itseffi/epfl-enterprise-osai-adoption-research-data huggingface dataset:
Industry: Healthcare / Biotech / Life Sciences
Survey question: 9
Response: 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.
Code: Support & SLAs
Type: Lever
Research question: RQ3
Industry: Healthcare / Biotech / Life Sciences
Survey question: 10
Response: Challenges include ensuring compliance with healthcare regulations and addressing data security and confidentiality concerns.
Code: Compliance
Type: Gate
Research question: RQ2
Code: Security posture
Type: Gate
Research question: RQ2
Industry: Healthcare / Biotech / Life Sciences
Survey question: 11
Response: Dedicated enterprise support.
Code: Support & SLAs
Type: Lever
Research question: RQ3
Industry: Healthcare / Biotech / Life Sciences
Survey question: 12
Response: The evaluation focuses on customization and cost benefits of open-source versus reliability, vendor support, and compliance assurances of proprietary solutions.
Code: Compliance
Type: Gate
Research question: RQ2
Code: Cost & TCO
Type: Lever
Research question: RQ3
Code: Support & SLAs
Type: Lever
Research question: RQ3
Code: Customization & PEFT
Type: Lever
Research question: RQ3
Industry: Healthcare / Biotech / Life Sciences
Survey question: 8
Response: Scientific progress, reproducibility, and network effects that benefit the entire community.
Code: No keyword match
Type: NA
Research question: NA
Industry: Healthcare / Biotech / Life Sciences
Survey question: 9
Response: Our flagship model created substantial value by demonstrating our capabilities to potential enterprise customers, generating research collaborations, and establishing our reputation in the industry.
Code: Customization & PEFT
Type: Lever
Research question: RQ3
Industry: Healthcare / Biotech / Life Sciences
Survey question: 10
Response: Data aggregation, talent, complex data licensing.
Code: Licensing clarity
Type: Gate
Research question: RQ2
Industry: Healthcare / Biotech / Life Sciences
Survey question: 11
Response: Enhanced model cards.
Code: No keyword match
Type: NA
Research question: NA
Industry: Healthcare / Biotech / Life Sciences
Survey question: 12
Response: Open-source: reproducibility, community validation, and broader adoption.
Code: No keyword match
Type: NA
Research question: NA
Industry: Healthcare / Biotech / Life Sciences
Survey question: Q5
Response: Regulatory compliance
Code: Compliance
Type: Gate
Research question: RQ1
Industry: Healthcare / Biotech / Life Sciences
Survey question: 8
Response: Innovation speed from open-source and reliability/compliance from proprietary AI.
Code: Compliance
Type: Gate
Research question: RQ2
Industry: Healthcare / Biotech / Life Sciences
Survey question: 9
Response: 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.
Code: Documentation completeness
Type: Gate
Research question: RQ2
Code: Observability & monitoring
Type: Lever
Research question: RQ3
Industry: Healthcare / Biotech / Life Sciences
Survey question: 10
Response: 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.
Code: Data privacy & control
Type: Gate
Research question: RQ2
Industry: Healthcare / Biotech / Life Sciences
Survey question: 11
Response: More robust enterprise-grade security features and audit trails.
Code: Security posture
Type: Gate
Research question: RQ2
Industry: Healthcare / Biotech / Life Sciences
Survey question: 12
Response: Regulatory compliance requirements, performance for specialised medical tasks, and total cost of ownership.
Code: Compliance
Type: Gate
Research question: RQ2
Code: Cost & TCO
Type: Lever
Research question: RQ3
Code: Performance sufficiency
Type: Lever
Research question: RQ3
Industry: Healthcare / Biotech / Life Sciences
Survey question: 8
Response: 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.
Code: Security posture
Type: Gate
Research question: RQ2
Code: Deployment architecture
Type: Lever
Research question: RQ3
Industry: Healthcare / Biotech / Life Sciences
Survey question: 9
Response: 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.
Code: No keyword match
Type: NA
Research question: NA
Industry: Healthcare / Biotech / Life Sciences
Survey question: 10
Response: - 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.
Code: No keyword match
Type: NA
Research question: NA
Industry: Healthcare / Biotech / Life Sciences
Survey question: 11
Response: Turnkey, life-sciences-specific model card templates with provenance and validation sections.
Code: No keyword match
Type: NA
Research question: NA
Industry: Healthcare / Biotech / Life Sciences
Survey question: 12
Response: 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.
