𝐇𝐨𝐰 𝐚𝐫𝐞 𝐲𝐨𝐮 𝐮𝐬𝐢𝐧𝐠 𝐀𝐈 𝐭𝐨 𝐞𝐧𝐚𝐛𝐥𝐢𝐧𝐠 𝐲𝐨𝐮𝐫 𝐢𝐧𝐭𝐞𝐫𝐧𝐚𝐥 𝐝𝐚𝐭𝐚/𝐈𝐏?

Every company embarking on this journey learns quickly that AI isn’t a plug-and-play solution. It’s a cycle of continuous 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠, 𝐚𝐝𝐚𝐩𝐭𝐢𝐧𝐠, 𝐚𝐧𝐝 𝐞𝐯𝐨𝐥𝐯𝐢𝐧𝐠.

On average, organizations will go through 𝐭𝐡𝐫𝐞𝐞 𝐭𝐨 𝐟𝐢𝐯𝐞 𝐫𝐞𝐢𝐧𝐯𝐞𝐧𝐭𝐢𝐨𝐧𝐬 during their AI adoption:

“𝐼𝑛 𝑚𝑎𝑛𝑦 𝑤𝑎𝑦𝑠 𝑡ℎ𝑒𝑠𝑒 𝑟𝑒𝑖𝑛𝑣𝑒𝑛𝑡𝑖𝑜𝑛𝑠 𝑤𝑖𝑙𝑙 𝑛𝑜𝑡 𝑏𝑒 𝑒𝑎𝑠𝑦, 𝑖𝑡 𝑤𝑖𝑙𝑙 𝑏𝑒 𝑝𝑎𝑖𝑛𝑓𝑢𝑙, 𝑎𝑛𝑑 𝑦𝑒𝑡 𝑖𝑡’𝑠 𝑡ℎ𝑒 𝑐𝑜𝑚𝑝𝑎𝑛𝑖𝑒𝑠 𝑝𝑒𝑜𝑝𝑙𝑒 𝑎𝑛𝑑 𝑐𝑢𝑙𝑡𝑢𝑟𝑒 𝑡ℎ𝑎𝑡 𝑤𝑖𝑙𝑙 𝑡𝑎𝑘𝑒 𝑖𝑡 𝑖𝑛𝑡𝑜 𝑡ℎ𝑒 𝑓𝑢𝑡𝑢𝑟𝑒”. -𝐷𝑜𝑢𝑔 𝑆ℎ𝑎𝑛𝑛𝑜𝑛

1. Defining the vision: What problems do we solve? Where do we innovate?

2. Experimenting in action: What works? What breaks?

3. Integrating and scaling: How do we operationalize AI without disrupting the business.

This iterative process isn’t a flaw it’s the necessary work of transformation. What’s more, the companies that succeed don’t just use AI, they empower themselves to grow with it. That’s where frameworks like 𝐆𝐚𝐮𝐠𝐞, 𝐀𝐧𝐚𝐥𝐲𝐳𝐞, 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐞, 𝐀𝐝𝐚𝐩𝐭 (𝐆𝐀𝐀𝐀) come into play. 𝐀𝐈 𝐞𝐯𝐨𝐥𝐯𝐞𝐬, 𝐚𝐧𝐝 𝐬𝐨 𝐬𝐡𝐨𝐮𝐥𝐝 𝐲𝐨𝐮𝐫 𝐨𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧.

𝐊𝐞𝐲 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲𝐬 𝐟𝐨𝐫 𝐀𝐈 𝐒𝐮𝐜𝐜𝐞𝐬𝐬:

1. 𝐒𝐭𝐚𝐫𝐭 𝐬𝐦𝐚𝐥𝐥. Use pilots to learn where AI can create meaningful impact. However, usecases maybe be too early for your organization. Start with areas.

