EVENT OVERVIEW

We’re pleased to invite you to the third edition of our CTO Breakfast Roundtable series—a peer-led conversation designed for senior engineering leaders.

In this session, we’ll dive into a topic that consistently surfaces but is rarely explored in depth: how AI is reshaping the structure of software teams. What will these teams look like in the future? Which roles will evolve, what responsibilities will shift, and how will SDLC stages adapt as AI tools and agents become embedded in the process?

This isn’t about theory or future speculation. Instead, we’ll focus on the practical impact of tools already in use—from copilots to workflow agents—and the broader implications for productivity, oversight, and collaboration.

Whether you’re experimenting with AI today or simply planning for what’s next, this roundtable offers a unique space to compare experiences, challenge assumptions, and leave with actionable insights.

KEY DETAILS

Date: Tuesday 1th July 2025
Time: 8:30–10:30 am
Location: etc. venues, 8 Fenchurch Place, London

KEY DISCUSSION POINTS

  1. Boosting Productivity with AI: Damilah’s Benchmark Insights

  • Exploring how AI tools like GitHub Copilot, Tabnine, and others can accelerate coding, debugging, and testing.
  • A look at Damilah’s internal benchmark experiment—and what the results revealed.

  1. Success Stories & Lessons from the Field

  • How engineering teams are using AI in the real world: what’s working, what’s not, and how to scale responsibly.

  1. Overcoming the Challenges of AI Deployment

  • Unpacking key adoption barriers: developer trust, governance, integration, and ethical or IP concerns.

  1. Rethinking Roles, Responsibilities & the SDLC

  • Where does human contribution matter most?
  • How do agents shift team dynamics, and what checks and balances are needed?

  1. Shaping the Teams of Tomorrow

  • What does the ideal AI-augmented team look like?
  • How do structures evolve—and how do we maintain creativity, ownership, and team culture?

  1. Recommendations for Adoption

  • Practical steps to adopt AI tools effectively, while balancing automation with engineering excellence.

WHY ATTEND

  • Get Ahead: Understand how AI tooling is actively reshaping engineering team design and delivery processes.
  • Build Connections: Share experiences with CTOs and tech leaders from high-growth product companies.
  • Learn & Share: Leave with new perspectives and practical ideas you can bring back to your team.

Explore more: Find related articles on our Blog

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    Exploring AI-Driven Productivity in Software Development and Agentic AI KEY TAKEAWAYS AI, Productivity & Agentic Systems: Where Engineering Leaders Are Headed Next As AI continues to transform software development, engineering leaders are rethinking how teams work, how tools are used, and how success is measured. At our latest CTO Breakfast Briefing, technology executives gathered to…

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    Exploring AI-Driven Productivity in Software Development KEY TAKEAWAYS Boosting developer productivity: How CTOs are leveraging AI for extraordinary gains AI tools are capable of dramatically accelerating the pace of software development—sometimes by up to 20x. Our recent CTO Roundtable discussion explored the possibilities and how to reap the benefits. Software developers can make huge productivity…

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    AI, Productivity & Agentic Systems: Where Engineering Leaders Are Headed Next


    As AI continues to transform software development, engineering leaders are rethinking how teams work, how tools are used, and how success is measured. At our latest CTO Breakfast Briefing, technology executives gathered to explore these shifts, share emerging practices, and examine the growing influence of agentic systems on the software development lifecycle.

    One of the highlights of the session came when Aleksandar Karavasilev, CTO at Damilah, shared the results of internal AI experiments. These revealed practical gains and sparked further discussion around how AI is reshaping team workflows, engineering oversight, and productivity.

    AI tools accelerate development, but measuring impact remains complex

    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.

    Agentic AI: Practical applications already in use

    The discussion moved beyond basic AI tools to focus on agentic AI—systems that operate autonomously and carry out multi-step tasks with limited human input. Attendees shared concrete examples already in use:

    1. Cursor AI for Refactoring SQL

    Used to analyse and refactor legacy SQL codebases. In one case, over 900 stored procedures were reviewed using Cursor, accelerating output by up to 40x. The tool helped not only with syntax cleanup but also with identifying duplication patterns, unused procedures, and potential areas for optimisation.

    2. Product Development Agents

    AI agents that help automate early-stage product work, such as persona creation, solution mapping, and documentation. One attendee described using agents to rapidly develop user personas by aggregating public data, aligning it with internal user journey maps, and drafting functional product outlines that served as a starting point for cross-functional discussions.

    3. Multi-Agent Systems (MAS)

    Autonomous agents working in coordination—one generating code, another testing it, a third documenting it—mimicking the functions of a development team. In one example, a multi-agent system designed a basic feature end-to-end, then returned human-readable notes and integration steps, helping the engineering lead decide whether to implement or modify.

    4. Legacy System Modernisation

    Agentic tools helped parse and understand legacy systems (like COBOL), reducing dependency on domain-specific knowledge held by a few individuals. A participant shared how their team used AI to map out the dependencies across an aging monolithic application, flagging risky elements and suggesting components that could be containerised or rewritten.

    5. Security-Wrapped Agent Frameworks

    To meet regulatory requirements, some attendees described wrapping agents in code that tracks data flow for full traceability. These wrappers were used to ensure that LLM-based agents operating in sectors like healthcare and finance logged all activity, allowing compliance officers to audit and validate their outputs against policy.

    6. MCP Servers for Workflow Orchestration

    Using Model Context Protocol, attendees built workflows where agents autonomously accessed APIs, gathered data, and triggered actions without manual steps. One use case involved pricing intelligence: the AI queried multiple APIs for pricing data, summarised the output, sent updates via internal messaging tools, and triggered reports—automatically and on schedule.

    Reviewing AI-generated code requires human oversight

    Participants expressed caution about relying too heavily on AI-generated code. Some described scenarios where AI wrote code, created the tests, and approved the changes—resulting in logic flaws that went unnoticed.

