itseffi/epfl-enterprise-osai-adoption-research-data huggingface dataset:
Industry: Technology / Software
Survey question: 8
Response: 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.
Code: Cost & TCO
Type: Lever
Research question: RQ3
Code: Customization & PEFT
Type: Lever
Research question: RQ3
Code: Deployment architecture
Type: Lever
Research question: RQ3
Industry: Technology / Software
Survey question: 9
Response: 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.
Code: No keyword match
Type: NA
Research question: NA
Industry: Technology / Software
Survey question: 10
Response: Technical, organisational (Upskilling engineers), legal / IP.
Code: No keyword match
Type: NA
Research question: NA
Industry: Technology / Software
Survey question: 11
Response: Industrial-Protocol Streaming Connectors to enable fast, reliable deployment of AI in industrial environments.
Code: Deployment architecture
Type: Lever
Research question: RQ3
Industry: Technology / Software
Survey question: 12
Response: Data sovereignty & privacy, total cost of ownership over 3 years, performance on domain benchmarks, regulatory & safety compliance fit, vendor / community support & roadmap.
Code: Compliance
Type: Gate
Research question: RQ2
Code: Data privacy & control
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: Technology / Software
Survey question: 8
Response: 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.
Code: Cost & TCO
Type: Lever
Research question: RQ3
Industry: Technology / Software
Survey question: 9
Response: 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.
Code: No keyword match
Type: NA
Research question: NA
Industry: Technology / Software
Survey question: 10
Response: OSS license & supply-chain vetting
Silent upstream checkpoint changes (solved by pinning SHAs)
GDPR/data-residency assessments
Change-management for non-technical stakeholders
Code: Licensing clarity
Type: Gate
Research question: RQ2
Industry: Technology / Software
Survey question: 11
Response: Model cards (eg. make it crystal-clear where—and where not—the model should be applied).
Code: No keyword match
Type: NA
Research question: NA
Industry: Technology / Software
Survey question: 12
Response: 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.
Code: Compliance
Type: Gate
Research question: RQ2
Code: Cost & TCO
Type: Lever
Research question: RQ3
Code: Performance & latency
Type: Lever
Research question: RQ3
Code: Performance sufficiency
Type: Lever
Research question: RQ3
Industry: Technology / Software
Survey question: 8
Response: 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.
Code: Cost & TCO
Type: Lever
Research question: RQ3
Code: Performance sufficiency
Type: Lever
Research question: RQ3
Industry: Technology / Software
Survey question: 9
Response: 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.
Code: Cost & TCO
Type: Lever
Research question: RQ3
Code: Support & SLAs
Type: Lever
Research question: RQ3
Industry: Technology / Software
Survey question: 10
Response: Keeping pace with weekly model releases and benchmarks.
GPU scarcity for fine-tuning larger models.
Licensing clarity.
Code: Licensing clarity
Type: Gate
Research question: RQ2
Industry: Technology / Software
Survey question: 11
Response: Observability & experimentation, optimised infra & cost control.
Code: Cost & TCO
Type: Lever
Research question: RQ3
Industry: Technology / Software
Survey question: 12
Response: Time-to-value, total cost of ownership, privacy/compliance, and adaptability
Code: Compliance
Type: Gate
Research question: RQ2
Code: Data privacy & control
Type: Gate
Research question: RQ2
Code: Cost & TCO
Type: Lever
Research question: RQ3
Industry: Technology / Software
Survey question: Q5
Response: Cost
Code: Cost & TCO
Type: Lever
Research question: RQ1
Industry: Technology / Software
Survey question: 8
Response: 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.
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: Performance & latency
Type: Lever
Research question: RQ3
Code: Performance sufficiency
Type: Lever
Research question: RQ3
Code: Support & SLAs
Type: Lever
Research question: RQ3
Code: Customization & PEFT
Type: Lever
Research question: RQ3
Industry: Technology / Software
Survey question: 9
Response: 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.
Code: Support & SLAs
Type: Lever
Research question: RQ3
Code: Customization & PEFT
Type: Lever
Research question: RQ3
Industry: Technology / Software
Survey question: 10
Response: Model size and inference costs required heavy optimization to meet latency targets. Documentation and prebuilt tooling were inconsistent across projects.
