For our third CTO Breakfast Roundtable, we brought together CTOs, Heads of Engineering, Product leaders, and investment partners for a direct, practical discussion: What will our engineering teams actually look like as AI becomes part of everyday work?

Setting the Scene

Participants shared why this topic matters so much now.

AI coding tools like GitHub Copilot, Cursor and custom LLMs have moved quickly from interesting prototypes to being used by engineers every day. But the conversation is moving beyond “how do we use this tool” to bigger questions:

• How does this change who we hire and train?
• Do we need fewer engineers? Or just different skills?
• How do we keep security, quality, and compliance in check when code is being generated so fast?
• How do we manage the inevitable hype internally?

As one person put it: “Software engineering has always been a story of more abstraction—from assembler, to higher-level languages, to cloud, and now to prompting and orchestration. This is just the next layer.”

But that doesn’t mean it’s simple.

A Concrete Example from Damilah

By way of an example Aleksandar Karavasilev, CTO at Damilah explained how they deliberately picked a small but real innovation project to see what using AI end-to-end could achieve.

The project? Helping the University of Economics in Skopje improve exam scheduling—a task that normally would take a full team 2–3 months.

Damilah’s approach was to use AI in every step:

• Scope: The team interviewed university staff, recorded the sessions, transcribed it, and immediately ran them through ChatGPT and Perplexity to summarise and define scope in one go.
• Architecture: Using Napkin AI, they generated a solution architecture in about 30 minutes.
• Code: A single developer and an architect used prompting tools to generate the working code.
• Testing: About an hour to validate and check requirements.

Total time? Around six hours from interview to functional prototype.

Aleksandar pointed out it wasn’t a throwaway demo. The university is adopting it for real use, with just a bit more refinement.

His takeaway:

“Instead of 2–3 months with a full team, we did 90% of it in six hours with a tiny team. You can’t do that everywhere, but in the right context it’s game-changing.”

Where AI Delivers Value

This example opened up a lively discussion about where AI tools are already proving valuable.

Several attendees talked about how AI tools are cutting down the time for:

• Discovery and inception: moving from a full sprint to a few hours.
• Prototyping: empowering product managers and designers to spin up clickable prototypes in hours instead of weeks.
• Automating boring tasks: writing repetitive tests or scaffolding code.

The phrase “fail fast and see what flies” came up more than once.

But there was strong agreement the gains aren’t universal. Greenfield projects and quick experiments are ideal for these tools. Brownfield, compliance-heavy, or legacy work? Much harder.

The Buck Still Stops with Humans

Nobody in the room pretended that AI was a silver bullet.

Especially for financial services and banking teams, AI-generated code can’t just be merged in. They described strict security and compliance requirements that demand human review in order to consider:

• Performance problems or concurrency bugs.
• Security vulnerabilities that AI won’t catch.
• SOC 2, auditability, and client expectations.
• IP ownership issues when vendors claim rights to generated code.
• The opaque, “black box” nature of LLMs.

The message was clear: “The buck stops with the human.”

Voices around the table agreed that guardrails and discipline were essential. They discussed needing:

• Strong CI/CD pipelines with security checks.
• Policies defining acceptable AI usage.
• Clear processes for reviewing and approving code.

Shifts in Roles and Skills

One big theme was how AI changes what engineers actually do.

Several participants described the rise of what they called the Product Engineer:

“People who solve business problems end-to-end. Tech is just the means to an end.”

Others pointed to the emergence of super-productive seniors:

“Seniors can use AI to do 5x the work they used to. But that creates a problem—how do we train the next generation?”

The worry is that AI takes away many of the simple, repetitive tasks juniors used to learn on. Without those, how do they build the foundational knowledge needed to review, design, and maintain complex systems?

Voices around the table debated how to handle this:

• Some suggested not letting juniors use AI too early, to force them to learn fundamentals.
• Others felt curiosity should be encouraged but structured, with real mentorship and deliberate training plans.

New Roles and Skills Emerging

There was broad agreement that AI is creating demand for new kinds of roles:

• Prompt Engineers: People who know how to craft effective, context-aware instructions for AI tools.
• Model Integrators: People who understand different models and can chain them together to solve real business problems.
• AI Governance Leads: Defining what’s allowed, securing approvals, and managing risk.

One of the attendees warned:

“If you don’t have guardrails, you’re just setting yourself up for chaos.”

Keeping Product, Design, and Engineering Aligned

A big part of the conversation turned to how AI is changing relationships between teams.

Product managers and designers now have access to tools that let them build rich prototypes in hours.

That’s great for speeding up feedback loops, but it also risks creating silos:

“We spent years getting product, design, and engineering to work together in discovery. Now they’re off doing their own thing again.”

Participants talked about the need for:

• Clear, shared OKRs to keep teams aligned.
• Involving engineering early in discovery, even if AI is making everything faster.
• Creating review processes that make sure those prototypes, if they’re not going to be thrown away, are secure, performant, and maintainable.

As someone summed it up:

“AI won’t fix your process. If you have a crap process, it’ll just make the chaos go faster.”

Managing the Business Hype

Several participants brought up the challenge of managing internal expectations.

“Every CEO is watching videos on LinkedIn about AI and then turning to the CTO saying ‘make this happen.’”

People shared practical advice:

• Channel enthusiasm into small, targeted pilots that deliver real wins.
• Educate business stakeholders on what AI can and can’t do.
• Avoid “solutions looking for a problem.”

One private equity partner noted:

“We see a growing gap between AI-native startups that are lean and fast, and legacy companies buried in compliance and old systems. This is just cloud and Agile all over again, but on steroids.”

What Will Teams Look Like?

Toward the end, the group talked about what their teams might look like in a couple of years.

Ideas included:

• Smaller, more focused teams with tighter domains.
• Closer ratios of product managers to engineers.
• New roles such as Product engineer, Prompt Engineer, Model Integrators, etc
• More diverse skills on teams, balancing deep tech expertise with communication, design, security, and understanding customers.

The collective takeaway was that the industry is currently in transition as AI technologies are emerging and evolving and is yet to reach a steady state.

AI presents a generational opportunity to redefine software engineering productivity, team dynamics, and product development. However, its successful adoption depends on careful governance, cultural alignment, and sustained investment in human expertise.

Engineering teams will not disappear but will evolve—becoming more impactful, cross-functional, and focused on high-value problem-solving. The organisations that succeed will be those that balance technological acceleration with human-centred development, ethical oversight, and continuous learning.

The roundtable underscored both the optimism and caution required to navigate this AI-driven transformation.

A Straight-Talking Conversation

As with every CTO Breakfast, this session was defined by openness and honesty.

Voices around the table challenged assumptions, shared real-world experiments, and talked about the cultural changes needed to make AI work in practice.

The clear takeaway?

AI tools will make some things easier. But they also make the hard stuff—like aligning teams, maintaining quality, building skills, and staying focused on the customer—even more important.

As one participant put it:

“Technology is the easy bit. It’s people and process that make or break you.”

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Thanks to everyone who joined us.
We’ll keep these discussions going, helping engineering leaders navigate the real changes AI is bringing to our teams and organisations.