itseffi/epfl-enterprise-osai-adoption-research-data huggingface dataset:
Interview with AI Lead (Technology/ Software), open-source AI adoption stage: Adopted

Context: 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.

Decision factors for AI solution selection: 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.

Integration challenges and organisational readiness: 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.

Motivations for adopting open-source AI: 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.

Use cases and demonstrated value: 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.

Adoption gates for governance and compliance: 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.

Execution levers for adoption success: 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.

Link to framework: 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.

Main takeaway: '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.'
Interview with AI Lead (Energy), open-source AI adoption stage: Adopting

Context: 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.

Decision factors for AI solution selection: 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 challenges and organisational readiness: 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.

Motivations for adopting open-source AI: 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.'

Use cases and demonstrated value: The organization focuses on analytics and forecasting tools built into proprietary platforms. No open-source use cases yet, only sandbox experiments under supervision.

Adoption gates for governance and compliance: 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.

Execution levers for adoption success: 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.

Link to framework: Reinforces RQ2 and RQ3. Compliance, documentation, and licensing act as strict Gate conditions. Levers such as cost efficiency remain theoretical until trust is achieved.

Main takeaway: Highly regulated industries remain risk-averse; adoption depends on trust, certification, and documentation rather than cost or performance.
Interview with Head of AI Strategy (Finance), open-source AI adoption stage: Pre-adoption

Context: 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 for AI solution selection: 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 and organisational readiness: Integration challenges stem from strict data governance policies and internal compliance frameworks. Open-source tools lack legal vetting and risk scoring, which halts experimentation.

Motivations for adopting open-source AI: 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.'

Use cases and demonstrated value: No open-source use cases yet. Internal pilots focus on data anonymization and model validation tools, all within managed cloud environments.

Adoption gates for governance and compliance: 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 for adoption success: 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.

Link to framework: 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.

Main takeaway: In highly regulated finance, adoption is ruled by governance gates. Cost and innovation only matter once compliance assurance is guaranteed.
Interview with Data Science Manager (Telecom), open-source AI adoption stage: Adopting

Context: 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 for AI solution selection: 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 and organisational readiness: 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.

Motivations for adopting open-source AI: Motivation for adopting open-source AI is driven by cost reduction and vendor independence. However, production teams still depend on managed APIs for scaling.

Use cases and demonstrated value: Successful experiments exist in customer churn prediction and text summarization. However, scaling these pilots remains slow due to monitoring complexity.

Adoption gates for governance and compliance: Governance is a constant concern. The firm requires clarity in licensing and provenance before integrating open models into live customer systems.

Execution levers for adoption success: Execution levers include cost optimization, customization, and faster iteration cycles once internal validation is done. Performance and latency drive adoption success.

Link to framework: Reinforces RQ2 and RQ3. Integration barriers (Q10) and documentation gaps (Gate) slow adoption. Levers emerge only when trust and monitoring readiness are proven.

Main takeaway: Telecom adoption is paced by integration maturity. Cost and performance attract interest, but documentation and monitoring readiness determine scale.
Interview with AI & Analytics Lead (FMCG), open-source AI adoption stage: Adopting

Context: 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 for AI solution selection: 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 and organisational readiness: 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.

Motivations for adopting open-source AI: 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 and demonstrated value: 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.

Adoption gates for governance and compliance: 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 for adoption success: 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.

Link to framework: Supports RQ2 (decision factors and readiness) and RQ3 (lever activation without open-source gates). Demonstrates adoption driven by ROI and integration simplicity, not openness.

Main takeaway: FMCG enterprises adopt AI through proprietary cloud ecosystems such as Azure, prioritizing speed, support, and ROI over transparency or control.
Interview with Research Director (Education / Research), open-source AI adoption stage: Adopted

Context: 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 for AI solution selection: 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 challenges and organisational readiness: Integration is supported by strong internal technical expertise but limited enterprise infrastructure. Challenges arise around maintenance, version control, and alignment with institutional IT policies.

Motivations for adopting open-source AI: 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 and demonstrated value: 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.

Adoption gates for governance and compliance: 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 for adoption success: Execution levers include community support, documentation availability, and model reproducibility. Performance is valued when it serves academic goals, not just commercial outcomes.

Link to framework: 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.

Main takeaway: Education and research adopters favor open-source AI for transparency and reproducibility. Governance alignment and internal expertise allow them to scale experimentation responsibly.