“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.”

Interview to prototype in six hours, down from three months

At the third of Damilah’s CTO Breakfast sessions, Aleksandar Karavasilev, CTO at Damilah described a project that demonstrated what using AI end-to-end could achieve in software engineering.

The project: Helping the University of Economics in Skopje improve exam scheduling. This would 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.

The university is adopting this tool for real use, with just a bit more refinement.

“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.”

Shifts in engineering roles and skills with AI

This prompted a discussion around 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 should just be the means to an end.

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.

Teams will become smaller, with tighter domains. The ratio between product managers to engineers will be closer, and there will be more diverse skills on teams, balancing deep tech expertise with communication, design, security, and understanding customers.

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.

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

How to manage expectations of AI

Several attendees discussed managing internal expectations, as well as those of their investors. Practical advice included:

• 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.”

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 to become 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.

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