    In response, organisations have implemented stricter review processes:

    • Senior engineers are required to approve all AI-generated pull requests
    • AI-generated reviews are disallowed
    • AI support is used during reviews, but not in place of human reviewers

    Human accountability and measurement challenges

    Participants also discussed optimal approaches to handling bugs in AI-generated code – as, they generally agreed, 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.

    Clear rules and guardrails enable safe adoption

    Governance came up as a priority. Attendees explained how their organisations define usage boundaries and assign accountability:

    • Technical leads or architects determine how and where AI tools can be applied
    • Guardrails are introduced to prevent misuse in code, testing, or deployment
    • Some use lightweight wrappers to log all AI input and output, enabling auditability

    This helps organisations embrace AI without losing control over quality, process, or compliance.

    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 changing more than just speed—it’s 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—are 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—especially as LLM compute costs rise and multi-agent architectures become more common.

    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.

    Our next CTO Breakfast will focus on how organisations scale their AI practices across teams, while maintaining quality, security, and cultural cohesion.

    We hope you’ll join us.

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      Exploring AI-Driven Productivity in Software Development KEY TAKEAWAYS Boosting developer productivity: How CTOs are leveraging AI for extraordinary gains AI tools are capable of dramatically accelerating the pace of software development—sometimes by up to 20x. Our recent CTO Roundtable discussion explored the possibilities and how to reap the benefits. Software developers can make huge productivity…

    • Accelerating software development with AI: a controlled experiment

      Accelerating software development with AI: a controlled experiment

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    Subscribe to our newsletter

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      Delivering Great Software on Time

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        And this sums up, in a nutshell, our philosophy when it comes to gender balance and inclusion.

        Diversity isn’t our goal; it’s how we work as a business. This is not only because we believe it’s the right thing to do, but also because we know it achieves better results for us and for our clients. We therefore see it as an intrinsic part of our culture, and one of our key strengths.

        Consequently, we’re extremely proud of the fact that nearly 50% of our workforce are women, with many in senior leadership roles.

        Elena has seen this first-hand at Damilah.

        Elena Madjirova Miladinoska - Inclusive culture in tech

        “When I first joined the company nearly three years ago, I was the only woman on my team. Now we are seven men and five women, and I was promoted after less than a year, which shows that the company doesn’t just talk about inclusion – it really means it.”

        Elena Madjirova Miladinoska, Principal Software Engineer and Technical Lead

        Her colleague, Fikrije Stafai, a Senior Delivery Manager who has been with Damilah for a similar amount of time and now line manages the other DMs, strongly agrees. She also believes that balanced teams tend to result in a higher level of efficiency and productivity. “Everything is more organised when women are included!” she says.

        Galina Dushanski, a Software Engineer who joined Damilah as a graduate in 2022, adds: “Gender diversity creates a more open and respectful environment, and a team dynamic where we all listen to each other, even if we don’t agree at first. That leads to better outcomes in the long run.”

        Our inclusive culture

        Such deep respect for colleagues, regardless of gender or background, stems largely from our company culture, where inclusivity is deeply embedded.

        “It’s not someone’s gender that matters,” says Sandra Velichkovska, a Senior Talent Acquisition Specialist, who joined Damilah more than three years ago and has been promoted twice in that time. “It’s about the culture we bring with us.”

        Sandra Velichovska - Inclusive culture in tech

        This leads to a feeling of empowerment for every employee, says Galina. “We are creating a place where everyone feels comfortable to speak and grow. In my team, every voice is genuinely heard. From day one, I was treated with respect, and my opinions were valued, just like anyone else’s.”

        The women at Damilah also appreciate the many benefits that the business offers its employees, allowing them to progress their careers and fulfil their potential. There are training days and workshops – which are, of course, helpful for all colleagues – but also family-friendly policies, such as generous parental leave, the freedom to work from home at any time, the option to take unexpected days off (for example, to look after a sick child), and even birthday presents for employees’ children. “My kid loves those!” says Elena.

        Valuable business impacts

        Damilah’s women strongly believe that our inclusive culture makes a genuinely positive impact on the work we do as a business.

        Elena points to the greater degree of collaboration that tends to happen when women are present. “We’re currently working on a big project with multiple teams,” she says. “I’ve been able to make a big impact by facilitating cross-team communication and collaboration, ensuring we have alignment, arranging the meetings, following up on people’s actions. I’m not sure this would happen without a woman’s input!”

        Meanwhile, Fikrije constantly sees evidence that the gender balance creates high degrees of accountability and a strong focus on detail – as well as the ability of certain female colleagues to give team members a lift.

        Fikrije Stafai - Inclusive culture in tech

        “We bring more positive emotion, and that brings more harmony,” she says. “For example, one member of my team takes care of people’s birthdays – she never forgets anyone and always ensures we buy them a gift. It may not seem a big deal, but it really helps the team to function as one.”

        Fikrije Stafai, Senior Delivery Manager

        Changing the stereotypes

        Everyone at Damilah agrees that IT, which has traditionally been a male-dominated world, is all the better for the growing number of women who are entering the profession despite the barriers that still exist.

        Galina has some advice for women considering working in this field. “Be yourself, be proud, and don’t be afraid to speak up and share your ideas,” she says.

        “For a long time, tech was seen as a man’s job. We all know the stereotype of the guy in the hoodie, coding. But that’s changed and today we see amazing women in our profession. We are confident and stylish – and we also write great code, and lead teams, and build outstanding products. “We are changing what it means to be a developer.”

        Galina Dushanski, Software Engineer

        Galina Dushanski - Inclusive culture in tech

        Elena concurs. “If your company is not valuing your voice, leave the company. Do not stop speaking,” she says.

        And for businesses that are struggling to build and maintain a good gender balance, Sandra has some important advice. “They need to look beyond the numbers, because gender balance starts with culture,” she says.

        “Businesses that find this a problem need to ask themselves: Are they creating an environment where everyone, regardless of gender, feels safe to speak, grow and lead? Because when you focus on that foundation, then gender balance becomes a natural outcome.”

        Pride and positive outcomes

        Unsurprisingly, the women at Damilah have a shared sense of pride about our company and how they are empowered to fulfil their potential – while also delivering great outcomes for clients.