Code: Documentation completeness
Type: Gate
Research question: RQ2
Code: Cost & TCO
Type: Lever
Research question: RQ3
Code: Performance & latency
Type: Lever
Research question: RQ3
Industry: Technology / Software
Survey question: 11
Response: More granular, policy-based authorization than the current role and resource-group model.
Code: No keyword match
Type: NA
Research question: NA
Industry: Technology / Software
Survey question: 12
Response: Performance, control, flexibility, cost, risk & compliance.
Code: Compliance
Type: Gate
Research question: RQ2
Code: Cost & TCO
Type: Lever
Research question: RQ3
Code: Performance sufficiency
Type: Lever
Research question: RQ3
Industry: Technology / Software
Survey question: 8
Response: For open-source models: cost efficiency, customization needs, vendor independence. For proprietary AI: specialized capabilities (multimodal processing work).
Code: Cost & TCO
Type: Lever
Research question: RQ3
Code: Customization & PEFT
Type: Lever
Research question: RQ3
Industry: Technology / Software
Survey question: 9
Response: 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.
Code: Support & SLAs
Type: Lever
Research question: RQ3
Code: Customization & PEFT
Type: Lever
Research question: RQ3
Industry: Technology / Software
Survey question: 10
Response: 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.
Code: Deployment architecture
Type: Lever
Research question: RQ3
Industry: Technology / Software
Survey question: 11
Response: More granular pricing tiers between the free tier and full enterprise.
Code: No keyword match
Type: NA
Research question: NA
Industry: Technology / Software
Survey question: 12
Response: We use a decision matrix weighing cost per inference, required accuracy thresholds, data sensitivity, time-to-deployment, and maintenance overhead.
Code: Cost & TCO
Type: Lever
Research question: RQ3
Code: Deployment architecture
Type: Lever
Research question: RQ3
Industry: Technology / Software
Survey question: 8
Response: Cost-effectiveness, rapid prototyping and experimentation, community support and collaboration.
Code: Cost & TCO
Type: Lever
Research question: RQ3
Code: Support & SLAs
Type: Lever
Research question: RQ3
Industry: Technology / Software
Survey question: 9
Response: 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.
Code: Cost & TCO
Type: Lever
Research question: RQ3
Code: Customization & PEFT
Type: Lever
Research question: RQ3
Industry: Technology / Software
Survey question: 10
Response: 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.
Code: Compliance
Type: Gate
Research question: RQ2
Code: Licensing clarity
Type: Gate
Research question: RQ2
Industry: Technology / Software
Survey question: 11
Response: Improved licensing models, enhanced security and governance controls, and more comprehensive documentation.
Code: Licensing clarity
Type: Gate
Research question: RQ2
Code: Security posture
Type: Gate
Research question: RQ2
Code: Governance readiness
Type: Gate
Research question: RQ2
Code: Documentation completeness
Type: Gate
Research question: RQ2
Industry: Technology / Software
Survey question: 12
Response: 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.
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
Industry: Technology / Software
Survey question: 8
Response: 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.
Code: Performance sufficiency
Type: Lever
Research question: RQ3
Industry: Technology / Software
Survey question: 9
Response: Orchestrated multiple open-source LLMs in real-time, enabling generalized multi-agent conversation outputs that improved both diversity and robustness of results.
Code: No keyword match
Type: NA
Research question: NA
Industry: Technology / Software
Survey question: 10
Response: model hosting scalability, aligning inference performance with proprietary systems, and managing model version changes in open-source projects.
Code: Performance sufficiency
Type: Lever
Research question: RQ3
Industry: Technology / Software
Survey question: 11
Response: More detailed enterprise-grade model cards.
Code: No keyword match
Type: NA
Research question: NA
Industry: Technology / Software
Survey question: 12
Response: 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.
