AI Strategy for CTOs: A Practical Guide to Getting Started
Use AI to fix real problems, not chase hype.
👋 Ciao, Alex here. Welcome to a new free edition of Not Just Bits. Thank you to all the readers and supporters of my work! Every month, I aim to share lightweight and informative resources for CTOs.
Start with what AI can fix, not what it can do
Most AI strategy decks start with definitions. This one starts with fixes.
AI doesn’t have to be complex to be powerful
You don’t need a PhD team. You need:
Clean data
Clear goals
Confidence to ship
Use proven tools.
Pick hosted models like Claude, GPT-4, or other API-based LLMs.
Avoid running your own model unless AI is your product.
It adds cost, risk, and complexity without clear upside for most companies.
Educate your leadership team, not your engineers
Your engineers probably already get it.
Your commercial leads might not.
Run one-hour sessions:
What AI is (in your context)
What it can help with
What it won’t fix
Where it might go wrong
AI literacy at the top prevents bad bets and panic later.
Set up an AI advisory group early
You don’t need in-house experts for everything.
But you do need people who are already using AI to make real progress.
Start small. Form an internal group of people from different teams—ops, product, marketing, support—who are already using AI to improve their work.
Pair them with one or two external advisors who’ve shipped AI in production or navigated the legal side.
This kind of group gives you:
Fast feedback from real users
Early warnings on risk or scale issues
Credibility when you bring ideas to leadership
Build it before you scale your AI effort—not after.
Use real metrics. Don’t chase “transformation”
Your AI strategy should:
Save X hours/month
Cut Y% in cost
Improve Z metric by [something people already track]
No one cares if it’s AI. They care if it works.
Put value over novelty.
Build boring foundations first
Before you get excited:
Clean up your data
Review legal/compliance issues
Have a rollback plan
If you wouldn’t let a human do it without oversight, don’t let an AI do it either.
Use a Simple Matrix to Prioritize AI Work
Not every AI idea is worth building. Some are too early. Some don’t matter.
Use this 3x3 matrix to cut through the noise:
X-axis: Execution readiness: do you have the data, tools, and team to ship a small test fast?
Y-axis: Business value, will it improve revenue, reduce cost, or cut risk?
Start in the top-right corner: ideas that are high-value and easy to try.
Timebox everything else. If it takes more than two weeks to prove value, rethink it.
Use the matrix to focus your AI effort where it counts. Ignore the rest.
Find the Miro template here: https://miro.com/miroverse/ai-strategy-matrix-for-cxos-6muxpuwch0tcgo83/
Start now, not perfect
The longer you wait, the more catch-up you'll need to do.
The best way to learn AI is to try it in production.
Start small. Make mistakes. Course-correct.
Set a clear time limit—1 to 2 weeks max per experiment.
If you can’t see signs of value in that time, stop or adjust.
You’re testing usefulness, not building a final product.
Keep cost low. Keep scope tight. Learn fast.
✅ Before you go:
Please share this post and invite your network to subscribe to the Not Just Bits newsletter.
Feel free to connect with me on LinkedIn.
See you next month! Best, Alex Di Mango