AI Guide for CTOs

The million dollar question: What to do with AI?

AI is fundamentally reshaping software development. As a CTO, you’re under constant pressure from business leaders, product managers, and CEOs to leverage AI for delivery speed. But adopting AI isn’t just about efficiency—AI enables companies to generate more ideas and bring greater ideas to market than before. AI makes developers and CTOs creators again. AI introduces new challenges: maintaining code quality, redefining developer roles, and keeping up with rapidly evolving AI models and tools. After countless conversations with CTOs and hands-on AI experimentation, here’s my actionable guidance for CTOs navigating the AI transformation.

Overcommunicate

Communicate that you’re doing AI. And talk about AI. And talk about AI. And talk about AI. Until everyone knows you stand for AI and tell you to stop being annoying.

Don’t treat it as a sidegig

AI is not a sidegig. It needs to be the center of your tech vision and strategy. Don’t treat it like a sidegig, it’s the main course.

AI is not cloud

AI is a distruptive technology. Too many of the CTOs I meet treat is as just another technology like cloud, to decrease cost and increase efficiency. AI is not. AI is disruptive and will change all software development around it. Those companies who thought the internet was just like telefax, are no longer.

Experiment more

No one knows what is going on. The futuree is wide open. You need to experiment more with AI, what tools work for you, what tools do not work for you. Do you need your own AI model? Do you need to fine tune an AI model instead of writing source code? Constant experimentsx let you learn about AI and you, something no one can do for you.

Hire people - interns if no money - to find out what AI means for you

With your services and your data and your business model, only you know what AI means for you. Hire people to find out what AI means for your company. If you don’t have the money, hire some interns now.

Have a new product funnel

Experimenting more needs to have process. One suitable process, depending on the size of your company, is to produce one Prototype per day. Prototypes are for playing around and finding out if an idea makes sense. People can take a look at the prototype and discuss it.

From the prototypes, find the one that has the highest potential. Once a week create an MVP from that prototype. The MVP is there to show it to customers and discuss it with customers, find out it they need it.

AI needs more guardrails

AI needs more control. What worked when the guardrails were in the heads of the developers, now needs to be explicit. Architecture guidelines, screenshots, domain models, coding style, security requirements. All of these need to be documented in the project so the AI will do the right thing.

Human vs AI ownership of code

Declare all modules and microservices as either AI or Human owned. AI owned modules need different rules and guardrails than human owned modules. What do you need to confidently deploy AI owned modules to production? My tip would be to read the test summary of the AI module - like in the old days where as CTO you would take a look at the manual test list and decide: If all of that works, I’m confident we can deploy.

For human owned modules: All code generated by AI here is onwed by the developer who told the AI to produce it. In a crisis, “the AI wrote this” is not a valid defense. You generate it, you own it. You need to understand the code, review the code, test the code. It’s your code, just like with autocomplete before (“The IDE autocompled the line, I didn’t write it” was never an excuse).

Over time, migrate human owned modules to AI owned modules.

Have people with AI in their title

Only if people have AI in their title, it will be in their mind 100% of the time. And then there is someone in your org who relentlessly pushes AI.

Have teams with AI in their name

You don’t want to have taken AI away from you. To make it clear, that AI is with technology, rename data team to “AI & Data”, rename “DevOps” to “AI Infrastructure”. Expect resistance from all sides.

Have MCP everywhere

MCP is an easy step towards utilizing AI. Add MCP to all your services. Perhaps MCP will even replace REST in your microservices or public API.

Set minimum standards for AI usage

Not enough CTOs talk about their expectations. AI is no different here. Tell developers what your expactations around AI are. Set a minumum standard for AI usage, like “Everyone uses Cursor”, or “Bugs are fixed with Claude Code”

Promote people to “Product Engineer”

The developer role might change - as with every revolution (the internet brought us DevOps, CTOs and product managers). One guess is that with AI developers will take up more product reponsibility and we will see product engineers. If this is your strategy, define the role and requirements and promote people to it if they are able to do it. This creates pressure to not be left behind.

Have an AI strategy

Have an AI strategy.

Have an AI policy

Have an AI policy around AI usage, data usage, third party providers etc.

AI is not about “more code, faster!”

AI has many benefits for developers, finding bugs, fixing bugs, explaining code, suggesting migrations, summarizing framework usage, and much more. If you’re AI usage is focused on generating code, and this is all what you communicate to developers, you’re doing it wrong.

Have prompt training

Prompting is like project management. Everyone thinks they can just do it, there is nothing to it. But there is a huge difference between good and bad project management - one reason many projects go bad.

The same happens with prompting. Everyone thinks they can just do it, there is nothing to it. But there is a huge difference between good and bad prompting.

Invest in prompt training.

Frequently Asked Questions (FAQ) about AI for CTOs

What are the key benefits of AI adoption for software development teams?

AI can help developers by automating repetitive tasks, finding and fixing bugs, improving code quality, suggesting code completions, and providing insights into codebases. This leads to increased productivity, faster delivery, and higher job satisfaction for developers.

How can CTOs encourage developers to embrace AI tools?

CTOs can set clear expectations, provide training on AI tools and prompt engineering, and create a supportive environment for experimentation. Recognizing and rewarding early adopters and sharing success stories can also motivate teams to use AI effectively.

What should an AI policy for engineering teams include?

An AI policy should cover responsible AI usage, data privacy, security, acceptable third-party tools, intellectual property considerations, and guidelines for prompt engineering. It should also address how to handle sensitive data and compliance requirements.

How does AI change the role of developers?

AI enables developers to focus more on problem-solving, product thinking, and creative tasks, while automating routine coding. This shift may lead to new roles such as “Product Engineer,” where developers take on more product responsibility and collaborate closely with stakeholders.

Why is prompt training important for developers using AI?

Effective prompt engineering is crucial for getting accurate and useful results from AI tools. Training helps developers understand how to communicate with AI systems, craft better prompts, and avoid common pitfalls, leading to more reliable and valuable outcomes.

What are common challenges when introducing AI to engineering teams?

Challenges include resistance to change, lack of understanding of AI capabilities, concerns about job security, and integration with existing workflows. Addressing these through education, clear communication, and leadership support is essential for successful AI adoption.

How can CTOs measure the impact of AI on their teams?

CTOs can track metrics such as code quality, bug resolution time, developer satisfaction, feature delivery speed, and adoption rates of AI tools. Regular feedback from developers and stakeholders helps refine AI strategies and maximize benefits.

Is AI only about generating code faster?

No, AI offers much more than just faster code generation. It can assist with debugging, code reviews, documentation, learning new frameworks, and even supporting product decisions. Focusing solely on speed misses the broader value AI brings to engineering teams.