[music] So companies spend millions on AI tools for software engineering. But do we actually know how well these tools work in the enterprise or are these tools just all hype. To answer this and for the past two years, we've been researching the impact of AI on software engineering productivity.And our research is time series because we look at get historical data, meaning we can go back in time. And it's also cross-sectional because we cut across companies. And the way we use to measure most of the of the ...

Core Insights - The discussion at the Fortune Emerging CFO virtual event highlighted how AI is transforming finance and the evolving role of CFOs [1] AI Implementation and Strategy - AI must align with a company's core strategy, with CFOs defining objectives such as efficiency or effectiveness before targeting finance areas [2] - Companies that deploy AI without a broader plan struggle to capture meaningful enterprise value [2] - Training employees to effectively use AI tools is crucial, as improper use can lead to ineffective outcomes [3] ROI and Adoption - CFOs commonly inquire about the ROI for AI, where to start, and whether to buy or build AI solutions [4] - Early adopters of AI are beginning to see positive returns on investment [4] Real-World Applications - CFOs shared experiences of AI enhancing accuracy, forecasting, and productivity, emphasizing the need for iterative learning and collaboration [5] - Webflow's finance team automated routine policy queries using large language models, allowing for more strategic work [6] - INRIX utilizes AI to analyze over 50 petabytes of mobility data, improving reporting and forecasting accuracy to 95% [8] - Greenlight leverages AI for risk management, contract reviews, and educational content, while also fostering a culture of understanding through hackathons [9] Challenges and Learning - Not all AI use cases succeed, and human oversight remains essential [10] - Early experiments with AI tools may not yield immediate success, but persistence can lead to improved outcomes [10][11]

AI ROI and Business Outcomes - Achieving real AI ROI requires focusing on specific business outcomes and problem-solving [4][5] - Infrastructure planning is crucial for optimizing AI investments and achieving a greater return on invested capital [6] - Enterprises should clearly define measurable metrics to gauge the success of AI projects [21] Infrastructure as a Strategic Asset - Data infrastructure is a strategic asset that drives efficiency and optimization for AI projects [8][9] - Integrating infrastructure tightly into the ecosystem maximizes investments and drives ROI [9] - Early AI deployments sometimes overlook infrastructure efficiencies, leading to underutilization and wasted resources [10] Scaling AI Factories - DDN's new enterprise hyperpod, built with Super Micro and powered by NVIDIA, helps enterprises scale AI from pilot to exascale [11] - The Hyper Pod is a pre-engineered platform that simplifies AI inference tuning for various industries, sovereign clouds, and AI factories [11][12] - This platform enables scalable deployment and is optimized for high-performance, high-scale inference or tuning [12] Industry Impact of AI Infrastructure - Healthcare and life sciences benefit from AI in drug discovery, precision medicine, and genomics, improving physician efficiency and patient care [14] - Financial services leverage AI for algorithmic trading, fraud analytics, and risk management [14] - Other industries benefiting from AI include oil and gas, automotive (self-driving cars), and next-generation hyperscalers [15][16] Advice for Enterprise Leaders - Enterprise leaders should clearly define the outcomes they want to drive and the problems they aim to solve with AI [17][18] - Maximizing return on investment in infrastructure assets is essential, considering speed, performance, and utilization [18] - Enterprises should be mindful of their unique goals when deploying AI systems [20]