Our recent trivia night brought together colleagues from across the company for an evening of questions, debate and the occasional (very confident) wrong answer. Testing our knowledge of geography, football, science and pop culture was a great excuse to spend time together outside our usual routines, and the burgers and drinks helped too.
Whether people came ready to win or simply to have fun with their teammates, the evening was full of laughter, quick thinking and the spirit of friendly competition.
Then came the World Cup, which gave us another reason to get competitive. We set up a predictions board and a sweepstake so everyone could make their picks and follow the tournament with a little extra interest. The leaderboard became a running source of entertainment throughout the five weeks of competition.
A special congratulations to Aleksandar Karavasilev, who took first place in the sweepstake with Spain, and to Renata Todevska, our top Predictions Board player. It turned out that football knowledge is holding up nicely at Damilah!
What makes moments like these worth it
What we enjoy most about activities like these is the mix of people they bring together.
Colleagues who sit in different teams, work on different projects and may not cross paths much day-to-day end up sharing a table, debating answers and cheering on their sweepstake picks side by side.
These moments create the kind of familiarity that makes our team work well together. Getting to know one another outside of work helps us communicate more openly, support each other, and ask each other for help when we need it.
A good workplace is not only about getting the work done. It is also about enjoying the people you work with and celebrating these shared wins.
If you are curious about life at Damilah or thinking about joining us, check out our open positions here.
In many near-shore and outsourcing environments, the product owner role is difficult to shape well. It centres on building external products based on pre-set client requirements, so product owners end up acting as structural translators between client and development team. They receive instructions, shape them into user stories, and the team builds. There is rarely space to question whether those requirements are right.
A different approach starts with giving product owners the authority and scope to lead the full lifecycle – and that starts with product discovery.
Product discovery done right
Product discovery is an active, ongoing processproduct owners should lead from the start of any project – before development begins. They should ensure engineering effort goes towards problems that have been validated with real users. In practice, that means:
Step 1: Direct customer interviews. Run discovery sessions with users to understand their real challenges and how they work.
Step 2: Map customer pain points. Map user journeys to identify friction points and the problems that matter most to customers.
Step 3: Affinity mapping. Note patterns from user sessions, group common issues together and draw out clear themes to guide priorities.
Step 4: Ideation. Run workshops with design and development teams to explore solutions before any architecture decisions are made.
Step 5: UX/UI alignment. Work closely with designers to move from concept through wireframes to prototype.
Step 6: User testing. Put high-fidelity prototypes in front of real users before any production code is written – to confirm the solution solves the right problem.
The next step: the product engineer
As AI takes on more technical execution, the line between strategy and production is starting to blur. At Damilah, we are actively investing in this shift, developing our product people towards a new hybrid role: the product engineer.
A product engineer combines strong product thinking with hands-on technical capability. By training our product people to work with live coding tools and AI, we are building professionals who can take a product from discovery through to a working prototype.
What this looks like in practice
Iskra Ristovska is one of our product leaders moving from a classical product owner role towards that of a product engineer.
Working with a core client, she uses the client’s internal AI tools to analyse data, map dependencies and extract requirements in real time. During user discovery sessions, she works in a live coding environment, building, testing and refining prototypes on the spot. This shortens the gap between vision and working software, so customer feedback turns into tangible results quickly.
To support this way of working, we run a weekly review session with key stakeholders. We present the latest prototype, gather feedback and implement changes within the same cycle. The pace only works because it stays anchored to genuine client pain points – the AI tools support that focus, rather than becoming an end in themselves.
“We don’t wait for discovery to finish before we start building. We build during discovery, so customer feedback can turn into something tangible in the same session.” – Iskra Ristovska, Product Leader at Damilah
What this means for product delivery
When product ownership is reduced to backlog management, something gets lost at every handoff. The product manager speaks to the client, translates requirements to a designer, who hands them to an engineer, and something is lost at each step. That is how product feature factories form: lots of activity, bloated budget and not enough impact.
Our investment in product capability addresses that directly. Our product owners bring senior-level discovery expertise and live-coding technical capabilities. With Damilah, you get a partner who understands your business architecture, can challenge your assumptions, and move from problem to working solution faster.
If you would like to explore how this approach could strengthen your product delivery and reduce friction between strategy and execution, get in touch here to start the conversation.
Across many of the tech ecosystems that have built reputations on software delivery, we see strong engineering talent and reliable execution, but limited experience of real product ownership.
Central and Eastern Europe is home to over 3.5 million employed IT specialists, one of the largest regional tech workforces in the world. Yet for much of the past decade, the dominant model for that talent has been outsourcing and staff augmentation: building other people’s products, to other people’s specifications. According to Dreamix’s 2026 overview of the region, the sector has only recently begun shifting beyond staff augmentation, with partners starting to expect product direction and strategic thinking alongside delivery.
That model has driven real economic growth. But it has also limited how product culture develops. When a company functions primarily as an outsourced partner, its teams are frequently positioned as pure executors. Requirements arrive pre-packaged from abroad, and success is measured by output – tickets closed, deadlines met, specifications followed – rather than by actual outcomes for users.
In many offshore organisations, product owners and managers act mainly as go-betweens. The role is often limited to translating external client requirements into technical user stories, managing the backlog, and feeding specifications to development teams. Because teams are rarely part of the initial problem-solving phase, they miss out on the product discovery work that surfaces real customer pain points.
“Moving away from a pure execution mindset requires shifting our definition of success. True product culture is not about how many features an organisation can ship; it is about deeply understanding user needs and developing the autonomy to create meaningful solutions.” – Olgica Strezoska, Principal Product Owner at Damilah
Inspiring cultural transformation
Cultural change begins when someone recognises a gap and decides to do something about it.
