs and let us experiment broadly.

Proprietary models gave us performance and support guarantees we could not match in-house. For certain workloads they offered higher accuracy, better latency, or compliance features that reduced operational risk.

We use open-source when control, customization, or rapid iteration matters. We use proprietary when reliability, security, or competitive performance justifies the cost. The mix minimizes lock-in and maximizes delivery speed.",Performance & latency,Lever,RQ3
83,Technology / Software,8,"Open-source models gave us speed and flexibility. We could inspect the code, adapt architectures, and fine-tune without licensing delays. They lowered initial costs and let us experiment broadly.

Proprietary models gave us performance and support guarantees we could not match in-house. For certain workloads they offered higher accuracy, better latency, or compliance features that reduced operational risk.

We use open-source when control, customization, or rapid iteration matters. We use proprietary when reliability, security, or competitive performance justifies the cost. The mix minimizes lock-in and maximizes delivery speed.",Performance sufficiency,Lever,RQ3
83,Technology / Software,8,"Open-source models gave us speed and flexibility. We could inspect the code, adapt architectures, and fine-tune without licensing delays. They lowered initial costs and let us experiment broadly.

Proprietary models gave us performance and support guarantees we could not match in-house. For certain workloads they offered higher accuracy, better latency, or compliance features that reduced operational risk.

We use open-source when control, customization, or rapid iteration matters. We use proprietary when reliability, security, or competitive performance justifies the cost. The mix minimizes lock-in and maximizes delivery speed.",Support & SLAs,Lever,RQ3
83,Technology / Software,8,"Open-source models gave us speed and flexibility. We could inspect the code, adapt architectures, and fine-tune without licensing delays. They lowered initial costs and let us experiment broadly.

Proprietary models gave us performance and support guarantees we could not match in-house. For certain workloads they offered higher accuracy, better latency, or compliance features that reduced operational risk.

We use open-source when control, customization, or rapid iteration matters. We use proprietary when reliability, security, or competitive performance justifies the cost. The mix minimizes lock-in and maximizes delivery speed.",Customization & PEFT,Lever,RQ3
83,Technology / Software,9,"We used open-source AI to power semantic search in our customer support knowledge base. Fine-tuning it on our historical support tickets let users find answers by intent rather than exact keywords.

Result: support ticket deflection increased 28%, average resolution time dropped by over a third, and we avoided vendor lock-in while keeping the model fully under our control for future domain adaptation.",Support & SLAs,Lever,RQ3
83,Technology / Software,9,"We used open-source AI to power semantic search in our customer support knowledge base. Fine-tuning it on our historical support tickets let users find answers by intent rather than exact keywords.