        As Galina puts it: “People here genuinely care – not just about the work, but about each other. We help one another, we share knowledge, we’re always open to new ideas. We create a space where everyone can thrive. Being part of a company where inclusion is not just a word, but a reality, makes me genuinely proud every day.”

        Case Study: Damilah | Valve Space

        Here’s how our partner-shoring model has enabled us to create outstanding business value for a growing UK start-up.

        Valve Space is a rapidly growing UK-based start-up. It provides sales and marketing software to the flexible workspace market that enables their customers to unlock lead flow, build brand awareness, connect to clients, service occupiers and grow revenues.

        As an ambitious business in a fast-moving market, Valve Space needed to expand engineering capacity quickly and flexibly to meet evolving product and customer demands. To support this, Valve Space opted to partner with a trusted nearshore team to accelerate delivery, complement in-house expertise and maintain momentum.

        We were proud to be selected as that partner. Valve Space recognised our ability to attract and retain high-quality talent in North Macedonia, while valuing our strong cultural alignment and ability to work seamlessly as part of their team.

        Our ‘partner-shoring’ approach to working with clients – a near-shoring model where the client and supplier work seamlessly and flexibly together towards common goals – aligned closely with Valve Space’s need for a scalable and collaborative delivery model and was a key factor in the company’s decision.ess working relationship across borders.


        Delivering technical depth and collaborative support

        Since January 2024, we’ve been working closely with Valve Space to provide additional engineering capacity as part of their software engineering function.

        We have supplied a team of skilled professionals, including:

        • Software developers
        • Quality assurance (QA) testers
        • Automation QA specialists

        These experts supplement Valve Space’s in-house team by carrying out product development and testing, supporting on complex projects for Valve Space’s product-led growth initiatives.

        This has included:

        • Developing and implementing a new performance testing suite to ensure system reliability and scalability
        • Enhancing customer-focused features that add value and contribute to platform revenue

        Beyond pure technical expertise, we also supply additional support to enhance Valve Space’s team structure and workflow. This includes, as required:

        • Fractional scrum master support on a few key projects
        • Flexible team organisation, allowing Valve Space to scale capacity based on workload and project complexity
        • Collaborative working environments, fostering a close partnership between Valve Space’s in-house team and our own people

        One of the key highlights of the partnership so far has been a company-wide event we hosted at our headquarters in Skopje at the start of 2025. Here, the entire product and engineering teams from both companies assembled to work together and, after hours, enjoy a night of music and fun.

        We are always keen proponents of this kind of gathering, to ensure distributed groups of colleagues meet face to face and can bond in person – and there is no doubt it boosted team morale, while fostering ever closer alignment and integration.


        Generating added value through close collaboration

        Thanks to our collaborative approach and technical expertise, the partnership between our two companies has yielded significant value for Valve Space, particularly when it comes to rapid product development, flexibility and efficiency – allowing Valve Space to sharpen its focus on delivering high-quality products to meet its customers’ needs to drive growth.

        Above all, for a start-up company like Valve Space, the ability to rapidly align talent to priority initiatives is key. To date, we’ve contributed by:

        • Rapidly onboarding skilled professionals to meet immediate product needs, while taking care to find individuals who align well with Valve Space’s team and culture
        • Managing resources, allowing Valve Space to focus on growth rather than hiring and HR administration
        • Embedding people who are able to quickly get up to speed on Valve Space’s market and customer needs through close collaboration with product managers and cross-functional teams

        At the same time, Valve Space benefits from strong organisational support, such as:

        • Senior leadership support that helps to ensure complex projects receive the expertise they require; whenever a significant challenge has occurred, we have been ready and willing to lean into it, with a hands-on approach from our most experienced people – including CEO Iain Bishop and CTO Aleksandar Karavasilev – as required
        • Proactive problem-solving, where we always listen carefully, in order to fully understand and address challenges swiftly and effectively
        • Delivery manager support to help accelerate key workstreams and keep momentum strong

        Additionally, as Lucinda Faucher, Valve Space’s VP of Product Management, explains:

        “A successful partnership of this kind is always built on trust, and Damilah have proven to be reliable and collaborative partners for Valve Space.”

        Lucinda is pleased that the current relationship between the two companies has been highly effective so far. As Valve Space continues to grow and maximise profits, we’re proud to be seen as a long-term partner in helping them deliver on their ambitions.


        A reliable and aligned partnership

        The partnership between Valve Space and Damilah exemplifies the benefits of working with a skilled, flexible, and trustworthy ‘partner-shoring’ provider. Our depth of expertise, proactive approach, and commitment to building a collaborative team culture – one that feels like a seamless extension of Valve Space’s own team – has made this a highly effective and rewarding collaboration.

        As Lucinda puts it:

        “Damilah fosters a positive culture that really cares about its people. Most importantly, they enjoy what they do and bring great energy to the team. They’re collaborative, knowledgeable, and have taken the time to understand our vision, which helps ensure they are aligned with where we are headed and how best to support us along the way.”


        Inspired by Our Approach?
        Let’s Collaborate!

        Whether you operate with a similar mindset or share our passion for streamlined processes, we’re excited to explore how we can work together.

        Podcast powered by RingStone

        John White

        Partner at RingStone
        Host

        Hazem Abolrous

        CEO at RingStone
        Guest

        Iain Bishop

        CEO and Founder at Damilah
        Guest

        In this episode of the RingStone Podcast, Hazem Abolrous (CEO of Ringstone) and Iain Bishop (CEO of Damilah) discuss the realities of distributed software development—from building trust across borders to the game-changing role of AI. What emerges is a refreshingly honest conversation on what’s working, what’s not, and why a new approach to nearshoring—partner-shoring—is proving to be the way forward.

        Key Insights from the Episode

        1. Partner-shoring: A better way to nearshore

        Partner-shoring is reshaping how companies think about nearshore development. Rather than operating as a supplier at arm’s length, the external team becomes a fully integrated extension of the client’s business—sharing the same vision, ownership, and commitment to outcomes.

