Straight Facts on AI Agents in Production
AI agents are reshaping industries, from data engineering to large-scale simulations and production-ready LLMs. Our in-depth research helps leaders navigate this evolving landscape with clarity. Stay ahead with expert analysis—free and accessible.
AI Agent Invasion? What Experts Expect for 2025
The AI Agent Invasion has started. We are in the early stages. Experts share their expectations and how you can prepare yourself. Our takeaway: Don’t see AI agents as competitors; see them as catalysts for your own professional growth. By automating repetitive tasks, focusing on human-centric skills, and actively learning to collaborate with AI, you can future-proof your career and increase your value within any organization. Check out how
Putting Agents in Production: CI/CD Matters
Prosus and MLOps.Community hosted the Agents in Production Event on November, 13th. The event featured top speakers like Thomas Wolf - Co-Founder of HuggingFace, Prashanth Chandrasekar - CEO of Stack Overflow and Nathan Benaich from Air Street Capital, along with experts from OpenAI, Stanford, Replit, and more! The event focused on bringing AI agents into real-world production. YouGot.Us conducted a research study about AI Agents with over 320 participants.
A top research outcome was how early the entire AI Agent Hype is. More than half of surveyed organizations still rate AI Agents as ‘not or slightly critical.’ However, teams that emphasize CI/CD view them as much more essential. This highlights how DevOps Best Practices may accelerate the value of AI within daily operations.
Additional Insights
Cross-functional collaboration also corresponds to a slightly higher appreciation for AI, indicating that shared ownership and integrated workflows could amplify AI’s perceived impact.
Investing in training and skill development stands out as another success factor: teams that strengthen internal AI competencies might see AI agents as playing a more critical role in everyday operations.
Straight Facts: Startups are LLM Mature
For entrepreneurs and business owners eager to bring Large Language Models (LLMs) into their organizations, the process often raises essential questions: Where can LLMs add the most value? What technical resources are required, and how can data privacy be maintained? Our 2024 AI/LLM in Production study answers these questions and more, guiding practitioners and beginners.
The findings from the “Artificial Intelligence in Production” event, held in February 2024, reveal a stark disparity in organizational LLM maturity across industries and company sizes. AI startups and organizations within the ML sector are leading in LLM expertise, with a significant proportion of respondents identifying as Experts or Advanced in their adoption and application of these technologies. In contrast, traditional industries such as Data Analytics, Healthcare, and large enterprises, including Fortune 1000 companies, report lower levels of maturity. Our study highlights that while smaller, AI-focused companies are making strides in leveraging LLMs, many larger and more established businesses are still in the early stages of adoption. This trend underscores the critical need for greater investment in ML capabilities and upskilling within traditional industries to remain competitive in an increasingly AI-driven market.
About MLOps Community
The MLOps Community addresses the rapidly expanding necessity for sharing real-world Machine Learning Operations (MLOps) best practices among engineers actively working in the field. Although MLOps and DevOps share considerable common ground, the distinctions between them are just as significant. Recognizing the need for a dedicated platform to tackle the distinct challenges encountered daily in constructing production AI/ML pipelines, the MLOps Community was born. United by a shared commitment to innovation and excellence, MLOps Community offers a space where professionals can collaborate, share insights, and learn from each other. Discover more and become part of the growing network at MLOps Community.
About YouGot.Us Insights
At YouGot.us Insights, we specialize in market research across data engineering, AI, AI agents, and data science. Our mission is to turn data into actionable insights, helping businesses innovate and stay ahead. We collaborate with leading communities and organizations like MLOps.community, Prosus, and others, fostering connections across data engineering, MLOps, and data science. Guided by a commitment to open research and knowledge sharing, we strive to inspire progress and innovation. Our online community, Machine Learning Hangout, brings together professionals to exchange insights and ideas, amplifying the value of shared knowledge.
Insights: Uneven Access to ML Resources Across Industries and Company Sizes
Access to high-cost machine learning resources like GPUs and cloud platforms varies widely across industries, company sizes, and team sizes. This recent YouGot.us study, based on data from the ‘Data Engineering for AI’ event, highlights significant disparities in resource availability.
Our findings underscore the importance of developing strategies to democratize access to ML resources, enabling broader innovation across all sectors and company sizes.
Access to high-cost machine learning (ML) resources like GPUs and cloud platforms (e.g., AWS, Databricks) varies widely across industries, company sizes, and team sizes. This recent study, based on data from the ‘Data Engineering for AI’ event, highlights significant disparities in resource availability.
Key Findings
Industry Differences: Tech-heavy sectors such as Computers/Technology and Energy/Mining report better access to ML resources, with a majority rating their access as “Good” or “Excellent.” In contrast, industries like Retail/Sales and Insurance/Risk Assessment struggle, with few participants reporting strong resource availability. This gap may hinder these industries’ ability to keep up with tech-driven innovation.
Impact of Company and Team Size: Larger companies (10,000+ employees) and teams report higher access to ML resources, while smaller organizations face significant challenges. Limited budgets and competing priorities likely drive this disparity, putting smaller companies at a disadvantage in leveraging data-driven insights.
Implications: These access gaps highlight the need for more equitable resource availability across industries. Without affordable and scalable access to ML infrastructure, smaller and less tech-focused sectors risk falling behind in an increasingly data-reliant landscape.
Watch YouGot.Us Co-Founder Martin Stein on the Posit Data Science Hangout Podcast
Martin Stein is a seasoned data scientist, entrepreneur, and Chief Analytics Officer. With over 20 years of experience in AI, machine learning, and data science, he brings a wealth of expertise, developing cutting-edge MarTech solutions that empower marketing leaders to make data-driven decisions to maximize their investments. Stein is also the co-founder of Bend.ai, an AI/machine learning company acquired by Conversion Logix. His career includes significant roles at Apple, IDG/IDC, G5, RStudio, and startups like FileThis, Defined, and Union. Throughout his career, he has demonstrated a proven ability to build and scale products to over $200 million in revenue.
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About YouGot.us
Accelerating AI Transformation: At YouGot.Us, Co-Founder Martin Stein and his community team provide end-to-end advice for organizations looking to harness the full power of AI. His expertise in the applied AI space equips him to guide your business through every step of your digital transformation journey—from strategic planning to practical implementation.