Code: Compliance
Type: Gate
Research question: RQ2
Code: Cost & TCO
Type: Lever
Research question: RQ3
Code: Performance sufficiency
Type: Lever
Research question: RQ3
Industry: Healthcare / Biotech / Life Sciences
Survey question: Q5
Response: Compliance, Data Control
Code: Compliance
Type: Gate
Research question: RQ1
Industry: Healthcare / Biotech / Life Sciences
Survey question: 8
Response: Data protection, patient privacy
Code: Data privacy & control
Type: Gate
Research question: RQ2
Industry: Healthcare / Biotech / Life Sciences
Survey question: 9
Response: Prototype de-identification workflow
Code: No keyword match
Type: NA
Research question: NA
Industry: Healthcare / Biotech / Life Sciences
Survey question: 10
Response: Documentation and traceability
Code: Documentation completeness
Type: Gate
Research question: RQ2
Industry: Healthcare / Biotech / Life Sciences
Survey question: 11
Response: Health-specific compliance templates
Code: Compliance
Type: Gate
Research question: RQ2
Industry: Healthcare / Biotech / Life Sciences
Survey question: 12
Response: GDPR first, then performance
Code: Performance sufficiency
Type: Lever
Research question: RQ3
Industry: Healthcare / Biotech / Life Sciences
Survey question: Q5
Response: Compliance, Documentation
Code: Compliance
Type: Gate
Research question: RQ1
Industry: Healthcare / Biotech / Life Sciences
Survey question: 8
Response: Transparency, traceability
Code: No keyword match
Type: NA
Research question: NA
Industry: Healthcare / Biotech / Life Sciences
Survey question: 9
Response: Planning for medical text summarisation
Code: No keyword match
Type: NA
Research question: NA
Industry: Healthcare / Biotech / Life Sciences
Survey question: 10
Response: License ambiguity
Code: Licensing clarity
Type: Gate
Research question: RQ2
Industry: Healthcare / Biotech / Life Sciences
Survey question: 11
Response: Legal templates and clearer model cards
Code: No keyword match
Type: NA
Research question: NA
Industry: Healthcare / Biotech / Life Sciences
Survey question: 12
Response: Governance filter, no shortcuts
Code: Governance readiness
Type: Gate
Research question: RQ2
Industry: Healthcare / Biotech / Life Sciences
Survey question: Q5
Response: Cost, Support, Time to Value
Code: Cost & TCO
Type: Lever
Research question: RQ1
Industry: Healthcare / Biotech / Life Sciences
Survey question: 8
Response: Fine-tuning capability
Code: No keyword match
Type: NA
Research question: NA
Industry: Healthcare / Biotech / Life Sciences
Survey question: 9
Response: Annotating patient feedback for triage
Code: No keyword match
Type: NA
Research question: NA
Industry: Healthcare / Biotech / Life Sciences
Survey question: 10
Response: Model reliability
Code: No keyword match
Type: NA
Research question: NA
Industry: Healthcare / Biotech / Life Sciences
Survey question: 11
Response: Enterprise SLAs
Code: No keyword match
Type: NA
Research question: NA
Industry: Healthcare / Biotech / Life Sciences
Survey question: 12
Response: Feasibility check → Compare cost
Code: Cost & TCO
Type: Lever
Research question: RQ3
Industry: Hospitality and Tourism Industry
Survey question: Q5
Response: Performance
Code: Performance sufficiency
Type: Lever
Research question: RQ1
Industry: Hospitality and Tourism Industry
Survey question: 8
Response: Reliability, brand safety, client compliance needs, and integration with content and production systems.
Code: Compliance
Type: Gate
Research question: RQ2
Industry: Hospitality and Tourism Industry
Survey question: 9
Response: Currently none
Code: No keyword match
Type: NA
Research question: NA
Industry: Hospitality and Tourism Industry
Survey question: 10
Response: Governance/compliance, brand-safety controls for generated content, and integration with production/logistics/content systems.
Code: Compliance
Type: Gate
Research question: RQ2
Code: Governance readiness
Type: Gate
Research question: RQ2
Industry: Hospitality and Tourism Industry
Survey question: 11
Response: Enriched model cards.
Code: No keyword match
Type: NA
Research question: NA
Industry: Hospitality and Tourism Industry
Survey question: 12
Response: Reliability, brand safety, compliance, and support (proprietary) against flexibility and cost (open-source).
Code: Compliance
Type: Gate
Research question: RQ2
Code: Cost & TCO
Type: Lever
Research question: RQ3
Code: Support & SLAs
Type: Lever
Research question: RQ3
Industry: Hospitality and Tourism Industry
Survey question: Q5
Response: Cost, Time to Value
Code: Cost & TCO
Type: Lever
Research question: RQ1
Industry: Hospitality and Tourism Industry
Survey question: 8
Response: Ease of entry
Code: No keyword match
Type: NA
Research question: NA
Industry: Hospitality and Tourism Industry
Survey question: 9
Response: RAG chatbot for guest FAQs
Code: No keyword match
Type: NA
Research question: NA
Industry: Hospitality and Tourism Industry
Survey question: 10
Response: Lack of skilled staff
Code: No keyword match
Type: NA
Research question: NA
Industry: Hospitality and Tourism Industry
Survey question: 11
Response: Simple deployment SDKs
Code: Deployment architecture
Type: Lever
Research question: RQ3
Industry: Hospitality and Tourism Industry
Survey question: 12
Response: Feasibility before ROI
Code: No keyword match
Type: NA
Research question: NA
Industry: Hospitality and Tourism Industry
Survey question: Q5
Response: Support, Compliance
Code: Compliance
Type: Gate
Research question: RQ1