        “My team feels like they own the product. They’re aligned behind the vision. That’s what makes it work.” – Iain Bishop

        It’s not just a resourcing solution—it’s a mindset shift, building mutual accountability and a seamless working relationship across borders.


        2. Getting distributed teams right starts with people

        Access to global talent and flexibility are undeniable advantages—but distributed development only works when teams are structured intentionally. Cross-functional, autonomous teams that are set up to own outcomes perform best.

        Blending new distributed hires with existing team members helps transfer knowledge and build rapport. And in-person time—even casually over food or drinks—remains a powerful enabler of team cohesion.

        “That’s when they stop being just colleagues and start being a team.” – Iain Bishop


        3. It’s not just tools—it’s the process around them

        Success in remote environments isn’t about choosing the perfect tool—it’s about creating clarity, structure, and seamless workflows across time zones. Poor processes that might be manageable onshore quickly become blockers in a distributed setup.

        “You can’t just copy-paste the same setup you had onshore. You need to rethink it entirely.” – Hazem Abolrous

        Rationalising tooling, aligning data flows, and automating routine steps are all part of creating a distributed model that actually works.


        4. Leadership & loyalty are still human

        No matter where a team sits, leadership remains the key to performance and retention. Respect, psychological safety, shared purpose, and team-based recognition go further than any perks or systems.

        “Treat people like adults. Respect their input. Create a culture where people want to stay.” – Iain Bishop

        Leaders who lead by example, stay close to their teams, and invest in growth and recognition help foster long-term loyalty.


        5. Scaling smartly means managing risk

        Scaling distributed teams isn’t just about hiring—it’s about risk management. That means starting with blended teams, understanding cultural dynamics, and ensuring proper onboarding and ownership from day one.

        “Don’t make big bets blind. Blend teams. Learn the culture. Then scale.” – Iain Bishop

        Scaling should be measured and deliberate, not rushed or spreadsheet-driven. When done right, it unlocks speed and resilience.


        6. AI is here—and it’s changing the game

        AI is already helping development teams get things done faster and better. From writing unit tests to untangling legacy code, the tools are taking care of the repetitive stuff—so engineers can focus on real problem-solving. In internal tests, developers using AI completed tasks up to five times faster, often with higher quality.

        “Some people with experience of the AI tools were producing the same applications five times faster and to better quality.” – Iain Bishop

        It’s not about replacing developers—it’s about accelerating them. As Hazem put it, the time saved should be reinvested in collaboration because building great software still comes down to people working well together.


        7. AI + autonomy = A powerful mix

        Autonomous teams supported by the right AI tools are more agile, more efficient, and better positioned to innovate. AI becomes an enabler, not a replacement, helping teams prototype, analyse, and solve problems faster—without removing the human oversight that ensures quality.

        “Good teams are agile. Add AI to the mix, and you have a very powerful recipe. But it takes planning.” – Hazem Abolrous

        When AI is paired with team ownership and clarity of purpose, the results compound.

        Ready to Listen?

        This episode is a must-hear for CTOs, product leaders, and decision-makers navigating the realities of distributed development in an AI-driven world. It’s packed with practical insight—and a refreshing focus on the human side of technology.


        Check out the episode on:

        Find out more, talk to us!

        Are you ready to learn more about how we can deliver outstanding value for your business? Get in touch with us today to discuss your requirements and discover the difference we make.

        Boosting developer productivity: How CTOs are leveraging AI for extraordinary gains


        AI tools are capable of dramatically accelerating the pace of software development—sometimes by up to 20x. Our recent CTO Roundtable discussion explored the possibilities and how to reap the benefits.

        Software developers can make huge productivity gains through the adoption of AI tools – sometimes with speed increases of up to 20x. That was the outstanding conclusion of our recent CTO Roundtable, Exploring AI-Driven Productivity in Software Development, hosted by Iain Bishop and Aleksandar Karavasilev, CEO and CTO of Damilah Technology.

        Kicking off the event, they presented the findings from a series of controlled experiments they had conducted at Damilah, which demonstrated that time savings of up to 2x are quickly and easily achievable by using AI tools, in particular for tasks such as:

        • Quality analysis
        • Coding
        • Unit testing

        Furthermore, when engineers already experienced in using AI performed the set tasks, they were able to operate 4x to 6x faster.

        While Iain and Aleksandar highlighted these impressive gains, the discussion revealed even greater possibilities. A start-up founder explained how combining a series of AI tools for different stages of development enabled him to deliver productivity improvements of up to 20x. To do this, he used:

        • Perplexity for market research
        • Replit and Lovable for building apps
        • Tabnine for auto-completion of code

        This illustrated how companies willing to fully embrace AI across the development lifecycle can potentially achieve extraordinary results – for example, by enabling them to rapidly put new product ideas in front of users as basic MVPs. As a result, this founder now plans to launch at least three start-ups within a year.

        Increasing dynamism

        This kind of accelerated development cycle can usher in a whole new era of dynamism in the market, it was concluded—the main points being:

        • Start-ups are usually lacking capital.
        • AI enables them to develop an application at pace and at low cost, before significant sums of money need to be invested.
        • This helps to de-risk new ventures and make it easier to attract funding.

        But even companies who take a steadier or more cautious approach to AI-adoption, it was agreed, can still make immediate productivity gains. This could be, for example, by starting with ChatGPT, then progressing to more sophisticated tools such as GitHub Copilot or Cursor.

        Overcoming barriers and managing adoption

        The conversation also turned to barriers to AI adoption. Several of those present described a certain amount of reluctance or resistance among larger enterprises, often due to concerns relating to company policies and regulatory matters.

        However, it was noted that simply banning the use of AI tools can lead to “shadow AI”, where developers, in particular, will find workarounds in a constant pursuit of innovative ways of working.