Result: support ticket deflection increased 28%, average resolution time dropped by over a third, and we avoided vendor lock-in while keeping the model fully under our control for future domain adaptation.",Customization & PEFT,Lever,RQ3
83,Technology / Software,10,Model size and inference costs required heavy optimization to meet latency targets. Documentation and prebuilt tooling were inconsistent across projects.,Documentation completeness,Gate,RQ2
83,Technology / Software,10,Model size and inference costs required heavy optimization to meet latency targets. Documentation and prebuilt tooling were inconsistent across projects.,Cost & TCO,Lever,RQ3
83,Technology / Software,10,Model size and inference costs required heavy optimization to meet latency targets. Documentation and prebuilt tooling were inconsistent across projects.,Performance & latency,Lever,RQ3
83,Technology / Software,11,"More granular, policy-based authorization than the current role and resource-group model.",No keyword match,NA,NA
83,Technology / Software,12,"Performance, control, flexibility, cost, risk & compliance.",Compliance,Gate,RQ2
83,Technology / Software,12,"Performance, control, flexibility, cost, risk & compliance.",Cost & TCO,Lever,RQ3
83,Technology / Software,12,"Performance, control, flexibility, cost, risk & compliance.",Performance sufficiency,Lever,RQ3
84,Technology / Software,Q5,Cost,Cost & TCO,Lever,RQ1
84,Technology / Software,8,"For open-source models: cost efficiency, customization needs, vendor independence. For proprietary AI: specialized capabilities (multimodal processing work).",Cost & TCO,Lever,RQ3
84,Technology / Software,8,"For open-source models: cost efficiency, customization needs, vendor independence. For proprietary AI: specialized capabilities (multimodal processing work).",Customization & PEFT,Lever,RQ3
84,Technology / Software,9,"To fine-tune a customer support ticket classification system that reduced our daily triage time from 3 hours to 20 minutes while saving $180K annually compared to proprietary solutions, achieving 92% accuracy on our domain-specific fintech support data.",Support & SLAs,Lever,RQ3
84,Technology / Software,9,"To fine-tune a customer support ticket classification system that reduced our daily triage time from 3 hours to 20 minutes while saving $180K annually compared to proprietary solutions, achieving 92% accuracy on our domain-specific fintech support data.",Customization & PEFT,Lever,RQ3
84,Technology / Software,10,"Our biggest challenge was the steep learning curve and resource investment required to fine-tune, deploy, and maintain open-source models in production, which initially stretched our small engineering team thin compared to the plug-and-play nature of proprietary APIs.",Deployment architecture,Lever,RQ3
84,Technology / Software,11,More granular pricing tiers between the free tier and full enterprise.,No keyword match,NA,NA
84,Technology / Software,12,"We use a decision matrix weighing cost per inference, required accuracy thresholds, data sensitivity, time-to-deployment, and maintenance overhead.",Cost & TCO,Lever,RQ3
84,Technology / Software,12,"We use a decision matrix weighing cost per inference, required accuracy thresholds, data sensitivity, time-to-deployment, and maintenance overhead.",Deployment architecture,Lever,RQ3
85,Technology / Software,Q5,Cost,Cost & TCO,Lever,RQ1
85,Technology / Software,8,"Cost-effectiveness, rapid prototyping and experimentation, community support and collaboration.",Cost & TCO,Lever,RQ3
85,Technology / Software,8,"Cost-effectiveness, rapid prototyping and experimentation, community support and collaboration.",Support & SLAs,Lever,RQ3
85,Technology / Software,9,"We built a scalable and accurate sentiment analysis solution, which delivered significant value by automating our analysis, improving customer experience, and enabling data-driven decision-making, all while reducing costs and resources.",Cost & TCO,Lever,RQ3
85,Technology / Software,9,"We built a scalable and accurate sentiment analysis solution, which delivered significant value by automating our analysis, improving customer experience, and enabling data-driven decision-making, all while reducing costs and resources.",Customization & PEFT,Lever,RQ3
85,Technology / Software,10,"Ensuring compliance with regulatory requirements, managing intellectual property and licensing agreements, and addressing potential compatibility and scalability issues with our existing infrastructure and proprietary systems when adopting open-source AI solutions.",Compliance,Gate,RQ2
85,Technology / Software,10,"Ensuring compliance with regulatory requirements, managing intellectual property and licensing agreements, and addressing potential compatibility and scalability issues with our existing infrastructure and proprietary systems when adopting open-source AI solutions.",Licensing clarity,Gate,RQ2
85,Technology / Software,11,"Improved licensing models, enhanced security and governance controls, and more comprehensive documentation.",Licensing clarity,Gate,RQ2
85,Technology / Software,11,"Improved licensing models, enhanced security and governance controls, and more comprehensive documentation.",Security posture,Gate,RQ2
