Why AI needs the Product Lens

I wrote a post yesterday about understanding whether you are managing a project or a product.

AI needs to be viewed through the Product lens, as it's never going to be a project, where you go live and walk away. If you treat AI like a project, your AI initiatives aren't going to deliver sustained value.

𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐲 𝐀𝐈 𝐧𝐞𝐞𝐝𝐬 𝐭𝐨 𝐛𝐞 𝐦𝐚𝐧𝐚𝐠𝐞𝐝 𝐥𝐢𝐤𝐞 𝐚 𝐏𝐫𝐨𝐝𝐮𝐜𝐭:

👉 𝐔𝐬𝐞𝐫 𝐧𝐞𝐞𝐝𝐬 𝐞𝐯𝐨𝐥𝐯𝐞 𝐟𝐚𝐬𝐭𝐞𝐫:  AI capabilities expand rapidly. What users accept today (80% accuracy, basic recommendations) won't satisfy them in six months. Your AI solution must continuously improve or it will feel outdated almost immediately.

👉 𝐀𝐝𝐨𝐩𝐭𝐢𝐨𝐧 𝐫𝐞𝐪𝐮𝐢𝐫𝐞𝐬 𝐭𝐫𝐮𝐬𝐭 𝐚𝐧𝐝 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠: People don't just "use" AI, they learn to trust it, understand its boundaries, and integrate it into their workflows. This behavioural change takes time, iteration, and ongoing refinement based on how people actually interact with the system.

👉 𝐓𝐡𝐞 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 𝐧𝐞𝐯𝐞𝐫 𝐬𝐭𝐨𝐩𝐬 𝐞𝐯𝐨𝐥𝐯𝐢𝐧𝐠:  New models emerge quarterly, techniques improve, and competitors advance. An AI solution deployed today will be technically obsolete within a year without continuous updates. You're not building a static tool, you're maintaining a capability that must keep pace with a rapidly advancing field.

👉 𝐅𝐞𝐞𝐝𝐛𝐚𝐜𝐤 𝐥𝐨𝐨𝐩𝐬 𝐚𝐫𝐞 𝐞𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥: AI products improve through usage data. Every interaction teaches you what works, what confuses users, where the model fails, and what features matter most. Without a stable team empowered to act on these insights, you're flying blind, and your AI will stagnate.

👉 "𝐃𝐨𝐧𝐞" 𝐦𝐞𝐚𝐧𝐬 𝐢𝐫𝐫𝐞𝐥𝐞𝐯𝐚𝐧𝐭 𝐢𝐦𝐦𝐞𝐝𝐢𝐚𝐭𝐞𝐥𝐲: Unlike traditional software, AI solutions degrade without attention. Models drift as the world changes. Edge cases emerge. User expectations rise. An AI "project" with a fixed end date is guaranteed to underdeliver within months of launch.

𝐓𝐡𝐞 𝐀𝐈 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐌𝐢𝐧𝐝𝐬𝐞𝐭:

Successful AI initiatives require:

✔️𝐃𝐞𝐝𝐢𝐜𝐚𝐭𝐞𝐝 𝐀𝐈 𝐩𝐫𝐨𝐝𝐮𝐜𝐭 𝐭𝐞𝐚𝐦𝐬 with data scientists, engineers, and product managers who stay with the solution long-term

✔️𝐂𝐨𝐧𝐭𝐢𝐧𝐮𝐨𝐮𝐬 𝐦𝐨𝐝𝐞𝐥 𝐦𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 𝐚𝐧𝐝 𝐫𝐞𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠 based on performance metrics and changing patterns

✔️𝐈𝐭𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐫𝐞𝐥𝐞𝐚𝐬𝐞𝐬 that test, learn, and adapt based on real-world usage

✔️𝐎𝐮𝐭𝐜𝐨𝐦𝐞-𝐛𝐚𝐬𝐞𝐝 𝐬𝐮𝐜𝐜𝐞𝐬𝐬 𝐦𝐞𝐭𝐫𝐢𝐜𝐬 that measure business impact, not just model accuracy

✔️𝐎𝐧𝐠𝐨𝐢𝐧𝐠 𝐟𝐮𝐧𝐝𝐢𝐧𝐠 that treats AI as a capability requiring sustained investment, not a one-time capital expense

When you build AI solutions, you are building AI products, living, learning systems that require continuous evolution, dedicated teams, and long-term commitment. NOT a one and done project that you can walk away from post go live.