EPFL_Enterprise_OSAI_Adoption_Interview_Data.csv

ID,Interview Type,Date,Confidentiality,Role,Industry,Stage,Context Summary,Theme 1: Decision Factors for AI Solution Selection,Theme 2: Integration Challenges and Organisational Readiness,"Theme 3: Motivations for Adopting Open-Source AI",Theme 4: Use Cases and Demonstrated Value,Theme 5: Adoption Gates for Governance and Compliance,Theme 6: Execution Levers for Adoption Success,Link to Framework,Main Takeaway
I1,Semi-structured (online video),20250605,Anonymous,AI Lead,Technology/ Software,Adopted,"The AI Lead shared insights on enterprise evaluation of open-source AI models, focusing on cost-performance trade-offs, integration barriers, and governance readiness. Customers increasingly mention DeepSeek for its popularity, but most underestimate infrastructure demands.","Bigger models mean higher inference cost, latency, and infra demands. The participant stressed cost-capability trade-offs and said clients compare models like Qwen, Mistral, and DeepSeek based on fit and affordability. 'Bigger models equal higher inference cost.' Customers often ask for DeepSeek due to its popularity, but few grasp its compute requirements.",Most clients lack internal evaluation capacity. Fine-tuning remains complex and rare: '70% only go for fine-tuning as the last solution.' Teams typically rely on APIs or pre-integrated tools instead of custom retraining. Integration remains a major friction due to limited expertise and documentation.,"Open-source adoption is motivated by flexibility, cost, and customization. Proprietary APIs are easier to use but limit control. Open models are chosen once organizations trust the governance layer and want adaptability.","Value arises from multi-agent use and task-specific models, enabling latency reduction and cost savings. Token costs dropped by ~80%, encouraging broader experimentation. Demonstrates tangible cost and performance benefits when matched to context.","Governance prerequisites include licensing clarity and documentation completeness. 'Transparency and licensing: yes, they matter.' Deployment guidance is insufficient: 'How can I deploy straight from the model card?' Without structured documentation and licensing, enterprises hesitate to scale.","After governance gates are cleared, key enablers include cost efficiency, performance, and latency optimization. 'Use case first, then cost, then latency.' Confirms the Gate-Lever model: governance first, then optimization through cost and performance levers.","Reinforces RQ2 and RQ3: Gates: Licensing, documentation, and governance must be satisfied before scale. Levers: Cost, latency, and performance drive adoption once trust is established. Integration capability is a critical maturity barrier.","'Enterprises are drawn to open-source AI for flexibility and cost, but trust gates (licensing, documentation, and evaluation capability) determine whether adoption moves beyond pilot stage.'"
I2,Semi-structured (online video),20250605,Anonymous,AI Lead,Energy,Adopting,"The AI Lead discussed challenges in adopting open-source AI within a highly regulated, risk-averse energy enterprise. The firm primarily uses proprietary AI for governance and reliability reasons.",Cost is secondary to reliability and compliance. The participant explained that the energy sector's tolerance for uncertainty is low. Proprietary solutions are preferred due to clear SLAs and security posture.,"Integration complexity is high because of legacy systems, strict internal IT policies, and dependence on external vendors for compliance checks. The firm avoids open-source tools lacking enterprise-grade documentation.","Motivation to explore open models exists, but the risk profile is seen as incompatible. The participant said, 'We monitor the open space, but for now we stay on managed APIs.'","The organization focuses on analytics and forecasting tools built into proprietary platforms. No open-source use cases yet, only sandbox experiments under supervision.","Compliance and security audits are mandatory. The firm requires formal certification, licensing clarity, and full documentation before any production use. These factors are non-negotiable gates.","If gate conditions were met, the main lever would be cost savings and customization for forecasting models. For now, vendor dependence is accepted as a trade-off for safety.","Reinforces RQ2 and RQ3. Compliance, documentation, and licensing act as strict Gate conditions. Levers such as cost efficiency remain theoretical until trust is achieved.","Highly regulated industries remain risk-averse; adoption depends on trust, certification, and documentation rather than cost or performance."

I3,Semi-structured (online video),20250611,Anonymous,Head of AI Strategy,Finance,Pre-adoption,"The participant described a cautious approach to AI adoption. The firm uses primarily proprietary solutions within a tightly regulated environment, with minimal exposure to open-source AI.","Decision factors center on compliance, security, and auditability. The participant said cost is 'not the first concern; risk is.' Proprietary systems are preferred due to full traceability and vendor accountability.","Integration challenges stem from strict data governance policies and internal compliance frameworks. Open-source tools lack legal vetting and risk scoring, which halts experimentation.","Motivation to adopt open-source AI is very low. The organization prioritizes stability and regulatory trust. The participant noted, 'We cannot risk using code without full control over provenance.'","No open-source use cases yet. Internal pilots focus on data anonymization and model validation tools, all within managed cloud environments.","Governance is gate-dominant. The firm requires verified licensing, source traceability, and complete documentation before considering open models. Compliance officers must approve every vendor or repository.","Execution levers are not active at this stage. If adoption occurs, it will depend on proven compliance, auditable data flows, and certified model risk frameworks.","Reinforces RQ2 by showing that pre-adoption sectors remain blocked by Gate conditions: compliance, documentation, and provenance. RQ3 logic holds as no Levers are activated.","In highly regulated finance, adoption is ruled by governance gates. Cost and innovation only matter once compliance assurance is guaranteed."