85,Technology / Software,11,"Improved licensing models, enhanced security and governance controls, and more comprehensive documentation.",Governance readiness,Gate,RQ2
85,Technology / Software,11,"Improved licensing models, enhanced security and governance controls, and more comprehensive documentation.",Documentation completeness,Gate,RQ2
85,Technology / Software,12,"We evaluate the trade-offs between open-source and proprietary options by considering factors such as cost, customization, scalability, security, and community support, as well as our business goals, risk tolerance, and internal expertise, to determine the best fit for each project or initiative.",Security posture,Gate,RQ2
85,Technology / Software,12,"We evaluate the trade-offs between open-source and proprietary options by considering factors such as cost, customization, scalability, security, and community support, as well as our business goals, risk tolerance, and internal expertise, to determine the best fit for each project or initiative.",Cost & TCO,Lever,RQ3
85,Technology / Software,12,"We evaluate the trade-offs between open-source and proprietary options by considering factors such as cost, customization, scalability, security, and community support, as well as our business goals, risk tolerance, and internal expertise, to determine the best fit for each project or initiative.",Support & SLAs,Lever,RQ3
85,Technology / Software,12,"We evaluate the trade-offs between open-source and proprietary options by considering factors such as cost, customization, scalability, security, and community support, as well as our business goals, risk tolerance, and internal expertise, to determine the best fit for each project or initiative.",Customization & PEFT,Lever,RQ3
86,Technology / Software,Q5,Customization,Customization & PEFT,Lever,RQ1
86,Technology / Software,8,"We value open-source for flexibility, transparency, and avoiding vendor lock-in. Proprietary models are used when they offer unique capabilities or higher performance in specific contexts.",Performance sufficiency,Lever,RQ3
86,Technology / Software,9,"Orchestrated multiple open-source LLMs in real-time, enabling generalized multi-agent conversation outputs that improved both diversity and robustness of results.",No keyword match,NA,NA
86,Technology / Software,10,"model hosting scalability, aligning inference performance with proprietary systems, and managing model version changes in open-source projects.",Performance sufficiency,Lever,RQ3
86,Technology / Software,11,More detailed enterprise-grade model cards.,No keyword match,NA,NA
86,Technology / Software,12,"We assess trade-offs by weighing adaptability, transparency, and cost (open-source) against integration ease, reliability, and specialized capabilities (proprietary). Our abstraction layer minimizes switching costs, enabling a mixed-strategy approach.",Cost & TCO,Lever,RQ3
87,Technology / Software,Q5,"Performance, Flexibility, Community",Performance sufficiency,Lever,RQ1
87,Technology / Software,8,"Transparency, control, collaboration",No keyword match,NA,NA
87,Technology / Software,9,Text generation models integrated in production,No keyword match,NA,NA
87,Technology / Software,10,"Version control, scaling",No keyword match,NA,NA
87,Technology / Software,11,Enterprise-grade fine-tuning API,No keyword match,NA,NA
87,Technology / Software,12,Performance > Cost,Cost & TCO,Lever,RQ3
87,Technology / Software,12,Performance > Cost,Performance sufficiency,Lever,RQ3
88,Technology / Software,Q5,"Performance, Governance, Security",Performance sufficiency,Lever,RQ1
88,Technology / Software,8,Vendor independence,No keyword match,NA,NA
88,Technology / Software,9,Custom LLM for internal knowledge base,Customization & PEFT,Lever,RQ3
88,Technology / Software,10,Policy compliance,Compliance,Gate,RQ2
88,Technology / Software,11,Role-based access control,No keyword match,NA,NA
88,Technology / Software,12,Balance governance vs innovation,Governance readiness,Gate,RQ2
89,Technology / Software,Q5,"Cost, Customization, Ecosystem",Cost & TCO,Lever,RQ1
89,Technology / Software,8,Freedom to innovate,No keyword match,NA,NA
89,Technology / Software,9,Multi-language classification pipeline,No keyword match,NA,NA
89,Technology / Software,10,Maintaining dependencies,No keyword match,NA,NA
89,Technology / Software,11,Long-term maintenance support,Support & SLAs,Lever,RQ3
89,Technology / Software,12,Openness before SLA,No keyword match,NA,NA
90,Technology / Software,Q5,"Performance, Ecosystem, Integration",Performance sufficiency,Lever,RQ1
90,Technology / Software,8,Open innovation,No keyword match,NA,NA
90,Technology / Software,9,Multimodal search system,No keyword match,NA,NA
90,Technology / Software,10,Integration with legacy apps,No keyword match,NA,NA
90,Technology / Software,11,Simplified API monitoring,Observability & monitoring,Lever,RQ3
90,Technology / Software,12,Cost-performance equilibrium,Cost & TCO,Lever,RQ3
90,Technology / Software,12,Cost-performance equilibrium,Performance sufficiency,Lever,RQ3
91,Telecommunications,Q5,Regulatory compliance,Compliance,Gate,RQ1
91,Telecommunications,8,"Exploring new markets, we are building AI platform for B2B customers.",Customization & PEFT,Lever,RQ3