𝑌𝑒𝑠 𝐼 ℎ𝑒𝑎𝑟 𝑚𝑎𝑛𝑦 𝑛𝑜𝑤 𝑠𝑎𝑦𝑖𝑛𝑔 𝑝𝑖𝑙𝑜𝑡𝑠??, 𝑦𝑒𝑠, 𝑖𝑡’𝑠 𝑛𝑒𝑤 𝑎𝑛𝑑 𝑜𝑟𝑔𝑎𝑛𝑖𝑧𝑎𝑡𝑖𝑜𝑛𝑠 ℎ𝑎𝑣𝑒 𝑡𝑜 𝑙𝑒𝑎𝑟𝑛… 𝑑𝑜 𝑝𝑖𝑙𝑜𝑡𝑠.

2. 𝐎𝐰𝐧 𝐲𝐨𝐮𝐫 𝐝𝐚𝐭𝐚. Control over your data is the foundation for long-term success.

𝑇ℎ𝑖𝑠 ℎ𝑎𝑠 𝑏𝑒𝑒𝑛 𝑠𝑎𝑖𝑑 𝑎𝑔𝑎𝑖𝑛 𝑎𝑛𝑑 𝑎𝑔𝑎𝑖𝑛, 𝑎𝑛𝑑 𝑦𝑒𝑡 𝑤𝑒 𝑎𝑟𝑒 𝑠𝑡𝑖𝑙𝑙 𝑎𝑠𝑘𝑖𝑛𝑔 𝑓𝑜𝑟 𝑔𝑜𝑜𝑑 𝑑𝑎𝑡𝑎, 𝑠𝑜𝑢𝑟𝑐𝑒 𝑑𝑎𝑡𝑎 𝑎𝑛𝑑 𝑛𝑜𝑤 𝑔𝑟𝑜𝑢𝑛𝑑𝑖𝑛𝑔 𝑑𝑎𝑡𝑎

3. 𝐀𝐝𝐚𝐩𝐭 𝐜𝐨𝐧𝐬𝐭𝐚𝐧𝐭𝐥𝐲. Every stage of AI integration will demand new ways of thinking.

𝐽𝑢𝑠𝑡 𝑤𝑎𝑖𝑡 𝑡𝑖𝑙𝑙 𝑦𝑜𝑢 𝑠𝑒𝑒 ℎ𝑜𝑤 𝐿𝑎𝑟𝑔𝑒 𝐺𝑒𝑜𝑠𝑝𝑎𝑡𝑖𝑎𝑙 𝑀𝑜𝑑𝑒𝑙𝑠 (𝐿𝐺𝑀𝑠) 𝑎𝑟𝑒 𝑠𝑒𝑡 𝑡𝑜 𝑡𝑟𝑎𝑛𝑠𝑓𝑜𝑟𝑚 ℎ𝑜𝑤 𝑤𝑒 𝑠𝑒𝑒 𝑎𝑛𝑑 𝑛𝑎𝑣𝑖𝑔𝑎𝑡𝑒 𝑡ℎ𝑒 𝑝ℎ𝑦𝑠𝑖𝑐𝑎𝑙 𝑤𝑜𝑟𝑙𝑑.

“𝑇ℎ𝑒 𝑐𝑜𝑚𝑝𝑎𝑛𝑖𝑒𝑠 𝑡ℎ𝑎𝑡 𝑡ℎ𝑟𝑖𝑣𝑒 𝑤𝑖𝑡ℎ 𝐴𝐼 𝑤𝑜𝑛’𝑡 𝑗𝑢𝑠𝑡 𝑖𝑚𝑝𝑙𝑒𝑚𝑒𝑛𝑡 𝑖𝑡. 𝑇ℎ𝑒𝑦’𝑙𝑙 𝑏𝑒𝑐𝑜𝑚𝑒 𝑓𝑢𝑛𝑑𝑎𝑚𝑒𝑛𝑡𝑎𝑙𝑙𝑦 𝑑𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑡 𝑜𝑟𝑔𝑎𝑛𝑖𝑧𝑎𝑡𝑖𝑜𝑛𝑠 𝑏𝑒𝑐𝑎𝑢𝑠𝑒 𝑜𝑓 𝑖𝑡.” -𝐷𝑜𝑢𝑔 𝑆ℎ𝑎𝑛𝑛𝑜𝑛

If you’re looking for AI to simply “fix things,” you’re already behind. The real question is:

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