        Several attendees agreed that the solution to this is not to block, but rather to encourage the use of specified AI tools with some strict guardrails in place to ensure certain boundaries are not crossed, particularly with regard to security, privacy and IP protection

        Addressing recruitment challenges

        Questions were also raised about whether developers should be allowed to use AI during the hiring process, as often this could mask an applicant’s true capabilities when it comes to coding and testing. However, as one participant pointed out, companies should be looking to hire employees who are adept at using AI, as having an AI-proficient workforce will be necessary to maintain competitive advantage in the future.

        Another attendee recommended a solution to the hiring problem: they deliberately asked applicants to use an AI tool which they knew would produce a certain bug in its code. They were then able to check whether the applicants had detected the bug, and therefore whether they were capable of testing and fixing code without the use of AI.

        Furthermore, as one participant noted from their own experiences, many developers will decline to work for a company that denies them the opportunity to use AI tools to enhance and accelerate their work.

        Encouraging adoption and keeping pace

        One recommendation to encourage those more hesitant about AI, alongside formal training, was to hold informal “brown bag” sessions. The participants from Damilah talked about their own experiences of this – holding the sessions once a fortnight, and inviting anyone from their own company or their client base to share their experiences and knowledge of AI tools.

        The importance of keeping on top of rapid changes in the quality of AI tools also emerged. Several participants pointed out that some, which didn’t perform well a few months ago, are now proving to be highly valuable – software-testing tool CodeRabbit being a good example of this.

        Human accountability and measurement challenges

        Participants also discussed optimal approaches to handling bugs in AI-generated code – as, they generally agreed, 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.

        Measuring success

        The roundtable also discussed the challenges related to measuring the gains achieved by AI. The key points raised were:

        • Software development, by its very nature, involves performing a different task for each new project.
        • Therefore, the only way to effectively measure productivity gains is to have two teams performing the same tasks in parallel – one with the use of AI tools, and the other without, to act as a control group.
        • AI tools, used in the right ways, can clearly unlock major productivity gains – but many businesses do not have the time or resources to conduct such controlled experiments to measure the gains with any accuracy.
        • In light of this, the tests conducted by Damilah have proved invaluable.

        Sharing knowledge and expertise

        Our roundtable participants agreed that keeping up with the lightning pace of change in AI is almost impossible. It therefore requires individuals and companies to work together to share knowledge and experiences for the advantage of all.

        For that reason, we’d like to continue our series of breakfast roundtables. Exploring AI-Driven Productivity in Software Development and Agentic AI, to be held on the 13th of May, will continue to focus on the productivity benefits of AI while also addressing the emergence of agents.

        We invite you to join us. Previous attendees found the session a valuable use of their time, and we’re confident you’ll feel the same about the next one.

        Explore more: Find related articles on our Blog

        • Damilah | CTO Roundtable Series | Second Breakfast

          Damilah | CTO Roundtable Series | Second Breakfast

          Exploring AI-Driven Productivity in Software Development and Agentic AI KEY TAKEAWAYS AI, Productivity & Agentic Systems: Where Engineering Leaders Are Headed Next As AI continues to transform software development, engineering leaders are rethinking how teams work, how tools are used, and how success is measured. At our latest CTO Breakfast Briefing, technology executives gathered to…

        • Damilah | CTO Roundtable Series | First Breakfast

          Damilah | CTO Roundtable Series | First Breakfast

          Exploring AI-Driven Productivity in Software Development KEY TAKEAWAYS Boosting developer productivity: How CTOs are leveraging AI for extraordinary gains AI tools are capable of dramatically accelerating the pace of software development—sometimes by up to 20x. Our recent CTO Roundtable discussion explored the possibilities and how to reap the benefits. Software developers can make huge productivity…

        • Accelerating software development with AI: a controlled experiment

          Accelerating software development with AI: a controlled experiment

          We all know that AI is transforming the way software is developed. But how many of us are clear on exactly what kinds of business benefits it can deliver? We wanted to develop a better understanding of this, to ensure we maximise the productivity and quality gains AI is able to deliver, and also to…

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        Sign up for our newsletter to get regular updates and insights into our solutions and technologies


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          Delivering Great Software on Time

            By using this form, you consent to the processing of your data in accordance with our Privacy Policy. This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

            Boosting developer productivity: How CTOs are leveraging AI for extraordinary gains


            AI tools are capable of dramatically accelerating the pace of software development—sometimes by up to 20x. Our recent CTO Roundtable discussion explored the possibilities and how to reap the benefits.

            Software developers can make huge productivity gains through the adoption of AI tools – sometimes with speed increases of up to 20x. That was the outstanding conclusion of our recent CTO Roundtable, Exploring AI-Driven Productivity in Software Development, hosted by Iain Bishop and Aleksandar Karavasilev, CEO and CTO of Damilah Technology.

            Kicking off the event, they presented the findings from a series of controlled experiments they had conducted at Damilah, which demonstrated that time savings of up to 2x are quickly and easily achievable by using AI tools, in particular for tasks such as:

            • Quality analysis
            • Coding
            • Unit testing

            Furthermore, when engineers already experienced in using AI performed the set tasks, they were able to operate 4x to 6x faster.

            While Iain and Aleksandar highlighted these impressive gains, the discussion revealed even greater possibilities. A start-up founder explained how combining a series of AI tools for different stages of development enabled him to deliver productivity improvements of up to 20x. To do this, he used:

            • Perplexity for market research
            • Replit and Lovable for building apps
            • Tabnine for auto-completion of code

            This illustrated how companies willing to fully embrace AI across the development lifecycle can potentially achieve extraordinary results – for example, by enabling them to rapidly put new product ideas in front of users as basic MVPs. As a result, this founder now plans to launch at least three start-ups within a year.

            Increasing dynamism

            This kind of accelerated development cycle can usher in a whole new era of dynamism in the market, it was concluded—the main points being:

            • Start-ups are usually lacking capital.
            • AI enables them to develop an application at pace and at low cost, before significant sums of money need to be invested.
            • This helps to de-risk new ventures and make it easier to attract funding.

            But even companies who take a steadier or more cautious approach to AI-adoption, it was agreed, can still make immediate productivity gains. This could be, for example, by starting with ChatGPT, then progressing to more sophisticated tools such as GitHub Copilot or Cursor.

