The 7 Deadly Sins of AI

🔗https://blog.tobiaszwingmann.com/p/the-7-deadly-sins-of-ai

Tobias Zwingmann explains the seven deadly sins of AI. Despite over 70% of companies adopting AI in at least one business function and investments soaring to $276.1 billion, more than 80% of AI projects fail. This high failure rate is often attributed to human behavior and organizational issues rather than technological shortcomings. Common pitfalls include:

• 𝗪𝗿𝗮𝘁𝗵 - 𝗺𝗶𝘀𝗮𝗹𝗶𝗴𝗻𝗲𝗱 𝗲𝘅𝗽𝗲𝗰𝘁𝗮𝘁𝗶𝗼𝗻𝘀: "AI isn't a destination you reach - it's a journey you take". "AI is only as good as the people who use it. The human factor decides AI success, not the amount of compute resources you throw at it".

• 𝗣𝗿𝗶𝗱𝗲 - 𝗿𝗲𝘀𝗶𝘀𝘁𝗮𝗻𝗰𝗲 𝘁𝗼 𝗲𝘅𝘁𝗲𝗿𝗻𝗮𝗹 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀: "Companies didn't fail because they couldn't adapt. They failed because they were too proud to adapt". "The question isn't whether you can build it... The question is whether you should"

• 𝗟𝘂𝘀𝘁 - 𝘂𝘀𝗶𝗻𝗴 𝗔𝗜 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗮 𝗰𝗹𝗲𝗮𝗿 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗽𝘂𝗿𝗽𝗼𝘀𝗲: "When we get too excited about what AI can do, we tend to forget about what our business actually needs".

• 𝗦𝗹𝗼𝘁𝗵 - 𝗟𝗮𝗰𝗸 𝗼𝗳 𝗖𝗼𝗺𝗺𝗶𝘁𝗺𝗲𝗻𝘁: "Small enough to implement quickly, big enough to matter, clear enough to measure success. The key is to start small, but start NOW. Because while you're analyzing, your competitors are learning".

• 𝗚𝗹𝘂𝘁𝘁𝗼𝗻𝘆 - 𝗧𝗼𝗼 𝗠𝘂𝗰𝗵 𝗔𝗜 𝗞𝗶𝗹𝗹𝘀 𝗦𝘂𝗰𝗰𝗲𝘀𝘀: "Imagine a starving person suddenly get access to a buffet. That's how some companies approach AI, trying to consume everything at once".

• 𝗚𝗿𝗲𝗲𝗱 - 𝗘𝘅𝗽𝗲𝗰𝘁𝗶𝗻𝗴 𝗤𝘂𝗶𝗰𝗸 𝗥𝗲𝘁𝘂𝗿𝗻𝘀: "It's better starting in familiar problem domains and exploring use cases with relatively established AI technology before progressing to more advanced, game-changing fields.

• 𝗘𝗻𝘃𝘆 - 𝗖𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗼𝗿 𝗜𝗺𝗶𝘁𝗮𝘁𝗶𝗼𝗻: "During every gold rush, there are two kinds of people: those digging for gold and those selling shovels. Don't go buying shovels just because everyone around you is buying shovels". "The question isn't What AI tools is everyone buying?, but What problems are we trying to solve?".

Addressing these human and organizational factors is crucial for the success of AI initiatives, underscoring the need for realistic expectations, openness to external solutions, strategic alignment, and sustained commitment.