91,Telecommunications,9,Company provides internal LLM models to get help in everyday tasks. Github copilot speeds up developers in implementations and reasoning.,No keyword match,NA,NA
91,Telecommunications,10,"As in many big enterprise companies, the organisational and legal overhead slows down project execution.",No keyword match,NA,NA
91,Telecommunications,11,Hard to say,No keyword match,NA,NA
91,Telecommunications,12,"Currently, we're mainly focused on open-source models when selling AI solutions, we use proprietary models whenever they are already integrated in a product we use, e.g. Github Copilot.",No keyword match,NA,NA
92,Telecommunications,Q5,Data control / privacy,Data privacy & control,Gate,RQ1
92,Telecommunications,8,"Scalability, flexibility, security, and maintaining compliance while optimizing costs.",Compliance,Gate,RQ2
92,Telecommunications,8,"Scalability, flexibility, security, and maintaining compliance while optimizing costs.",Security posture,Gate,RQ2
92,Telecommunications,8,"Scalability, flexibility, security, and maintaining compliance while optimizing costs.",Cost & TCO,Lever,RQ3
92,Telecommunications,9,"We have used Hugging Face to develop multilingual customer support bots, resulting in faster response times and greater customer satisfaction.",Support & SLAs,Lever,RQ3
92,Telecommunications,9,"We have used Hugging Face to develop multilingual customer support bots, resulting in faster response times and greater customer satisfaction.",Customization & PEFT,Lever,RQ3
92,Telecommunications,10,"Integrating open-source AI with existing legacy systems, ensuring robust data privacy and security, and navigating a complex regulatory environment.",Security posture,Gate,RQ2
92,Telecommunications,10,"Integrating open-source AI with existing legacy systems, ensuring robust data privacy and security, and navigating a complex regulatory environment.",Data privacy & control,Gate,RQ2
92,Telecommunications,11,"Enhanced technical support, stronger security guarantees, better integration tools, and certification for regulatory compliance.",Compliance,Gate,RQ2
92,Telecommunications,11,"Enhanced technical support, stronger security guarantees, better integration tools, and certification for regulatory compliance.",Security posture,Gate,RQ2
92,Telecommunications,11,"Enhanced technical support, stronger security guarantees, better integration tools, and certification for regulatory compliance.",Support & SLAs,Lever,RQ3
92,Telecommunications,12,"By weighing the flexibility and cost benefits of open-source against the managed services, reliability, and vendor support of proprietary offerings.",Cost & TCO,Lever,RQ3
92,Telecommunications,12,"By weighing the flexibility and cost benefits of open-source against the managed services, reliability, and vendor support of proprietary offerings.",Support & SLAs,Lever,RQ3
93,Telecommunications,Q5,Performance,Performance sufficiency,Lever,RQ1
93,Telecommunications,8,Proprietary AI and in-house developed solutions are preferred for control and performance reliability. Open-source AI is considered for pilot projects but with caution regarding integration and support.,Performance sufficiency,Lever,RQ3
93,Telecommunications,8,Proprietary AI and in-house developed solutions are preferred for control and performance reliability. Open-source AI is considered for pilot projects but with caution regarding integration and support.,Support & SLAs,Lever,RQ3
93,Telecommunications,9,Possibly limited but may include customer support automation.,Support & SLAs,Lever,RQ3
93,Telecommunications,9,Possibly limited but may include customer support automation.,Customization & PEFT,Lever,RQ3
93,Telecommunications,10,"Integration complexity with specialized telecom hardware and software.
Data security and compliance considerations.",Compliance,Gate,RQ2
93,Telecommunications,10,"Integration complexity with specialized telecom hardware and software.
Data security and compliance considerations.",Security posture,Gate,RQ2
93,Telecommunications,11,More specialized signal processing resources.,No keyword match,NA,NA
93,Telecommunications,12,"The evaluation prioritizes performance, reliability, and control, favoring proprietary or in-house AI for core functions. Open-source AI is valuable for non-critical, exploratory, or supplementary use cases where rapid innovation is advantageous and resource constraints exist.",Performance sufficiency,Lever,RQ3
94,Telecommunications,Q5,Data control / privacy,Data privacy & control,Gate,RQ1
94,Telecommunications,8,Proprietary AI is favored for core operational and network management systems due to reliability and vendor support. Open-source AI is leveraged for rapid experimentation.,Support & SLAs,Lever,RQ3
94,Telecommunications,9,"Exploratory and pilot use in NLP for customer service automation, chatbots, and data analytics as part of innovation initiatives at the Innovation Lab.",Customization & PEFT,Lever,RQ3
94,Telecommunications,10,"Integrating with complex telecom infrastructure and legacy systems.
Navigating regulatory and compliance requirements around data security.
Building and maintaining AI expertise in-house.
Ensuring operational stability and risk mitigation in critical network services.",Compliance,Gate,RQ2