I4,Semi-structured (online video),20250618,Anonymous,Data Science Manager,Telecom,Adopting,The participant described a telecom enterprise that uses both proprietary and open-source components. The firm experiments with open models but faces integration and compliance hurdles.,"Decision factors include performance consistency, cost efficiency, and interoperability with legacy systems. Open models are evaluated but often fail due to unclear SLAs.","Integration challenges are significant. Telecom workloads demand low latency and high reliability. The participant said, 'We need models we can monitor continuously.' Internal ops teams find deployment documentation lacking.","Motivation for adopting open-source AI is driven by cost reduction and vendor independence. However, production teams still depend on managed APIs for scaling.","Successful experiments exist in customer churn prediction and text summarization. However, scaling these pilots remains slow due to monitoring complexity.",Governance is a constant concern. The firm requires clarity in licensing and provenance before integrating open models into live customer systems.,"Execution levers include cost optimization, customization, and faster iteration cycles once internal validation is done. Performance and latency drive adoption success.",Reinforces RQ2 and RQ3. Integration barriers (Q10) and documentation gaps (Gate) slow adoption. Levers emerge only when trust and monitoring readiness are proven.,"Telecom adoption is paced by integration maturity. Cost and performance attract interest, but documentation and monitoring readiness determine scale."

I5,Semi-structured (online video),20250623,Anonymous,AI & Analytics Lead,FMCG,Adopting,"The participant leads analytics initiatives in a global FMCG firm. The company integrates AI mainly through Azure services, focusing on automation, forecasting, and marketing optimization. It uses proprietary tools due to internal skill constraints and reliance on enterprise-grade SLAs.","Decision factors center on reliability, integration ease, and vendor support. The participant said, 'We use Azure because it works with everything we already have.' Cost is monitored but secondary to scalability and security.",Integration challenges arise from legacy systems and distributed data silos. The company lacks deep ML engineering capacity and depends on managed APIs. Internal teams face difficulty maintaining custom pipelines beyond Azure workflows.,"Motivation to adopt open-source AI is limited. The firm monitors OSS models but avoids them due to maintenance risk and lack of expertise. The participant said, 'We prefer solutions that come supported - we do not have time to retrain or host models ourselves.'","Use cases include demand forecasting, campaign optimization, and customer insights using Azure ML and Cognitive Services. These applications deliver measurable ROI and are integrated with BI dashboards.","Governance is focused on data security, privacy, and brand risk. All AI deployments must pass internal IT compliance review. The participant emphasized data residency and model auditability as key gates before production.","Execution levers include vendor support, fast deployment, and cost predictability. Managed environments reduce operational risk and allow rapid scaling. 'We measure success by how fast a model moves to production,' the participant said.","Supports RQ2 (decision factors and readiness) and RQ3 (lever activation without open-source gates). Demonstrates adoption driven by ROI and integration simplicity, not openness.","FMCG enterprises adopt AI through proprietary cloud ecosystems such as Azure, prioritizing speed, support, and ROI over transparency or control."

I6,Semi-structured (online video),20250708,Anonymous,Research Director,Education / Research,Adopted,"The Research Director oversees AI adoption in a higher education institution. The organization integrates open-source models through academic partnerships and Hugging Face resources. Its focus is on transparency, reproducibility, and academic collaboration.","Decision factors emphasize transparency, accessibility, and peer validation. Cost is important but secondary to control and reproducibility. The participant said, 'If we cannot inspect it, we cannot trust it.'","Integration is supported by strong internal technical expertise but limited enterprise infrastructure. Challenges arise around maintenance, version control, and alignment with institutional IT policies.","The main motivation for adopting open-source AI is academic integrity and collaborative innovation. The participant stated, 'We teach and research with open models because they reflect how science should work.'","Use cases include NLP research, student AI projects, and open-access teaching tools using Hugging Face and local deployments. These initiatives increase learning efficiency and research output.","Governance is centered on ethical AI use, licensing awareness, and data transparency. The institution follows strict research ethics protocols and uses model cards to document provenance and bias.","Execution levers include community support, documentation availability, and model reproducibility. Performance is valued when it serves academic goals, not just commercial outcomes.","Supports RQ2 (decision and motivation factors) and RQ3 (Gate - Lever interaction). Shows that governance gates are met through transparency, enabling levers such as innovation and collaboration.",Education and research adopters favor open-source AI for transparency and reproducibility. Governance alignment and internal expertise allow them to scale experimentation responsibly.