At the second of our CTO Breakfast Briefings, where technology executives gathered to examine the growing influence of AI on the software development lifecycle, Aleksandar Karavasilev, CTO at Damilah, shared the results of Damilah’s AI experiments. These revealed practical gains and sparked further discussion around how AI is reshaping team workflows, engineering oversight, and productivity.

500 hours’ time saved in three months with AI

Aleksandar opened the session by presenting findings from structured experiments using tools like GitHub Copilot and Cursor. One example showed how engineers used Cursor to analyse over 900 SQL procedures, reducing what would typically take weeks into a few days. Across 80 engineers, the company recorded over 400 – 500 hours saved in just three months.

While the productivity gains were clear, attendees agreed that traditional metrics, such as story points or cycle time, struggle to reflect the real impact of AI assistance. Some have started running side-by-side comparisons using past sprint data. Others rely on direct feedback from engineers to assess where AI adds the most value.

Human accountability and measurement challenges

Participants discussed optimal approaches to handling bugs in AI-generated code. They generally agreed that no AI tool is capable of producing perfect code.

In particular, “code bloat” was highlighted as a major issue. In other words, as AI operates so rapidly, it’s easy to generate huge amounts of code – and inevitably the more code that exists, the more bugs there will be.

Because of this, there was a consensus that:

• It’s essential to ensure human involvement at every step of the development process.
• “People must remain accountable for what they produce,” said one attendee.
• Code reviews, both by humans and tools such as CodeRabbit, are particularly important.

Privacy, security and regulatory requirements shape implementation

Security and compliance featured heavily in the conversation. Organisations are under pressure to ensure that AI tools don’t expose sensitive information or create audit gaps. A few attendees discussed:

• Creating data wrappers to monitor agent activity
• Preventing tools from leaking PII
• Building traceable workflows that can meet third-party audit standards

These measures allow AI to be used in sensitive industries like finance and healthcare.

Managing AI Costs and Infrastructure choices

As AI becomes more integrated into workflows, its operational cost has come under scrutiny. Attendees noted the rising expenses of using large language models through public APIs and the unpredictability of long-term pricing.

Alternatives currently under consideration include:

• Hosting lightweight private LLMs
• Running AI models locally on edge devices
• Replacing SaaS-based solutions with internal AI-driven systems built using MCP

These strategies aim to make AI adoption more sustainable while maintaining data privacy and performance.

AI is redefining engineering workflows

Attendees agreed that AI is influencing how engineers work. By offloading repetitive or time-intensive tasks, AI enables developers to focus on problem-solving and innovation.

However, participants raised concerns about the learning curve for junior engineers. If AI handles too much, new developers risk missing foundational knowledge. Several organisations now combine AI tooling with mentorship programmes and hands-on training. One attendee noted that AI should be treated as a peer contributor: fast, efficient, but imperfect, always requiring human oversight.

Rethinking SaaS business models in the age of AI

Attendees discussed how AI, particularly LLMs and generative tools, is forcing product and commercial leaders to reconsider SaaS business models. One speaker described the tension between using AI to accelerate a content-driven business while also facing existential risk from the same technology. As AI agents shift how people search, consume, and generate content, traditional SEO-led monetisation strategies may become less effective. Another participant noted that the ease of creating AI-powered workflows has made it harder to differentiate products, urging businesses to focus on their unique value propositions. Several attendees agreed that pricing models would need to evolve, moving away from flat-fee SaaS toward usage-based or outcome-driven approaches.

Final Thoughts: Success requires structure, not just tools

AI tools have moved from experimentation to execution. They’re helping engineering leaders solve long-standing problems, accelerate timelines, and explore new ways of working. But the session also made it clear that success depends on structure: on clear governance, trusted oversight, and shared learning.