94,Telecommunications,10,"Integrating with complex telecom infrastructure and legacy systems.
Navigating regulatory and compliance requirements around data security.
Building and maintaining AI expertise in-house.
Ensuring operational stability and risk mitigation in critical network services.",Security posture,Gate,RQ2
94,Telecommunications,11,More specialized documentation and use cases tailored to telecom signal processing and 6G network.,Documentation completeness,Gate,RQ2
94,Telecommunications,12,"Flexibility and innovation speed of open-source AI with the reliability, compliance, and vendor support of proprietary solutions. Critical network infrastructure relies on proprietary systems, while open-source AI is embraced in labs and pilot projects to drive future digital transformation and sustainability.",Compliance,Gate,RQ2
94,Telecommunications,12,"Flexibility and innovation speed of open-source AI with the reliability, compliance, and vendor support of proprietary solutions. Critical network infrastructure relies on proprietary systems, while open-source AI is embraced in labs and pilot projects to drive future digital transformation and sustainability.",Support & SLAs,Lever,RQ3
95,Telecommunications,Q5,Regulatory compliance,Compliance,Gate,RQ1
95,Telecommunications,8,"We handle network telemetry and personal data. We keep retrieval and fine tuning in our virtual private cloud to meet GDPR and data residency, and use a managed API for select copilots with SLAs. This balances auditability, control, and speed.",No keyword match,NA,NA
95,Telecommunications,9,"Retrieval over incident runbooks and knowledge base articles using HF in our VPC, with a vendor LLM for response drafting. We saw higher first contact resolution and shorter time to resolve incidents.",No keyword match,NA,NA
95,Telecommunications,10,"Legal review of model and dataset licences for commercial use, hardening inference endpoints.",No keyword match,NA,NA
95,Telecommunications,11,"Risk-register templates mapped to GDPR and the EU AI Act transparency duties, with links to model cards",No keyword match,NA,NA
95,Telecommunications,12,"Compliance and privacy first, then security and support readiness. If those are satisfied, we compare task performance and lifecycle cost.",Compliance,Gate,RQ2
95,Telecommunications,12,"Compliance and privacy first, then security and support readiness. If those are satisfied, we compare task performance and lifecycle cost.",Security posture,Gate,RQ2
95,Telecommunications,12,"Compliance and privacy first, then security and support readiness. If those are satisfied, we compare task performance and lifecycle cost.",Data privacy & control,Gate,RQ2
95,Telecommunications,12,"Compliance and privacy first, then security and support readiness. If those are satisfied, we compare task performance and lifecycle cost.",Cost & TCO,Lever,RQ3
95,Telecommunications,12,"Compliance and privacy first, then security and support readiness. If those are satisfied, we compare task performance and lifecycle cost.",Performance sufficiency,Lever,RQ3
95,Telecommunications,12,"Compliance and privacy first, then security and support readiness. If those are satisfied, we compare task performance and lifecycle cost.",Support & SLAs,Lever,RQ3
96,Telecommunications,Q5,"Cost, Performance",Cost & TCO,Lever,RQ1
96,Telecommunications,8,Avoid API cost,Cost & TCO,Lever,RQ3
96,Telecommunications,9,Log summarisation,No keyword match,NA,NA
96,Telecommunications,10,Licensing clarity,Licensing clarity,Gate,RQ2
96,Telecommunications,11,Repository rating,No keyword match,NA,NA
96,Telecommunications,12,Compare ROI,No keyword match,NA,NA
97,Telecommunications,Q5,"Performance, SLAs",Performance sufficiency,Lever,RQ1
97,Telecommunications,8,Reliable delivery,No keyword match,NA,NA
97,Telecommunications,9,Chatbot for tech support,Support & SLAs,Lever,RQ3
97,Telecommunications,10,Integration,No keyword match,NA,NA
97,Telecommunications,11,HF connector kits,No keyword match,NA,NA
97,Telecommunications,12,SLA before openness,No keyword match,NA,NA
98,Telecommunications,Q5,"Customization, Support",Customization & PEFT,Lever,RQ1
98,Telecommunications,8,Adaptability,No keyword match,NA,NA
98,Telecommunications,9,Customer ticket triage,Customization & PEFT,Lever,RQ3
98,Telecommunications,10,Tooling,No keyword match,NA,NA
98,Telecommunications,11,Workflow templates,No keyword match,NA,NA
98,Telecommunications,12,Balance support vs autonomy,Support & SLAs,Lever,RQ3
99,Telecommunications,Q5,"Performance, Cost",Cost & TCO,Lever,RQ1
99,Telecommunications,8,Cost-effective pipeline,Cost & TCO,Lever,RQ3
99,Telecommunications,9,Internal Q&A bot,No keyword match,NA,NA
99,Telecommunications,10,Limited resources,No keyword match,NA,NA
99,Telecommunications,11,Step-by-step MLOps guide,No keyword match,NA,NA
99,Telecommunications,12,Feasibility → ROI,No keyword match,NA,NA
100,Telecommunications,Q5,"Performance, Compliance",Compliance,Gate,RQ1
100,Telecommunications,8,Data control,No keyword match,NA,NA
100,Telecommunications,9,Intent detection model,No keyword match,NA,NA
100,Telecommunications,10,Security,Security posture,Gate,RQ2
100,Telecommunications,11,Compliance docs,Compliance,Gate,RQ2
100,Telecommunications,12,Feasibility → SLA,No keyword match,NA,NA