42:I[58016,["/_next/static/chunks/b5ea50e123aa84a6.js","/_next/static/chunks/5e17b2e37c80a96c.js","/_next/static/chunks/de880ec5a0241b38.js","/_next/static/chunks/48003610e91209eb.js","/_next/static/chunks/15a1b1fbd3cf7d4a.js","/_next/static/chunks/81ce7bf5f20dbd5a.js","/_next/static/chunks/53f751967ccc7d59.js","/_next/static/chunks/903c358828afe0bc.js","/_next/static/chunks/00da439d4db502cc.js","/_next/static/chunks/88bed98e5cd5e639.js"],"ProcessEpisodeButton"] 45:I[45269,["/_next/static/chunks/b5ea50e123aa84a6.js","/_next/static/chunks/5e17b2e37c80a96c.js","/_next/static/chunks/de880ec5a0241b38.js","/_next/static/chunks/48003610e91209eb.js","/_next/static/chunks/15a1b1fbd3cf7d4a.js","/_next/static/chunks/81ce7bf5f20dbd5a.js","/_next/static/chunks/53f751967ccc7d59.js","/_next/static/chunks/903c358828afe0bc.js","/_next/static/chunks/00da439d4db502cc.js","/_next/static/chunks/88bed98e5cd5e639.js"],"SignupCTA"] 47:I[56939,["/_next/static/chunks/b5ea50e123aa84a6.js","/_next/static/chunks/5e17b2e37c80a96c.js","/_next/static/chunks/de880ec5a0241b38.js","/_next/static/chunks/48003610e91209eb.js","/_next/static/chunks/15a1b1fbd3cf7d4a.js","/_next/static/chunks/81ce7bf5f20dbd5a.js","/_next/static/chunks/53f751967ccc7d59.js","/_next/static/chunks/903c358828afe0bc.js","/_next/static/chunks/00da439d4db502cc.js","/_next/static/chunks/88bed98e5cd5e639.js"],"EpisodeTranscript"] 3c:["$","div",null,{"className":"flex flex-col gap-2 text-sm text-muted-foreground","children":[["$","span",null,{"className":"inline-flex items-center gap-2 bg-secondary/50 px-3 py-1 rounded-full","children":[["$","svg",null,{"className":"w-4 h-4","fill":"none","stroke":"currentColor","viewBox":"0 0 24 24","children":["$","path",null,{"strokeLinecap":"round","strokeLinejoin":"round","strokeWidth":2,"d":"M8 7V3m8 4V3m-9 8h10M5 21h14a2 2 0 002-2V7a2 2 0 00-2-2H5a2 2 0 00-2 2v12a2 2 0 002 2z"}]}],["$","time",null,{"dateTime":"2025-03-05T15:00:13.000Z","children":"Wed Mar 05 2025"}]]}],null]}] 43:T4eb,What does the near term future of Enterprise AI look like? A scramble to get valuable, cost-effective, enterprise-grade AI solutions into production, with rigorous governance at scale. If that sounds like a tall order - that’s because it is! However, large advanced organizations (yes, even outside of the tech sector) are already seeing success deploying AI solutions and we can expect the pace to accelerate rapidly in the next 1-2 years.

To find out how enterprises are gearing up for this challenge we speak with Richard Swakla, AI and ML Specialist at NetApp and get the inside scoop on the trends that are underway and the strategies organizations are putting in place to operationalize meaningful AI outcomes.
Join us as we discuss:

The pressure to shift from experimentation to production
Breaking down the data, infrastructure and organizational silos
Best practices for tackling governance, security and cost challenges
Examples of AI efficiency gains in healthcare, pharma, and beyond

The Future of Enterprise AI? AI in Production!