            Overcoming barriers and managing adoption

            The conversation also turned to barriers to AI adoption. Several of those present described a certain amount of reluctance or resistance among larger enterprises, often due to concerns relating to company policies and regulatory matters.

            However, it was noted that simply banning the use of AI tools can lead to “shadow AI”, where developers, in particular, will find workarounds in a constant pursuit of innovative ways of working.

            Several attendees agreed that the solution to this is not to block, but rather to encourage the use of specified AI tools with some strict guardrails in place to ensure certain boundaries are not crossed, particularly with regard to security, privacy and IP protection

            Addressing recruitment challenges

            Questions were also raised about whether developers should be allowed to use AI during the hiring process, as often this could mask an applicant’s true capabilities when it comes to coding and testing. However, as one participant pointed out, companies should be looking to hire employees who are adept at using AI, as having an AI-proficient workforce will be necessary to maintain competitive advantage in the future.

            Another attendee recommended a solution to the hiring problem: they deliberately asked applicants to use an AI tool which they knew would produce a certain bug in its code. They were then able to check whether the applicants had detected the bug, and therefore whether they were capable of testing and fixing code without the use of AI.

            Furthermore, as one participant noted from their own experiences, many developers will decline to work for a company that denies them the opportunity to use AI tools to enhance and accelerate their work.

            Encouraging adoption and keeping pace

            One recommendation to encourage those more hesitant about AI, alongside formal training, was to hold informal “brown bag” sessions. The participants from Damilah talked about their own experiences of this – holding the sessions once a fortnight, and inviting anyone from their own company or their client base to share their experiences and knowledge of AI tools.

            The importance of keeping on top of rapid changes in the quality of AI tools also emerged. Several participants pointed out that some, which didn’t perform well a few months ago, are now proving to be highly valuable – software-testing tool CodeRabbit being a good example of this.

            Human accountability and measurement challenges

            Participants also discussed optimal approaches to handling bugs in AI-generated code – as, they generally agreed, 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.

            Measuring success

            The roundtable also discussed the challenges related to measuring the gains achieved by AI. The key points raised were:

            • Software development, by its very nature, involves performing a different task for each new project.
            • Therefore, the only way to effectively measure productivity gains is to have two teams performing the same tasks in parallel – one with the use of AI tools, and the other without, to act as a control group.
            • AI tools, used in the right ways, can clearly unlock major productivity gains – but many businesses do not have the time or resources to conduct such controlled experiments to measure the gains with any accuracy.
            • In light of this, the tests conducted by Damilah have proved invaluable.

            Sharing knowledge and expertise

            Our roundtable participants agreed that keeping up with the lightning pace of change in AI is almost impossible. It therefore requires individuals and companies to work together to share knowledge and experiences for the advantage of all.

            Explore more: Find related articles on our Blog

            • Damilah | CTO Roundtable Series | Second Breakfast

              Damilah | CTO Roundtable Series | Second Breakfast

              Exploring AI-Driven Productivity in Software Development and Agentic AI KEY TAKEAWAYS AI, Productivity & Agentic Systems: Where Engineering Leaders Are Headed Next As AI continues to transform software development, engineering leaders are rethinking how teams work, how tools are used, and how success is measured. At our latest CTO Breakfast Briefing, technology executives gathered to…

            • Damilah | CTO Roundtable Series | First Breakfast

              Damilah | CTO Roundtable Series | First Breakfast

              Exploring AI-Driven Productivity in Software Development KEY TAKEAWAYS Boosting developer productivity: How CTOs are leveraging AI for extraordinary gains AI tools are capable of dramatically accelerating the pace of software development—sometimes by up to 20x. Our recent CTO Roundtable discussion explored the possibilities and how to reap the benefits. Software developers can make huge productivity…

            • Accelerating software development with AI: a controlled experiment

              Accelerating software development with AI: a controlled experiment

              We all know that AI is transforming the way software is developed. But how many of us are clear on exactly what kinds of business benefits it can deliver? We wanted to develop a better understanding of this, to ensure we maximise the productivity and quality gains AI is able to deliver, and also to…

            Subscribe to our newsletter

            Sign up for our newsletter to get regular updates and insights into our solutions and technologies


              By using this form, you consent to the processing of your data in accordance with our Privacy Policy.

              Delivering Great Software on Time

                By using this form, you consent to the processing of your data in accordance with our Privacy Policy. This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

                We therefore ran a controlled experiment, pitting a team of AI-assisted software engineers and quality analysts against a ‘human-powered’ group who were only allowed to use their own brains.

                Here’s what we did and what we discovered…

                How we ran the experiment

                In all, a total of 52 of our people took part in our experiment, over a series of two hackathons.

                We split them into two groups:

                • One assisted by AI tools, with 30 engineers and 8 automation quality analysts (QAs)
                • A control group that was purely human-powered, with 12 engineers and 2 automation QAs – allowing us to baseline the potential gains of using AI

                We gave each group three hours to perform the same task.

                • Develop a .NET Web API to dynamically process mathematical expressions
                • Implement a custom PEMDAS-based algorithm for expression evaluation, without using any third-party libraries
                • Write unit tests to validate the functionality of the solution
                • Test the implementation using five provided edge cases
                • Write three automated functional test scripts for specified scenarios
                • Test these functions using the provided test web shop application
                • GitHub Copilot
                • Cursor IDE (using ChatGPT)
                • Qodo (was Codium)
                • Tabnine

                The results

                We expected the results of this experiment to be positive in favour of the AI team, but we were still amazed by the difference the AI tools made in terms of enhancing speed and quality.

                Here are the overall outcomes we recorded… 

                On average, when compared to our human-powered team, our AI-assisted engineers were able to:

                • Complete the coding nearly 2x (44%) faster

                • Conduct the unit tests just over 2x (51%) faster

                • Cover nearly twice as many (83%) more edge cases

                And when we compared our fastest human-powered engineer with our fastest AI-assisted engineer, the results were even more impressive: the AI-assisted engineer was nearly 5x (78%) faster.

