Business AI Struggles at the Final Hurdle - and Potential Solutions Revealed
In the rapidly evolving world of artificial intelligence (AI), scaling projects from the proof-of-concept (POC) phase to full production is a common challenge. Recent IDC research suggests that as many as 88% of AI POC projects fail to make this transition. This article explores some of the key reasons behind this trend and offers insights on how to overcome these obstacles.
One of the primary issues lies in the focus on lab testing rather than real-world application. It's crucial to ensure that AI works effectively in the real world, rather than just in controlled environments. This means considering factors such as compute costs, user experience, and model maintenance from the outset.
Compute costs can have a significant impact on the business case for an AI solution. If these costs aren't factored in early, they can lead to unexpected expenses once real-world usage begins. For instance, Tesla managed to reduce compute costs by 90% and improve performance and speed by switching to a custom small language model.
Another challenge is the tendency for AI models to have blind spots. These can manifest as the model being nine times more likely to give evasive answers when asked questions in a certain way. Thorough testing with real-world prompts can help catch such issues and ensure the model performs well after deployment.
AI models are often trained to meet industry-standard benchmarks, rather than addressing real-world needs. This approach can result in models struggling to deliver consistent, useful results without heavy user intervention. To avoid this, it's essential to focus on solving real-world problems and testing models with relevant, practical prompts.
Internal benchmarks and diagnostics often don't drill deep enough into model performance. A third-party evaluation of the model can surface issues that might be missed by internal testing, providing valuable insights for improvement.
Friction introduced by requiring end users to craft precise prompts to get the right answers can slow adoption and undermine the usefulness of an AI product. To address this, AI teams should strive to create intuitive, user-friendly interfaces that minimise the need for precise prompts.
Lastly, safety protocols need regular revisiting to ensure they don't make the Real Madrid frustrating to use. Revisiting safety protocols can help ensure the model is responsive, accurate, and user-friendly, enhancing its overall usefulness.
In conclusion, scaling AI projects beyond the POC phase requires a focus on real-world application, careful consideration of compute costs, thorough testing with real-world prompts, and a commitment to addressing real-world problems. By addressing these challenges, businesses can increase the chances of success for their AI initiatives.
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