Code: Cost & TCO
Type: Lever
Research question: RQ3
Industry: Technology / Software
Survey question: Q5
Response: Performance, Flexibility, Community
Code: Performance sufficiency
Type: Lever
Research question: RQ1
Industry: Technology / Software
Survey question: 8
Response: Transparency, control, collaboration
Code: No keyword match
Type: NA
Research question: NA
Industry: Technology / Software
Survey question: 9
Response: Text generation models integrated in production
Code: No keyword match
Type: NA
Research question: NA
Industry: Technology / Software
Survey question: 10
Response: Version control, scaling
Code: No keyword match
Type: NA
Research question: NA
Industry: Technology / Software
Survey question: 11
Response: Enterprise-grade fine-tuning API
Code: No keyword match
Type: NA
Research question: NA
Industry: Technology / Software
Survey question: 12
Response: Performance > Cost
Code: Cost & TCO
Type: Lever
Research question: RQ3
Code: Performance sufficiency
Type: Lever
Research question: RQ3
Industry: Technology / Software
Survey question: Q5
Response: Performance, Governance, Security
Code: Performance sufficiency
Type: Lever
Research question: RQ1
Industry: Technology / Software
Survey question: 8
Response: Vendor independence
Code: No keyword match
Type: NA
Research question: NA
Industry: Technology / Software
Survey question: 9
Response: Custom LLM for internal knowledge base
Code: Customization & PEFT
Type: Lever
Research question: RQ3
Industry: Technology / Software
Survey question: 10
Response: Policy compliance
Code: Compliance
Type: Gate
Research question: RQ2
Industry: Technology / Software
Survey question: 11
Response: Role-based access control
Code: No keyword match
Type: NA
Research question: NA
Industry: Technology / Software
Survey question: 12
Response: Balance governance vs innovation
Code: Governance readiness
Type: Gate
Research question: RQ2
Industry: Technology / Software
Survey question: Q5
Response: Cost, Customization, Ecosystem
Code: Cost & TCO
Type: Lever
Research question: RQ1
Industry: Technology / Software
Survey question: 8
Response: Freedom to innovate
Code: No keyword match
Type: NA
Research question: NA
Industry: Technology / Software
Survey question: 9
Response: Multi-language classification pipeline
Code: No keyword match
Type: NA
Research question: NA
Industry: Technology / Software
Survey question: 10
Response: Maintaining dependencies
Code: No keyword match
Type: NA
Research question: NA
Industry: Technology / Software
Survey question: 11
Response: Long-term maintenance support
Code: Support & SLAs
Type: Lever
Research question: RQ3
Industry: Technology / Software
Survey question: 12
Response: Openness before SLA
Code: No keyword match
Type: NA
Research question: NA
Industry: Technology / Software
Survey question: Q5
Response: Performance, Ecosystem, Integration
Code: Performance sufficiency
Type: Lever
Research question: RQ1
Industry: Technology / Software
Survey question: 8
Response: Open innovation
Code: No keyword match
Type: NA
Research question: NA
Industry: Technology / Software
Survey question: 9
Response: Multimodal search system
Code: No keyword match
Type: NA
Research question: NA
Industry: Technology / Software
Survey question: 10
Response: Integration with legacy apps
Code: No keyword match
Type: NA
Research question: NA
Industry: Technology / Software
Survey question: 11
Response: Simplified API monitoring
Code: Observability & monitoring
Type: Lever
Research question: RQ3
Industry: Technology / Software
Survey question: 12
Response: Cost-performance equilibrium
Code: Cost & TCO
Type: Lever
Research question: RQ3
Code: Performance sufficiency
Type: Lever
Research question: RQ3
Industry: Telecommunications
Survey question: Q5
Response: Regulatory compliance
Code: Compliance
Type: Gate
Research question: RQ1
Industry: Telecommunications
Survey question: 8
Response: Exploring new markets, we are building AI platform for B2B customers.
Code: Customization & PEFT
Type: Lever
Research question: RQ3
Industry: Telecommunications
Survey question: 9
Response: Company provides internal LLM models to get help in everyday tasks. Github copilot speeds up developers in implementations and reasoning.
Code: No keyword match
Type: NA
Research question: NA
Industry: Telecommunications
Survey question: 10
Response: As in many big enterprise companies, the organisational and legal overhead slows down project execution.
Code: No keyword match
Type: NA
Research question: NA
Industry: Telecommunications
Survey question: 11
Response: Hard to say
Code: No keyword match
Type: NA
Research question: NA
Industry: Telecommunications
Survey question: 12
Response: 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.
Code: No keyword match
Type: NA
Research question: NA
Industry: Telecommunications
Survey question: Q5
Response: Data control / privacy
Code: Data privacy & control
Type: Gate
Research question: RQ1
Industry: Telecommunications
Survey question: 8
Response: Scalability, flexibility, security, and maintaining compliance while optimizing costs.