                We also compared a human-powered engineer with one who already had experience using AI tools (in this case, GitHub Copilot). We found that:

                • For the coding, the AI-powered engineer was 4x (75%) faster

                • For the unit tests, the AI-powered engineer was 6x (83%) faster

                This demonstrated to us that, as our team of engineers become more experienced with AI tools, our productivity gains will increase even further.

                For the QAs, we also saw a significant improvement in the times it took the AI-assisted analysts: the average overall time was just over 2x (54%) faster with AI.

                And, as with the engineers, we compared the two fastest times, and found that the first AI-assisted QA to complete the task was 9x (89%) faster.

                A comparison of AI tools

                We also aimed to make some comparisons between the four different AI tools that we used in the experiment, in particular with regard to user experience, productivity gains, and security and IP protection.

                For user experience: GitHub Copilot came out on top. Our developers rated it as a robust and mature tool, suited for .NET application development. It offered consistent suggestions and responses as well as strong context management. Cursor and Codium came in joint second place.

                For productivity gains: Cursor came out on top, allowing our team to be 3.2x (69%) faster than human-only developers when it came to completing the full task. GitHub Copilot was in second place, making the team 2.7x (62%) faster.

                For security and IP protection , we found the following:

                GitHub Copilot transmits code snippets from the integrated development environment (IDE) to GitHub in real time to generate relevant suggestions. Once the suggestion is created, both the prompt and the suggested code snippet are immediately discarded—but note that this is only the case for the Business and Enterprise licence options.

                Cursor provides a Privacy Mode that can be activated during the onboarding process, ensuring that no code is stored on their servers or by their sub-processors.

                Qodo: Paid licence user data is not used to train its AI models. The data is deleted from their storage after 48 hours. Also,
                they provide an option for a zero-retention policy, where data is removed immediately if users specifically request.

                All three tools are certified for SOC2 compliance.

                (Note that we didn’t assess Tabnine as we felt the model wasn’t mature enough and its users struggled to complete the task.)

                Conclusion and our next steps

                Our experiment made it clear that AI could offer us some huge benefits in productivity and quality. In every aspect of the tests we conducted, from coding to unit tests to automated test script production, there was a clear time saving – in most cases very significant. It will also enable us to improve the quality of our outputs, as AI-generated code was able to give us broad edge case test coverage.

                Furthermore, we expect our efficiency gains to improve further as it is clear that development speed increases with experience when it comes to using AI tools. We found that just a short amount of training significantly accelerates outputs.

                As for the future… Our product owner colleagues also ran an experiment to understand how AI can accelerate and improve the product discovery process. We are now looking at how we can use their AI-generated requirements as prompts to build applications – ultimately with the possibility of using AI-assisted processes from an initial description of requirements right through to final outputs.

                Meanwhile, right now, we’re already starting to reap the benefits of AI-assisted development with some of our clients, delivering even greater value for them.

                If you’d like to find out more about how AI-assisted software development can benefit your business, get in touch now.

                Aleksandar Karavasilev, CTO at Damilah

                But my husband, who at the time had more experience than me in using AI, suggested I was asking the wrong question. He recommended that I reframe it as: “Are there any scientific articles that prove placing a chopped onion in a room will help with a cough?”

                This time, the response was far more credible and useful (and, it turns out, onions do really help).

                I learned an important lesson here: although the potential for AI is enormous, when most people engage with it for the first time, it’s usually in a superficial way, often leading to poor results. In order to gain maximum value from the tools, and improve outputs, it’s worth learning the best ways to provide context and specificity, while also asking for an answer based on relevant sources.

                Putting AI to the test

                At Damilah, we’re very excited about the transformative potential of AI. We have, therefore, been exploring how it can augment our business activities and help deliver greater value to our clients. For example, our engineering teams ran a series of hackathons to calculate whether using AI tools could accelerate software development and improve quality (in a nutshell: yes, it can – hugely).

                At the same time, we wanted to test whether AI could do the equivalent for our product discovery and inception processes – and if so, in what ways. So we ran an additional hackathon to examine this. In it, we asked three teams to work on a fictional brief, using a variety of AI tools, including ChatGPT and Perplexity (using Claude).

                As with our engineering colleagues, the results were astounding.

                We discovered that AI could significantly reduce the amount of time we spent on discovery – by anywhere from 20% to 50%, depending on the task and the tools being used – and with results that matched the quality of the work done without the assistance of AI.

                And, most importantly of all, it revealed areas where AI could free us from routine, repetitive work, allowing our people to focus on higher-value activities.[

                Accelerating and improving workflows

                Specifically, we identified two primary ways that AI can accelerate and improve workflows:

                1. Jump-starting a project: For example, when preparing for a client interview, we can use AI to generate an initial list of questions based on the context we provide. These AI-generated prompts serve as a springboard, helping us refine ideas faster, and ensure we’ve covered everything.
                2. Enhancing existing work: In other cases, we can input a draft of some work we’ve already created, prompting the tool to polish and improve it, as well as asking whether we may have missed something. This approach allows us to benefit from AI’s ability to enhance clarity and suggest useful amendments and additions.

                Another of our most impactful findings from the hackathon was the way in which we could use AI to accelerate the creation of wireframes with Figma. This aided our conversations with developers while showing similar levels of quality outcomes compared to when we use the traditional discovery processes.

                And a real game-changer has been using AI tools for writing acceptance criteria. Traditionally, creating detailed, actionable user stories (which list the requirements that a developer has to meet) is time-consuming and mentally draining. Now, however, by giving a well-crafted prompt to an AI tool, we can generate acceptance criteria in a matter of minutes. This not only saves time but also ensures consistency, freeing our teams to focus on other priorities (more on that later).

                AI as an enabler

                Despite these extremely encouraging results, we aren’t getting carried away with AI. While it accelerates and enhances many of our processes – and we’re already using it to speed up workflows in live environments with clients  who have agreed to us using AI tools – we also believe there should always be a human involved every step of the way.

