Making a Success of AI Projects

Thinking about starting an AI project?

Here's the checklist I'd use to help me make it a success:

✅ 𝐅𝐫𝐚𝐦𝐞 𝐲𝐨𝐮𝐫 𝐀𝐈 𝐭𝐡𝐢𝐧𝐤𝐢𝐧𝐠: It’s a business tool, not a side experiment so apply the same strategic intent, and ROI focus you would to any core investment

✅ 𝐓𝐢𝐞 𝐢𝐭 𝐭𝐨 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲: Every project should ladder up to enterprise goals, and AI projects are no different

✅ 𝐌𝐚𝐩 𝐲𝐨𝐮𝐫 𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐞𝐬: Know exactly where the friction is and why AI is the answer

✅ 𝐆𝐞𝐭 𝐲𝐨𝐮𝐫 𝐝𝐚𝐭𝐚 𝐫𝐞𝐚𝐝𝐲: Quality, accessible, and well-governed data is even more critical for AI projects

✅ 𝐁𝐮𝐢𝐥𝐝 𝐭𝐡𝐞 𝐫𝐢𝐠𝐡𝐭 𝐢𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞: scalable, secure, and integrated to manage intensive memory and compute demands

✅ 𝐏𝐥𝐚𝐧 𝐟𝐨𝐫 𝐡𝐚𝐫𝐝𝐰𝐚𝐫𝐞: GPU/TPU capacity matters more than you think

✅ 𝐁𝐚𝐤𝐞 𝐢𝐧 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲: Don't take security risks like adversarial attacks, training data exposure, or system manipulation lightly

✅ 𝐒𝐭𝐚𝐫𝐭 𝐰𝐢𝐭𝐡 𝐚𝐧 𝐌𝐕𝐏: Prove value fast before scaling

✅ 𝐂𝐡𝐞𝐜𝐤 𝐥𝐞𝐠𝐚𝐜𝐲 𝐭𝐞𝐜𝐡 𝐫𝐢𝐬𝐤𝐬: Old systems can block deployment through poor integration, outdated APIs or data silos

✅ 𝐆𝐨𝐯𝐞𝐫𝐧 & 𝐦𝐞𝐚𝐬𝐮𝐫𝐞: Track real business outcomes, not just model accuracy

✅ 𝐌𝐚𝐧𝐚𝐠𝐞 𝐭𝐡𝐞 𝐜𝐡𝐚𝐧𝐠𝐞: Adoption is 80% people, 20% tech, communication, trust and upskilling is key

✅ 𝐌𝐨𝐧𝐢𝐭𝐨𝐫 𝐩𝐨𝐬𝐭-𝐝𝐞𝐥𝐢𝐯𝐞𝐫𝐲: AI isn’t finished at launch, it needs ongoing tuning for drift, bias, and real-world feedback

👉 That’s the playbook I’d take into any AI project.