Code: Compliance
Type: Gate
Research question: RQ2
Code: Security posture
Type: Gate
Research question: RQ2
Code: Cost & TCO
Type: Lever
Research question: RQ3
Industry: Telecommunications
Survey question: 9
Response: We have used Hugging Face to develop multilingual customer support bots, resulting in faster response times and greater customer satisfaction.
Code: Support & SLAs
Type: Lever
Research question: RQ3
Code: Customization & PEFT
Type: Lever
Research question: RQ3
Industry: Telecommunications
Survey question: 10
Response: Integrating open-source AI with existing legacy systems, ensuring robust data privacy and security, and navigating a complex regulatory environment.
Code: Security posture
Type: Gate
Research question: RQ2
Code: Data privacy & control
Type: Gate
Research question: RQ2
Industry: Telecommunications
Survey question: 11
Response: Enhanced technical support, stronger security guarantees, better integration tools, and certification for regulatory compliance.
Code: Compliance
Type: Gate
Research question: RQ2
Code: Security posture
Type: Gate
Research question: RQ2
Code: Support & SLAs
Type: Lever
Research question: RQ3
Industry: Telecommunications
Survey question: 12
Response: By weighing the flexibility and cost benefits of open-source against the managed services, reliability, and vendor support of proprietary offerings.
Code: Cost & TCO
Type: Lever
Research question: RQ3
Code: Support & SLAs
Type: Lever
Research question: RQ3
Industry: Telecommunications
Survey question: Q5
Response: Performance
Code: Performance sufficiency
Type: Lever
Research question: RQ1
Industry: Telecommunications
Survey question: 8
Response: 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.
Code: Performance sufficiency
Type: Lever
Research question: RQ3
Code: Support & SLAs
Type: Lever
Research question: RQ3
Industry: Telecommunications
Survey question: 9
Response: Possibly limited but may include customer support automation.
Code: Support & SLAs
Type: Lever
Research question: RQ3
Code: Customization & PEFT
Type: Lever
Research question: RQ3
Industry: Telecommunications
Survey question: 10
Response: Integration complexity with specialized telecom hardware and software.
Data security and compliance considerations.
Code: Compliance
Type: Gate
Research question: RQ2
Code: Security posture
Type: Gate
Research question: RQ2
Industry: Telecommunications
Survey question: 11
Response: More specialized signal processing resources.
Code: No keyword match
Type: NA
Research question: NA
Industry: Telecommunications
Survey question: 12
Response: 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.
Code: Performance sufficiency
Type: Lever
Research question: RQ3
Industry: Telecommunications
Survey question: 8
Response: 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.
Code: Support & SLAs
Type: Lever
Research question: RQ3
Industry: Telecommunications
Survey question: 9
Response: Exploratory and pilot use in NLP for customer service automation, chatbots, and data analytics as part of innovation initiatives at the Innovation Lab.
Code: Customization & PEFT
Type: Lever
Research question: RQ3
Industry: Telecommunications
Survey question: 10
Response: 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.
Code: Compliance
Type: Gate
Research question: RQ2
Code: Security posture
Type: Gate
Research question: RQ2
Industry: Telecommunications
Survey question: 11
Response: More specialized documentation and use cases tailored to telecom signal processing and 6G network.
Code: Documentation completeness
Type: Gate
Research question: RQ2
Industry: Telecommunications
Survey question: 12
Response: 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.
Code: Compliance
Type: Gate
Research question: RQ2
Code: Support & SLAs
Type: Lever
Research question: RQ3
Industry: Telecommunications
Survey question: 8
Response: 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.
Code: No keyword match
Type: NA
Research question: NA
Industry: Telecommunications
Survey question: 9
Response: 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.
Code: No keyword match
Type: NA
Research question: NA
Industry: Telecommunications
Survey question: 10
Response: Legal review of model and dataset licences for commercial use, hardening inference endpoints.
Code: No keyword match
Type: NA
Research question: NA
Industry: Telecommunications
Survey question: 11
Response: Risk-register templates mapped to GDPR and the EU AI Act transparency duties, with links to model cards
Code: No keyword match
Type: NA
Research question: NA
Industry: Telecommunications
Survey question: 12
Response: Compliance and privacy first, then security and support readiness. If those are satisfied, we compare task performance and lifecycle cost.