Industry: Hospitality and Tourism Industry
Survey question: 8
Response: Data ownership and flexibility
Code: No keyword match
Type: NA
Research question: NA
Industry: Hospitality and Tourism Industry
Survey question: 9
Response: Internal knowledge base assistant
Code: No keyword match
Type: NA
Research question: NA
Industry: Hospitality and Tourism Industry
Survey question: 10
Response: Limited documentation
Code: Documentation completeness
Type: Gate
Research question: RQ2
Industry: Hospitality and Tourism Industry
Survey question: 11
Response: Step-by-step guides
Code: No keyword match
Type: NA
Research question: NA
Industry: Hospitality and Tourism Industry
Survey question: 12
Response: Security and GDPR checks first
Code: Security posture
Type: Gate
Research question: RQ2
Industry: Hospitality and Tourism Industry
Survey question: Q5
Response: Data Privacy, Performance
Code: Data privacy & control
Type: Gate
Research question: RQ1
Industry: Hospitality and Tourism Industry
Survey question: 8
Response: Better customer experience
Code: Customization & PEFT
Type: Lever
Research question: RQ3
Industry: Hospitality and Tourism Industry
Survey question: 9
Response: Pilot chatbot in sandbox
Code: No keyword match
Type: NA
Research question: NA
Industry: Hospitality and Tourism Industry
Survey question: 10
Response: Integration complexity
Code: No keyword match
Type: NA
Research question: NA
Industry: Hospitality and Tourism Industry
Survey question: 11
Response: Clearer hosting options
Code: No keyword match
Type: NA
Research question: NA
Industry: Hospitality and Tourism Industry
Survey question: 12
Response: Privacy and cost
Code: Data privacy & control
Type: Gate
Research question: RQ2
Code: Cost & TCO
Type: Lever
Research question: RQ3
Industry: Manufacturing / Industrial
Survey question: Q5
Response: Regulatory compliance
Code: Compliance
Type: Gate
Research question: RQ1
Industry: Manufacturing / Industrial
Survey question: 8
Response: We have not adopted either yet. Compliance and supplier-assurance gates come first
Code: Compliance
Type: Gate
Research question: RQ2
Industry: Manufacturing / Industrial
Survey question: 9
Response: Currently none.
Code: No keyword match
Type: NA
Research question: NA
Industry: Manufacturing / Industrial
Survey question: 10
Response: Technical and organisational.
Code: No keyword match
Type: NA
Research question: NA
Industry: Manufacturing / Industrial
Survey question: 11
Response: Not sure yet.
Code: No keyword match
Type: NA
Research question: NA
Industry: Manufacturing / Industrial
Survey question: 12
Response: Compliance and safety first, then security and support
Code: Compliance
Type: Gate
Research question: RQ2
Code: Security posture
Type: Gate
Research question: RQ2
Code: Support & SLAs
Type: Lever
Research question: RQ3
Industry: Manufacturing / Industrial
Survey question: Q5
Response: Data control / privacy
Code: Data privacy & control
Type: Gate
Research question: RQ1
Industry: Manufacturing / Industrial
Survey question: 8
Response: Productivity increase
Code: No keyword match
Type: NA
Research question: NA
Industry: Manufacturing / Industrial
Survey question: 9
Response: 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.
Code: No keyword match
Type: NA
Research question: NA
Industry: Manufacturing / Industrial
Survey question: 10
Response: Fear about data confidentiality
Code: No keyword match
Type: NA
Research question: NA
Industry: Manufacturing / Industrial
Survey question: 11
Response: nan
Code: No keyword match
Type: NA
Research question: NA
Industry: Manufacturing / Industrial
Survey question: 12
Response: Data confidentiality, IP protection, and GDPR compliance first. If those pass, we assess security and support, then task performance and total cost of ownership.
Code: Compliance
Type: Gate
Research question: RQ2
Code: Security posture
Type: Gate
Research question: RQ2
Code: Cost & TCO
Type: Lever
Research question: RQ3
Code: Performance sufficiency
Type: Lever
Research question: RQ3
Code: Support & SLAs
Type: Lever
Research question: RQ3
Industry: Manufacturing / Industrial
Survey question: 8
Response: 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.
Code: No keyword match
Type: NA
Research question: NA
Industry: Manufacturing / Industrial
Survey question: 9
Response: 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%.
Code: No keyword match
Type: NA
Research question: NA
Industry: Manufacturing / Industrial
Survey question: 10
Response: Compliance with EASA certification requirements, intellectual-property and export-control restrictions, aligning cross-functional processes between R&D, engineering, and safety authorities.
Code: Compliance
Type: Gate
Research question: RQ2
Industry: Manufacturing / Industrial
Survey question: 11
Response: Built-in audit trails.
Code: No keyword match
Type: NA
Research question: NA
Industry: Manufacturing / Industrial
Survey question: 12
Response: Innovation speed and flexibility (open-source) against the need for formal certification, traceability, and vendor accountability (proprietary).
Code: No keyword match
Type: NA
Research question: NA
Industry: Manufacturing / Industrial
Survey question: Q5
Response: Performance
Code: Performance sufficiency
Type: Lever
Research question: RQ1
Industry: Manufacturing / Industrial
Survey question: 8
Response: Open-source: customization of anomaly-detection models. Proprietary AI: enterprise-grade security.