                For us, quality is paramount, so our product owners will always review and refine the AI outputs to ensure they meet our standards and our clients’ needs.

                We’re also conscious that an over-reliance on AI may lead to diminished problem-solving skills – a phenomenon akin to forgetting basic arithmetic because we’re accustomed to using calculators. To counter this, we view AI as an enabler, not the be-all and end-all, so will always ensure our people develop and maintain those key analytical capabilities. Above all, AI’s purpose is to enhance human creativity and decision-making, not replace them.

                Furthermore, we’re wary of the common problem of ‘garbage in, garbage out’. That is, as I found out with my first experience of AI, it’s essential to take the time to learn how to craft well thought-through prompts and to train the model. AI tools can only become that valuable enabler and accelerator if we ensure we have the skills and patience to do this.

                Focusing on value-creation

                By learning to use AI in the most effective ways to perform routine and time-consuming tasks, we’re enabling many of our people to spend more time focusing on high-value activities, such as deepening their market understanding, engaging more effectively with stakeholders and shaping product roadmaps.

                And, perhaps most excitingly of all, it means we can experiment with bold ideas that we perhaps wouldn’t have risked testing previously as we’d be concerned about the time it would consume. Instead, it enables us to ‘fail fast’ in our search for innovative solutions that genuinely solve our clients’ problems and help them to meet their objectives.

                In fact, if I was a potential client looking for a software development partner, I’d always choose a firm that has already successfully established AI tools into its processes. That’s because their teams will be unburdened by all the mundane, repetitive work, and able to truly focus on building a highly creative partnership that delivers outstanding results.

                To discuss how our AI-accelerated workflows enable us to deliver greater value for your business, get in touch now.

                Iskra Ristovska, Principal Product Owner at Damilah

                And who could blame them? As technologists, we’re all excited about what AI can do for us, and we’re all asking the same question: how can we leverage AI tools to improve productivity and help our teams – and our clients – achieve their objectives?

                Fortunately, those developers didn’t stay annoyed for long, as we let them use AI in a second hackathon.

                Here’s what we did…

                A series of controlled experiments

                We wanted to establish some clarity, for ourselves and our clients, regarding the impacts AI can have on software development. We fully appreciate that some companies are just beginning to explore AI-assisted development, while others are already integrating it into their workflows. But wherever you are on this journey, the potential of these tools to deliver real, measurable value is undeniable – and we were keen to understand that in greater detail.

                So, we designed a series of controlled experiments. First, we split our engineers into two groups: one using traditional methods (the disgruntled ones); the other using AI tools like GitHub Copilot (the far happier ones). We then gave each team a series of identical tasks to complete, which included developing code and testing it.

                Then, in the second hackathon, we expanded the experiment to include a wider range of AI tools, such as Cursor, Codeium and Tabnine, and gave the developers short training sessions on using them effectively.

                And the results were striking. While the engineering teams using traditional methods delivered great work in a reasonably good time, the ones using AI completed the tasks far more rapidly – sometimes nearly five times faster – and with a higher degree of quality, finding edge cases more effectively and producing better unit tests.

                For quality analysts, the impact was even more pronounced: AI-assisted testing was up to nine times faster and improved the ability to identify edge cases, leading to significantly higher-quality outcomes.

                Overall, across all our experiments, we found that the average time taken to complete an entire task was around twice as fast.

                What’s more, a team of colleagues conducted a hackathon to test the value of using AI to accelerate and enhance our product discovery and inception processes – with equally astounding results. (find out more here)

                Delivering added value for our clients

                We are now starting to roll out the usage of AI into live environments with our clients, while leveraging the learning from our experiments.

                One of the key benefits we’re finding is that – as well as the significant acceleration in pace – AI removes much of the boring, repetitive or time-consuming work, such as writing unit tests, troubleshooting code and referring to community forums for solutions. This allows our developers to focus on more interesting – and more valuable – activities.

                For instance, in a recent live case with one of our clients, two developers struggled for hours to resolve a specific issue. When they finally turned to an AI tool, they fixed it in just 10 minutes, enabling them to move quickly onto a higher-level task.

                Another value-driver is that we’re able to share the results of our experiments with our clients. We understand that many of them do not have the time or resources to run hackathons of this kind, due to the day-to-day pressures of their business. But, thanks to our partner-shoring.

                approach – where we build fully integrated teams with our clients, all working towards the same goals – the insights we have developed are available for our mutual benefit. This includes not only productivity metrics, but also operational best practices, the ways AI can be integrated into workflows, and identifying the most effective AI tools for specific tasks.

                Addressing the risks and challenges of AI

                Despite the outstanding success of our experiments, we are also fully aware that there are several risks and challenges that need to be addressed with the adoption of AI.

                We believe that security and the protection of intellectual property (IP) should always be a primary concern, and that is no different when it comes to the use of AI. To mitigate this, we’ve implemented strict policies and only use tools with advanced security safeguards. For example, the tools will immediately discard any code snippets or prompts that we use once they have generated a suggestion.

                Another important challenge is ensuring that our engineers maintain ownership of their code and there is always a human in the loop at every stage of development. In its present state, AI cannot replace human judgement and expertise. While it nearly always generates great suggestions, it’s still up to our people to validate them, adapt them to our specific needs, and check for errors.

                Where next?

                As for the future… we’re truly excited. The pace of AI development is astonishing. In just a couple of years, we’ve seen transformational advances – and the next five years promise exponential improvements. As AI tools mature, the opportunities for end-to-end software development based on a series of high-level business requirements are becoming increasingly feasible.

                In the meantime, we’re committed to staying ahead of the curve. By continuously exploring new tools and refining our workflows in a constant search for improved quality and efficiency, we’re not just enhancing our own capabilities – and keeping a smile on the face of all our developers – we’re driving valuable innovation that delivers measurable benefits for our clients.

                To discuss how our AI-enhanced development – and the experiments we’ve been conducting – can benefit your business, get in touch now.

                Aleksandar Karavasilev, CTO at Damilah