Key Insights
Key takeaways from "Enterprise Generative AI adoption" report published by AI Infrastructure Alliance
CFTE summarised “Enterprise Generative AI adoption” report by AI Infrastructure Alliance. The report surveyed over 1000 companies with more than 1B USD in revenue to see how fast they’re adopting generative AI and what their challenges are as they embrace these powerful new systems.
Key Aspects
- The report investigates the integration of AI and machine learning, especially focusing on the impact of Large Language Models (LLMs) like ChatGPT in enterprises.
Table of Contents
- Introduction and Background
- Demographics of Survey Respondents
- Core Questions and Survey Insights
- Enterprise Priorities for AI Adoption
- Challenges and Blockers in AI Implementation
- Model Selection and Implementation Strategies
- ROI Challenges and Team Dynamics
- Future Prospects and Expectations from AI
- Regulatory Challenges and Compliance
- Overall Impact and Transformation in Enterprises
Key Findings and Insights
This report will give you an insight into:
- Adoption Priority: 67.2% of surveyed enterprises prioritize adopting LLMs and generative AI, with 49% rating it a high priority and 18.2% considering it essential.
- Customization and Flexibility: Enterprises focus on customization and flexibility to protect valuable assets, knowledge, and intellectual property. However, they also face challenges with compliance and the high costs associated with AI implementation.
- Policies and Resources for AI Implementation: 88.3% of organisation polled plan to implement policies specific to the adoption of Generative AI in your enterprise and/or implement internal policies around its usage. Only 41.2% believe they have the right budget and personnel to effectively implement these technologies.
- Model Selection Strategy: Most enterprises prefer using off-the-shelf models or cloud-based APIs for generative AI, moving away from building their own models from scratch. There's a growing trend towards fine-tuning foundational models with in-house data.
- ROI and Organisational Challenges: Only 34.2% of companies feel confident in demonstrating the return on investment for their AI/ML initiatives. There's a split in control over AI transformation, budgeting, and prioritization across data science, engineering, and IT teams.
- Unified Platforms Desire: 87.7% of organisation polled are seeking to standardise on a single AI/ML platform across departments (versus using different point solutions for different teams).
- Future Expectations: Businesses expect AI to bring more value in the next 18 months, particularly in speeding up market times, development cycles, improving customer experience, and reducing costs.
Key infographics
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