Code: Security posture
Type: Gate
Research question: RQ2
Code: Customization & PEFT
Type: Lever
Research question: RQ3
Industry: Manufacturing / Industrial
Survey question: 9
Response: We used Hugging Face on time-series sensor data to detect pump anomalies that reduced unscheduled downtime and maintenance costs.
Code: Cost & TCO
Type: Lever
Research question: RQ3
Industry: Manufacturing / Industrial
Survey question: 10
Response: Data quality and consistency across legacy SCADA systems. Model integration into existing OT networks with strict uptime requirements
Code: No keyword match
Type: NA
Research question: NA
Industry: Manufacturing / Industrial
Survey question: 11
Response: /
Code: No keyword match
Type: NA
Research question: NA
Industry: Manufacturing / Industrial
Survey question: 12
Response: Total cost of ownership and flexibility against operational reliability, SLAs, and integration ease.
Code: Cost & TCO
Type: Lever
Research question: RQ3
Industry: Manufacturing / Industrial
Survey question: Q5
Response: Vendor support
Code: Support & SLAs
Type: Lever
Research question: RQ1
Industry: Manufacturing / Industrial
Survey question: 8
Response: 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.
Code: Security posture
Type: Gate
Research question: RQ2
Code: Support & SLAs
Type: Lever
Research question: RQ3
Code: Deployment architecture
Type: Lever
Research question: RQ3
Industry: Manufacturing / Industrial
Survey question: 9
Response: Vision based defect detection on the production line using a managed model and pipeline. Reliability, monitoring, and traceability were decisive.
Code: Observability & monitoring
Type: Lever
Research question: RQ3
Industry: Manufacturing / Industrial
Survey question: 10
Response: Validating open components against safety and quality processes, standing up on premises GPU operations, and ensuring end to end traceability for audits.
Code: No keyword match
Type: NA
Research question: NA
Industry: Manufacturing / Industrial
Survey question: 11
Response: Example change-control records to speed quality reviews.
Code: No keyword match
Type: NA
Research question: NA
Industry: Manufacturing / Industrial
Survey question: 12
Response: Compliance risk and time to production are the gates. Also, flexibility and total cost of ownership.
Code: Compliance
Type: Gate
Research question: RQ2
Code: Cost & TCO
Type: Lever
Research question: RQ3
Industry: Media & Entertainment
Survey question: Q5
Response: Performance
Code: Performance sufficiency
Type: Lever
Research question: RQ1
Industry: Media & Entertainment
Survey question: 8
Response: On-prem deployment for PII-heavy content pipelines; full inspectability for legal review; flexible fine-tuning on our storytelling IP.
Code: Deployment architecture
Type: Lever
Research question: RQ3
Industry: Media & Entertainment
Survey question: 9
Response: 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.
Code: Cost & TCO
Type: Lever
Research question: RQ3
Industry: Media & Entertainment
Survey question: 10
Response: Licence clarity, Model-card completeness, Inference cost spikes, change management.
Code: Cost & TCO
Type: Lever
Research question: RQ3
Industry: Media & Entertainment
Survey question: 11
Response: Built-in policy enforcement hooks (e.g., export-compliance checks), Curated, security-scanned model zoo flagged for commercial use
Code: Compliance
Type: Gate
Research question: RQ2
Code: Security posture
Type: Gate
Research question: RQ2
Industry: Media & Entertainment
Survey question: 12
Response: Latency goal, data control, accuracy ceiling, reg-compliance, total cost.
Code: Compliance
Type: Gate
Research question: RQ2
Code: Cost & TCO
Type: Lever
Research question: RQ3
Code: Performance & latency
Type: Lever
Research question: RQ3
Industry: Media & Entertainment
Survey question: Q5
Response: Performance, Cost
Code: Cost & TCO
Type: Lever
Research question: RQ1
Industry: Media & Entertainment
Survey question: 8
Response: Creativity, Adaptability
Code: No keyword match
Type: NA
Research question: NA
Industry: Media & Entertainment
Survey question: 9
Response: Caption generation
Code: No keyword match
Type: NA
Research question: NA
Industry: Media & Entertainment
Survey question: 10
Response: IP review
Code: No keyword match
Type: NA
Research question: NA
Industry: Media & Entertainment
Survey question: 11
Response: Model card clarity
Code: No keyword match
Type: NA
Research question: NA
Industry: Media & Entertainment
Survey question: 12
Response: Performance > Cost
Code: Cost & TCO
Type: Lever
Research question: RQ3
Code: Performance sufficiency
Type: Lever
Research question: RQ3
Industry: Media & Entertainment
Survey question: Q5
Response: Cost, SLAs
Code: Cost & TCO
Type: Lever
Research question: RQ1
Industry: Media & Entertainment
Survey question: 8
Response: Agile iteration
Code: No keyword match
Type: NA
Research question: NA
Industry: Media & Entertainment
Survey question: 9
Response: Metadata tagging for archives
Code: No keyword match
Type: NA
Research question: NA
Industry: Media & Entertainment
Survey question: 10
Response: Compliance
Code: Compliance
Type: Gate
Research question: RQ2
Industry: Media & Entertainment
Survey question: 11
Response: License examples
Code: Licensing clarity
Type: Gate
Research question: RQ2
Industry: Media & Entertainment
Survey question: 12
Response: SLA vs openness
Code: No keyword match
Type: NA
Research question: NA
Industry: Retail / eCommerce
Survey question: Q5
Response: Cost
Code: Cost & TCO
Type: Lever
Research question: RQ1
Industry: Retail / eCommerce
Survey question: 8
Response: Proprietary solutions were the only one known so we took it
Code: No keyword match
Type: NA
Research question: NA
Industry: Retail / eCommerce
Survey question: 9
Response: It reduce the time we are spending on creating content mainly
Code: No keyword match
Type: NA
Research question: NA
Industry: Retail / eCommerce
Survey question: 10
Response: Understand how AI could help and How it could be helpful to our work
Code: No keyword match
Type: NA
Research question: NA
Industry: Retail / eCommerce
Survey question: 11
Response: Nothing to add.