Code: Compliance
Type: Gate
Research question: RQ2
Code: Security posture
Type: Gate
Research question: RQ2
Code: Data privacy & control
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: Telecommunications
Survey question: Q5
Response: Cost, Performance
Code: Cost & TCO
Type: Lever
Research question: RQ1
Industry: Telecommunications
Survey question: 8
Response: Avoid API cost
Code: Cost & TCO
Type: Lever
Research question: RQ3
Industry: Telecommunications
Survey question: 9
Response: Log summarisation
Code: No keyword match
Type: NA
Research question: NA
Industry: Telecommunications
Survey question: 10
Response: Licensing clarity
Code: Licensing clarity
Type: Gate
Research question: RQ2
Industry: Telecommunications
Survey question: 11
Response: Repository rating
Code: No keyword match
Type: NA
Research question: NA
Industry: Telecommunications
Survey question: 12
Response: Compare ROI
Code: No keyword match
Type: NA
Research question: NA
Industry: Telecommunications
Survey question: Q5
Response: Performance, SLAs
Code: Performance sufficiency
Type: Lever
Research question: RQ1
Industry: Telecommunications
Survey question: 8
Response: Reliable delivery
Code: No keyword match
Type: NA
Research question: NA
Industry: Telecommunications
Survey question: 9
Response: Chatbot for tech support
Code: Support & SLAs
Type: Lever
Research question: RQ3
Industry: Telecommunications
Survey question: 10
Response: Integration
Code: No keyword match
Type: NA
Research question: NA
Industry: Telecommunications
Survey question: 11
Response: HF connector kits
Code: No keyword match
Type: NA
Research question: NA
Industry: Telecommunications
Survey question: 12
Response: SLA before openness
Code: No keyword match
Type: NA
Research question: NA
Industry: Telecommunications
Survey question: Q5
Response: Customization, Support
Code: Customization & PEFT
Type: Lever
Research question: RQ1
Industry: Telecommunications
Survey question: 8
Response: Adaptability
Code: No keyword match
Type: NA
Research question: NA
Industry: Telecommunications
Survey question: 9
Response: Customer ticket triage
Code: Customization & PEFT
Type: Lever
Research question: RQ3
Industry: Telecommunications
Survey question: 10
Response: Tooling
Code: No keyword match
Type: NA
Research question: NA
Industry: Telecommunications
Survey question: 11
Response: Workflow templates
Code: No keyword match
Type: NA
Research question: NA
Industry: Telecommunications
Survey question: 12
Response: Balance support vs autonomy
Code: Support & SLAs
Type: Lever
Research question: RQ3
Industry: Telecommunications
Survey question: Q5
Response: Performance, Cost
Code: Cost & TCO
Type: Lever
Research question: RQ1
Industry: Telecommunications
Survey question: 8
Response: Cost-effective pipeline
Code: Cost & TCO
Type: Lever
Research question: RQ3
Industry: Telecommunications
Survey question: 9
Response: Internal Q&A bot
Code: No keyword match
Type: NA
Research question: NA
Industry: Telecommunications
Survey question: 10
Response: Limited resources
Code: No keyword match
Type: NA
Research question: NA
Industry: Telecommunications
Survey question: 11
Response: Step-by-step MLOps guide
Code: No keyword match
Type: NA
Research question: NA
Industry: Telecommunications
Survey question: 12
Response: Feasibility → ROI
Code: No keyword match
Type: NA
Research question: NA
Industry: Telecommunications
Survey question: Q5
Response: Performance, Compliance
Code: Compliance
Type: Gate
Research question: RQ1
Industry: Telecommunications
Survey question: 8
Response: Data control
Code: No keyword match
Type: NA
Research question: NA
Industry: Telecommunications
Survey question: 9
Response: Intent detection model
Code: No keyword match
Type: NA
Research question: NA
Industry: Telecommunications
Survey question: 10
Response: Security
Code: Security posture
Type: Gate
Research question: RQ2
Industry: Telecommunications
Survey question: 11
Response: Compliance docs
Code: Compliance
Type: Gate
Research question: RQ2
Industry: Telecommunications
Survey question: 12
Response: Feasibility → SLA
Code: No keyword match
Type: NA
Research question: NA