Is Your AI Strategy Backwards?

It might be if you're trying to revolutionise everything at once.

There seem to be the same two patterns, companies either going all-in on AI (and crash) or avoid it entirely (and fall behind).

The smart approach isn't necessarily revolution, instead it's a systematic evolution, experimenting without over-committing. 

In practice, it looks like this:

🧪 20% 𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧 𝐁𝐮𝐝𝐠𝐞𝐭: Allocate limited resources to AI experimentation, not a full-blown transformation.

⚙️ 𝐂𝐨𝐫𝐞 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 𝐒𝐭𝐚𝐲 𝐒𝐭𝐚𝐛𝐥𝐞: Don't rebuild your CRM just because ChatGPT exists. Protect what already works.

✅ 𝐏𝐫𝐨𝐯𝐞𝐧 𝐋𝐋𝐌 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 𝐅𝐢𝐫𝐬𝐭: Start with documented use cases that are already delivering value for others.

🔬𝐄𝐝𝐠𝐞 𝐂𝐚𝐬𝐞 𝐄𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭𝐬: Test AI on non-critical processes where failure is cheap and lessons are valuable.

An example could be: Don't replace your entire project management system. Keep your proven framework but then:

👉 Add AI for proposal writing (a proven workflow)

👉 Experiment with AI for initial risk assessments (an edge case test)

👉 Limit the AI budget to 10% of total PM costs (a controlled commitment)

AI isn't always going to be a system replacement, but a capability enhancement. 

Don't underestimate the hidden complexities of integration, data security, and the need for new team skills.

🎯 𝐓𝐨 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐥𝐞𝐚𝐝𝐞𝐫𝐬:  Your competitive advantage isn't having the most AI, it's having AI that measurably improves business outcomes without creating new risks.

🎯 𝐓𝐨 𝐩𝐫𝐨𝐣𝐞𝐜𝐭 𝐦𝐚𝐧𝐚𝐠𝐞𝐫𝐬:  AI should enhance your delivery capabilities, not replace your proven methodologies. Think of it as a tool for progressive elaboration, not a whole new playbook.

What's one "small bet" your team is making on AI right now?