Code: No keyword match
Type: NA
Research question: NA
Industry: Retail / eCommerce
Survey question: 12
Response: Vendor support
Code: Support & SLAs
Type: Lever
Research question: RQ3
Industry: Retail / eCommerce
Survey question: Q5
Response: Data control / privacy
Code: Data privacy & control
Type: Gate
Research question: RQ1
Industry: Retail / eCommerce
Survey question: 8
Response: 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.
Code: Security posture
Type: Gate
Research question: RQ2
Code: Documentation completeness
Type: Gate
Research question: RQ2
Code: Customization & PEFT
Type: Lever
Research question: RQ3
Industry: Retail / eCommerce
Survey question: 9
Response: Not in production.
Code: No keyword match
Type: NA
Research question: NA
Industry: Retail / eCommerce
Survey question: 10
Response: DPIA preparation, data minimisation and PII redaction, uncertainty over licence terms for commercial use, and hardening self-hosted inference with proper monitoring and patching.
Code: Observability & monitoring
Type: Lever
Research question: RQ3
Industry: Retail / eCommerce
Survey question: 11
Response: Org-level policy enforcement with automatic GDPR and EU AI Act documentation export.
Code: Documentation completeness
Type: Gate
Research question: RQ2
Industry: Retail / eCommerce
Survey question: 12
Response: Compliance, data residency, and security first
Code: Compliance
Type: Gate
Research question: RQ2
Code: Security posture
Type: Gate
Research question: RQ2
Industry: Retail / eCommerce
Survey question: Q5
Response: Scalability and cost.
Code: Cost & TCO
Type: Lever
Research question: RQ1
Industry: Retail / eCommerce
Survey question: 8
Response: 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.
Code: Compliance
Type: Gate
Research question: RQ2
Code: Cost & TCO
Type: Lever
Research question: RQ3
Industry: Retail / eCommerce
Survey question: 9
Response: AI-powered assistants leveraging a combination of internally built models and external large language models to enhance customer experience.
Code: Customization & PEFT
Type: Lever
Research question: RQ3
Industry: Retail / eCommerce
Survey question: 10
Response: 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.
Code: Compliance
Type: Gate
Research question: RQ2
Code: Customization & PEFT
Type: Lever
Research question: RQ3
Code: Deployment architecture
Type: Lever
Research question: RQ3
Industry: Retail / eCommerce
Survey question: 11
Response: Nothing here!
Code: No keyword match
Type: NA
Research question: NA
Industry: Retail / eCommerce
Survey question: 12
Response: Innovation speed, transparency, and community benefits of open-source AI vs. the operational stability, vendor accountability, and legal assurances of proprietary solutions.
Code: No keyword match
Type: NA
Research question: NA
Industry: Retail / eCommerce
Survey question: Q5
Response: Performance
Code: Performance sufficiency
Type: Lever
Research question: RQ1
Industry: Retail / eCommerce
Survey question: 8
Response: 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.
Code: No keyword match
Type: NA
Research question: NA
Industry: Retail / eCommerce
Survey question: 9
Response: Research and rapid prototyping in content personalization and user interaction.
Code: No keyword match
Type: NA
Research question: NA
Industry: Retail / eCommerce
Survey question: 10
Response: Fine-tuning models to ensure brand-consistent outputs without distortion and maintaining data privacy and compliance especially when dealing with user personalization.
Code: Compliance
Type: Gate
Research question: RQ2
Code: Data privacy & control
Type: Gate
Research question: RQ2
Industry: Retail / eCommerce
Survey question: 11
Response: More templates focused on image generation, fashion trend analysis, and personalization workflows.
Code: No keyword match
Type: NA
Research question: NA
Industry: Retail / eCommerce
Survey question: 12
Response: Proprietary AI is preferred for core product engineering and customer-facing features, while open-source is valuable for pilots and augmenting capabilities.
Code: Customization & PEFT
Type: Lever
Research question: RQ3
Industry: Retail / eCommerce
Survey question: Q5
Response: Performance, customization, deployment speed
Code: Customization & PEFT
Type: Lever
Research question: RQ1
Industry: Retail / eCommerce
Survey question: 8
Response: Open-source: prototyping of our social‐media image analysis pipelines. Proprietary: managed infrastructure and vendor support for production scalability.
