After 23 years in machine learning and data science, Cao Hong has seen plenty of AI projects come and go. His measure of success is simple: did it create value for the business? Recorded at *EVOLVE26* Singapore, this episode of The AI Forecast brings Paul Muller together with Cao Hong, Principal Director of Data Apps at NCS, to explore how organizations can turn AI experimentation into measurable business impact. That becomes harder as enthusiasm for AI pushes expectations higher. Cao Hong argues that organizations need to define the outcome they’re working toward early and then understand how AI will fit into how the business actually operates. Having worked across research and commercial AI projects, Cao Hong has seen how differently the two environments operate. Research can explore problems whose value may be realized years from now. In business, AI ultimately has to earn its investment. That means connecting the technology to a clear use case and establishing how its performance will translate into business value. *Paul and Cao Hong explore:* - How to define value before investing in an AI project - Why unrealistic expectations can derail AI initiatives - What leaders should consider when selecting AI use cases - How data readiness affects AI outcomes - Why AI governance needs to evolve alongside adoption - Where enterprises can find differentiation as AI models become widely available Cao Hong shares how AI helped a water utility predict demand weeks in advance and rethink decisions years ahead. His takeaway for other enterprises: when everyone can access the same models, the advantage lies in what you can do with your own data. Want to hear more about turning enterprise data into better decisions? Check out https://www.youtube.com/watch?v=5N61LpLFFAE&list=PLe-h9HrA9qfAmGHgsmXUZgLL-T4Xjhlq8&index=9 *Stay in touch with Cao Hong:* Cao Hong on LinkedIn: https://www.linkedin.com/in/hong-cao/ 🔔 Like and subscribe to The AI Forecast, sponsored by Cloudera, to follow our EVOLVE26 Singapore series and stay up to date on the latest conversations about enterprise data and AI. https://www.youtube.com/channel/UCXY5wm6HlBL_Y_8SDxJNR0g?sub_confirmation=1 *Links & Resources* 👉Full Series Playlist: https://www.youtube.com/playlist?list=PLe-h9HrA9qfAmGHgsmXUZgLL-T4Xjhlq8 👉Learn more: https://www.cloudera.com/resources/podcast/the-ai-forecast.html?utm_medium=social-organic&utm_source=youtube&keyplay=cross&utm_campaign=Other---FY27-GLOBAL-Always-On-Cloudera-Podcast&cid=UNGATED&utm_content=youtube-cta *🎧 Listen on your platform of choice:* Spotify: https://open.spotify.com/episode/0uloYVlwiCj3nAeccKPLMp Apple: https://podcasts.apple.com/ie/podcast/when-everyone-has-ai-your-data-sets-you-apart/id1779293119?i=1000792164626 Chapters: 00:00 Enterprise AI Expectations vs Reality 01:14 Escape the AI Pilot Trap 01:40 Fast Four: Fast Learning Superpower 02:59 Personalized AI Assistant Goal 04:08 Capturing Handwritten Data Sets 05:36 Surviving the AI Winter Era 07:07 Overcoming Pattern Recognition Limits 08:51 Research Labs vs Enterprise Needs 11:29 How Consultancies Help Deploy AI 15:00 Why Data Systems Outweigh Models 17:39 Proactive Water Supply Forecasting 22:00 Differentiating With Private Data *Connect with Cloudera* LinkedIn ► https://www.linkedin.com/company/cloudera Facebook ► https://www.facebook.com/cloudera/ X ► https://twitter.com/cloudera Spotify ► https://open.spotify.com/show/102S8zoZR6nmZV0HxZlxZu #TheAIForecast #Cloudera #EnterpriseAI #AIStrategy #DataOps #AIGovernance

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