As boards convene to plan long-term strategy, the questions are pressing: How can enterprises harness Artificial General Intelligence without falling behind competitors? How do we govern AI that can outthink humans? What ethical obligations must we fulfill?
Why do traditional demand forecasting methods fail in volatile markets? How can organizations balance efficiency with sustainability and ESG commitments using AI-driven insights?
Is your product roadmap slowed down because data insights are trapped in silos instead of driving real-time decisions? Are you struggling to differentiate your SaaS product in a crowded market despite investing in AI pilots?
Are your AI pilots stuck in an endless loop of experiments with unpredictable ROI?
Is your enterprise data sitting idle in Snowflake? Are your AI projects stuck in endless pilots with little impact? Are you finding it too costly and unpredictable to scale AI infrastructure outside of Snowflake, with ROI still uncertain?
Let’s be brutally honest: Your LLM project is most likely bleeding money right now instead of driving revenue.
Gen AI for businesses has swiftly evolved from a fringe innovation to a boardroom imperative. Over the past couple of years, leading enterprises in the US have moved beyond conducting mere experiments with chatbots and text generators.
A few years back, AI was just merely a concept and looked cool over sci-fi movies and series. What was once sci-fi fantasy is now an enterprise reality. Wonderful, isn’t it?
Gen AI strategies don’t fail because of lack of vision—they fail because they never leave the lab.
When financial institutions reported over $485 billion in fraud losses in 2023 , the message became clear– it was time to rethink the future of technology in industry practices.
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