Code: Support & SLAs
Type: Lever
Research question: RQ3
Industry: Retail / eCommerce
Survey question: 9
Response: We leveraged open-source models to build our trend‐detection models, reducing prototype time by 50% and improving seasonal trend prediction accuracy by 12%.
Code: No keyword match
Type: NA
Research question: NA
Industry: Retail / eCommerce
Survey question: 10
Response: Data compliance when scraping and processing social media imagery across jurisdictions
Code: Compliance
Type: Gate
Research question: RQ2
Industry: Retail / eCommerce
Survey question: 11
Response: -
Code: No keyword match
Type: NA
Research question: NA
Industry: Retail / eCommerce
Survey question: 12
Response: 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.
Code: Compliance
Type: Gate
Research question: RQ2
Code: Support & SLAs
Type: Lever
Research question: RQ3
Industry: Retail / eCommerce
Survey question: Q5
Response: Customization
Code: Customization & PEFT
Type: Lever
Research question: RQ1
Industry: Retail / eCommerce
Survey question: 8
Response: Proprietary solutions offer managed services and commercial SLAs. Open-source enables customization, transparency, and cost control.
Code: Cost & TCO
Type: Lever
Research question: RQ3
Code: Customization & PEFT
Type: Lever
Research question: RQ3
Industry: Retail / eCommerce
Survey question: 9
Response: We integrated Hugging Face transformers into our product recommendations engine. The solution increased recommendation click-through rates and boosted average order value.
Code: No keyword match
Type: NA
Research question: NA
Industry: Retail / eCommerce
Survey question: 10
Response: Model governance and version control across multiple teams, data-privacy and compliance requirements, scaling inference infrastructure cost-effectively under variable traffic patterns.
Code: Compliance
Type: Gate
Research question: RQ2
Code: Data privacy & control
Type: Gate
Research question: RQ2
Code: Governance readiness
Type: Gate
Research question: RQ2
Code: Cost & TCO
Type: Lever
Research question: RQ3
Industry: Retail / eCommerce
Survey question: 11
Response: Advanced monitoring dashboards with custom alerting on model drift.
Code: Customization & PEFT
Type: Lever
Research question: RQ3
Code: Observability & monitoring
Type: Lever
Research question: RQ3
Industry: Retail / eCommerce
Survey question: 12
Response: > 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
Code: Compliance
Type: Gate
Research question: RQ2
Code: Licensing clarity
Type: Gate
Research question: RQ2
Code: Security posture
Type: Gate
Research question: RQ2
Code: Cost & TCO
Type: Lever
Research question: RQ3
Code: Support & SLAs
Type: Lever
Research question: RQ3
Code: Customization & PEFT
Type: Lever
Research question: RQ3
Code: Deployment architecture
Type: Lever
Research question: RQ3
Industry: Retail / eCommerce
Survey question: Q5
Response: Cost, Performance, Support
Code: Cost & TCO
Type: Lever
Research question: RQ1
Industry: Retail / eCommerce
Survey question: 8
Response: Lower TCO and flexibility
Code: No keyword match
Type: NA
Research question: NA
Industry: Retail / eCommerce
Survey question: 9
Response: Product search enhancement
Code: No keyword match
Type: NA
Research question: NA
Industry: Retail / eCommerce
Survey question: 10
Response: Integration with legacy stack
Code: No keyword match
Type: NA
Research question: NA
Industry: Retail / eCommerce
Survey question: 11
Response: Clearer SDKs
Code: No keyword match
Type: NA
Research question: NA
Industry: Retail / eCommerce
Survey question: 12
Response: Compare ROI and SLA
Code: No keyword match
Type: NA
Research question: NA
Industry: Retail / eCommerce
Survey question: Q5
Response: Performance, Time to Value
Code: Performance sufficiency
Type: Lever
Research question: RQ1
Industry: Retail / eCommerce
Survey question: 8
Response: Faster iteration
Code: No keyword match
Type: NA
Research question: NA
Industry: Retail / eCommerce
Survey question: 9
Response: RAG over product manuals
Code: No keyword match
Type: NA
Research question: NA
Industry: Retail / eCommerce
Survey question: 10
Response: Model updates
Code: No keyword match
Type: NA
Research question: NA
Industry: Retail / eCommerce
Survey question: 11
Response: Update notifications
Code: No keyword match
Type: NA
Research question: NA
Industry: Retail / eCommerce
Survey question: 12
Response: Test feasibility → Cost
Code: Cost & TCO
Type: Lever
Research question: RQ3
Industry: Retail / eCommerce
Survey question: 8
Response: SLA reliability
Code: No keyword match
Type: NA
Research question: NA
Industry: Retail / eCommerce
Survey question: 9
Response: Recommendation bot POC
Code: No keyword match
Type: NA
Research question: NA
Industry: Retail / eCommerce
Survey question: 10
Response: Limited engineering
Code: No keyword match
Type: NA
Research question: NA
Industry: Retail / eCommerce
Survey question: 11
Response: Enterprise-tier support
Code: Support & SLAs
Type: Lever
Research question: RQ3
Industry: Retail / eCommerce
Survey question: 12
Response: SLA > Openness
Code: No keyword match
Type: NA
Research question: NA
Industry: Retail / eCommerce
Survey question: Q5
Response: Cost, Customization
Code: Cost & TCO
Type: Lever
Research question: RQ1
Industry: Retail / eCommerce
Survey question: 8
Response: Internalization control
Code: No keyword match
Type: NA
Research question: NA
Industry: Retail / eCommerce
Survey question: 9
Response: Classification for returns
Code: No keyword match
Type: NA
Research question: NA
Industry: Retail / eCommerce
Survey question: 10
Response: Model drift
Code: No keyword match
Type: NA
Research question: NA
Industry: Retail / eCommerce
Survey question: 11
Response: Monitoring tools
Code: Observability & monitoring
Type: Lever
Research question: RQ3
Industry: Retail / eCommerce
Survey question: 12
Response: Compare TCO vs reliability
Code: No keyword match
Type: NA
Research question: NA
Industry: Retail / eCommerce
Survey question: Q5
Response: Performance, Customization
Code: Customization & PEFT
Type: Lever
Research question: RQ1
Industry: Retail / eCommerce
Survey question: 8
Response: Domain adaptation
Code: No keyword match
Type: NA
Research question: NA
Industry: Retail / eCommerce
Survey question: 9
Response: Copilot for customer queries
Code: Customization & PEFT
Type: Lever
Research question: RQ3
Industry: Retail / eCommerce
Survey question: 10
Response: GPU constraints
Code: No keyword match
Type: NA
Research question: NA
Industry: Retail / eCommerce
Survey question: 11
Response: Fine-tuning templates
Code: No keyword match
Type: NA
Research question: NA
Industry: Retail / eCommerce
Survey question: 12
Response: Compare accuracy vs cost
Code: Cost & TCO
Type: Lever
Research question: RQ3
Industry: Technology / Software
Survey question: Q5
Response: Data control / privacy
Code: Data privacy & control
Type: Gate
Research question: RQ1
Industry: Technology / Software
Survey question: 8
Response: Customer zero, using in-house AI solution. Furthermore, security is always a concern.
Code: Security posture
Type: Gate
Research question: RQ2
Code: Customization & PEFT
Type: Lever
Research question: RQ3
Industry: Technology / Software
Survey question: 9
Response: Unfortunately not, haven’t used it in the pass
Code: No keyword match
Type: NA
Research question: NA
Industry: Technology / Software
Survey question: 10
Response: No open-source AI adoption
Code: No keyword match
Type: NA
Research question: NA
Industry: Technology / Software
Survey question: 11
Response: Not able to share internal information
Code: No keyword match
Type: NA
Research question: NA
Industry: Technology / Software
Survey question: 12
Response: No part of the decision making process
Code: No keyword match
Type: NA
Research question: NA
Industry: Technology / Software
Survey question: Q5
Response: Performance
Code: Performance sufficiency
Type: Lever
Research question: RQ1
Industry: Technology / Software
Survey question: 8
Response: 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
Code: Customization & PEFT
Type: Lever
Research question: RQ3
Industry: Technology / Software
Survey question: 9
Response: 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
Code: Documentation completeness
Type: Gate
Research question: RQ2
Code: Customization & PEFT
Type: Lever
Research question: RQ3
Industry: Technology / Software
Survey question: 10
Response: 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.
Code: Governance readiness
Type: Gate
Research question: RQ2
Code: Documentation completeness
Type: Gate
Research question: RQ2
Industry: Technology / Software
Survey question: 11
Response: I've not seen clients use HF yet, only Azure OpenAI
Code: No keyword match
Type: NA
Research question: NA
Industry: Technology / Software
Survey question: 12
Response: We don't use it ourselves but I know from clients:
1) Governance / data protection
2) Usability
3) Costs
Code: Governance readiness
Type: Gate
Research question: RQ2
Code: Cost & TCO
Type: Lever
Research question: RQ3
Industry: Technology / Software
Survey question: 8
Response: Ease of use and deployment speed
Code: Deployment architecture
Type: Lever
Research question: RQ3
Industry: Technology / Software
Survey question: 9
Response: Chatbot for software documentation
Code: Documentation completeness
Type: Gate
Research question: RQ2
Industry: Technology / Software
Survey question: 10
Response: /
Code: No keyword match
Type: NA
Research question: NA
Code: No keyword match
Type: NA
Research question: NA
Code: No keyword match
Type: NA
Research question: NA
Industry: Technology / Software
Survey question: Q5
Response: Regulatory compliance
Code: Compliance
Type: Gate
Research question: RQ1
Industry: Technology / Software
Survey question: 8
Response: Data privacy, security, customer demand, employee demand
Code: Security posture
Type: Gate
Research question: RQ2
Code: Data privacy & control
Type: Gate
Research question: RQ2
Code: Customization & PEFT
Type: Lever
Research question: RQ3
Industry: Technology / Software
Survey question: 9
Response: 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.
Code: No keyword match
Type: NA
Research question: NA
Industry: Technology / Software
Survey question: 10
Response: My guess: providing the hardware necessary and justifying the cost.
Code: Cost & TCO
Type: Lever
Research question: RQ3
Industry: Technology / Software
Survey question: 11
Response: maybe integrated cloud hosting or deployment solutions. Also model training capabilities to speed up processes and access to or pooling of specific training data
Code: Deployment architecture
Type: Lever
Research question: RQ3
Industry: Technology / Software
Survey question: 12
Response: 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
Code: Cost & TCO
Type: Lever
Research question: RQ3
Code: Customization & PEFT
Type: Lever
Research question: RQ3
Industry: Technology / Software
Survey question: 8
Response: acceleration of development
Code: No keyword match
Type: NA
Research question: NA
Industry: Technology / Software
Survey question: 9
Response: nan
Code: No keyword match
Type: NA
Research question: NA
Industry: Technology / Software
Survey question: 10
Response: main challenges are legal and compliances, then integration to existing workflows
Code: Compliance
Type: Gate
Research question: RQ2
Industry: Technology / Software
Survey question: 11
Response: nan
Code: No keyword match
Type: NA
Research question: NA
Industry: Technology / Software
Survey question: 12
Response: compliance and security
Code: Compliance
Type: Gate
Research question: RQ2
Code: Security posture
Type: Gate
Research question: RQ2
Industry: Technology / Software
Survey question: 8
Response: 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.
Code: Performance & latency
Type: Lever
Research question: RQ3
Code: Performance sufficiency
Type: Lever
Research question: RQ3
Code: Customization & PEFT
Type: Lever
Research question: RQ3
Industry: Technology / Software
Survey question: 9
Response: 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.
Code: Customization & PEFT
Type: Lever
Research question: RQ3
Industry: Technology / Software
Survey question: 10
Response: 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.
Code: Licensing clarity
Type: Gate
Research question: RQ2
Industry: Technology / Software
Survey question: 12
Response: Security, privacy, and customer commitments.
Code: Security posture
Type: Gate
Research question: RQ2
Code: Data privacy & control
Type: Gate
Research question: RQ2
Code: Customization & PEFT
Type: Lever
Research question: RQ3
Industry: Technology / Software
Survey question: 8
Response: 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.
Code: Cost & TCO
Type: Lever
Research question: RQ3
Code: Performance sufficiency
Type: Lever
Research question: RQ3
Industry: Technology / Software
Survey question: 9
Response: Developing our own open source LLMs
Code: No keyword match
Type: NA
Research question: NA
Industry: Technology / Software
Survey question: 10
Response: Integration and Support from model Provider in AI frameworks
Code: Support & SLAs
Type: Lever
Research question: RQ3
Industry: Technology / Software
Survey question: 11
Response: Easier Integration into Azure as Infra stack
Code: No keyword match
Type: NA
Research question: NA
Industry: Technology / Software
Survey question: 12
Response: Performance use case driven
Code: Performance sufficiency
Type: Lever
Research question: RQ3
Industry: Technology / Software
Survey question: Q5
Response: All of the above with Data control/privacy / Compliance & Risk at the forefront
Code: Compliance
Type: Gate
Research question: RQ1
Industry: Technology / Software
Survey question: 8
Response: Having the broadest possible portfolio of models available to us and our customers.
Code: Customization & PEFT
Type: Lever
Research question: RQ3
Industry: Technology / Software
Survey question: 9
Response: We sometimes (or our customers) use specialized OpenSource  models when specific needs arise.
Code: Customization & PEFT
Type: Lever
Research question: RQ3
Industry: Technology / Software
Survey question: 10
Response: 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.
Code: Compliance
Type: Gate
Research question: RQ2
Code: Security posture
Type: Gate
Research question: RQ2
Industry: Technology / Software
Survey question: 11
Response: We just got it a couple of weeks ago. Hugging Face is not integrated in Azure AI Foundry ;-)
Code: No keyword match
Type: NA
Research question: NA
Industry: Technology / Software
Survey question: 12
Response: Based on our Responsible AI principles, then it will depend on the use case: capability, cost, ease of use, ease of fine tuning, etc.
Code: Cost & TCO
Type: Lever
Research question: RQ3
Industry: Technology / Software
Survey question: Q5
Response: Customization
Code: Customization & PEFT
Type: Lever
Research question: RQ1
Industry: Technology / Software
Survey question: 8
Response: Eliminate single-vendor risk, fine-grained control over weights & infra, lower TCO at scale, and stronger data-sovereignty guarantees.
Code: No keyword match
Type: NA
Research question: NA
Industry: Technology / Software
Survey question: 9
Response: Automated pull-request review summaries. It now drafts ~70 % of review notes, cutting engineer PR turnaround by 32 hours per sprint.
Code: No keyword match
Type: NA
Research question: NA
Industry: Technology / Software
Survey question: 10
Response: 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.
Code: Licensing clarity
Type: Gate
Research question: RQ2
Code: Customization & PEFT
Type: Lever
Research question: RQ3
Industry: Technology / Software
Survey question: 11
Response: A push-time license-compliance gate.
Code: Compliance
Type: Gate
Research question: RQ2
Code: Licensing clarity
Type: Gate
Research question: RQ2
Industry: Technology / Software
Survey question: 12
Response: Total cost of ownership, latency & accuracy benchmarks, licence obligations, roadmap stability, and lock-in risk.
Code: Cost & TCO
Type: Lever
Research question: RQ3
Code: Performance & latency
Type: Lever
Research question: RQ3