At Damilah, we are proud to have people like that on the team. One of our Principal Product Owners, Olgica Strezoska, recognised the need to strengthen product thinking within North Macedonia’s tech ecosystem. She saw that building a sustainable product culture required a dedicated foundation of community support, knowledge exchange and continuous education.
Driven by this vision, she co-founded ProdACT, a non-profit she now leads as President.
ProdACT is an educational hub for product practitioners across North Macedonia – a place to develop product thinking, learn from peers and engage with modern methodologies.
“Product culture doesn’t develop in isolation. It grows when people share what they’ve learned, challenge each other and build on each other’s experiences. That’s what ProdACT exists to create.” – Olgica Strezoska, Principal Product Owner at Damilah
Through events and curated content, Olgica advocates for outcome-focused roadmaps, user-centric design and genuine team participation in product strategy.
Building a product mindset
Olgica’s work in the community reflects the core product values we hold at Damilah. We intentionally move away from the assembly-line execution model, giving our product professionals the autonomy and tools they need to do their best work.
Product owners at Damilah operate well beyond the traditional scope. They take part in proactive product discovery, leading efforts to surface the right requirements rather than wait for the customer to pass them down. In practice, this includes:
Direct customer research
Continuous user testing
Data-driven feedback
Strategic prioritisation
Partner-shoring
As AI takes on more of the execution work, expectations of delivery partners are changing. Clients are no longer looking to hire developers to execute a static checklist. They need a partner to focus on driving their key business metrics and avoid slipping into feature factory mode.
“The biggest misconception about [modern] Eastern European outsourcing is that you’re buying hours. You’re not. You’re buying engineering judgement.” – Denis Danov, CTO at Dreamix
Instead of simply checking off tasks, your offshore/near-shore software development partner should be committed to building successful products that deliver sustainable, measurable market value. This is what we call partner-shoring – and it starts with bringing product thinking to the table: knowing which problems to solve, in which order, and why.
If you would like to work with a near-shore software development partner that brings product judgement as well as engineering strength, get in touch here to explore how we can support your next stage of growth.
Our recent team building weekend in Ohrid was a great reminder of that.
The weekend began with a treasure hunt through the city. Teams worked together to solve clues, navigate Ohrid’s streets and enjoy a bit of friendly competition along the way. It was fun, but more importantly, it strengthened the trust, collaboration and teamwork that sit at the heart of our culture.
Later, we had lunch by the lake, taking time to talk, share stories and enjoy each other’s company outside our usual day-to-day work.
The weekend also gave us the chance to celebrate our people and reflect on everything we’ve achieved together over the past year. Hearing colleagues share project successes, milestones and lessons learned was a powerful reminder that behind every result is a group of talented individuals whose commitment keeps Damilah moving forward. Taking the time to recognise those contributions made the experience even more meaningful.
Celebrating who we are, not just what we deliver
One of the highlights of the weekend was our evening karaoke party on a boat. Between the music, laughter and unforgettable performances, it captured something we value deeply at Damilah: creating an environment where people feel comfortable being themselves.
The setting was beautiful, but what made the weekend special was the people. Moments like these help us build stronger relationships, create a real sense of belonging, and reinforce the values that shape how we work together every day.
Because at the end of the day, our culture isn’t defined by policies or processes – it’s defined by the connections we build, the support we give one another, and the experiences we share.
And this weekend gave us plenty of all three.
We’re proud of the team we’re building and the culture we’re shaping together. If you’re curious about life at Damilah or interested in joining us on the journey, we’d love to hear from you.
Speaking to CTO Craft at the 2026 London conference, Damilah CEO Iain Bishop outlined the stages organisations move through on the path to AI maturity: from early experimentation, through to copilot mode, to full orchestration and agentic working. He shared what AI maturity means for team structure: why teams get smaller, why product teams start to outweigh engineering, and what the role of a senior engineer looks like now.
Iain draws on what Damilah is seeing directly with clients to describe what changes, what stays the same, and what leaders often get wrong.
The full interview covers:
The stages of AI adoption, from experimentation to a full agentic model
What separates companies experimenting with AI from those approaching real maturity
The mindset shifts leaders need to make, including rethinking how teams are structured
What “senior” means for engineers in an AI era
How AI is reshaping the balance between product, engineering and the business
It has also formed the foundation of the CTO Craft Leadership Masterclass on Engineering in 2028, where Iain presented and expanded on the findings in person earlier this year. We summarised the key takeaways from that panel discussion in our post-event blog here.
If you are working through any of these questions in your own organisation and would like to talk through practical next steps, get in touch. We work with technology leaders at every stage of the AI maturity journey.
Fifty people watched the panel discussion in London in May 2026 on the theme of Engineering 2028: A Leadership Masterclass. Chaired by Iain Bishop, the panel included:
Lucinda Faucher, VP of Product at Valve (platform for sales, marketing and distribution for flexible workspaces)
Alttaf Hussain, Director of Engineering & AI Innovation at Yoti (platform for privacy-focused identity and age solutions)
Vishal Manani, CPTO at Eckoh (platform securing sensitive data in contact centres)
Chris Parsons, CTO and independent consultant, writer & AI strategist
Where to start with AI orchestration
A theme that continued throughout the session through to the final questions was how to get teams on board with AI orchestration. Vishal Manani gave the example of his software engineering colleague, describing his role as similar to an artisan baker’s. The colleague was loathe to become a machine operator making white sliced bread. While this example is of someone who respects their trade and the outcomes from his work, the panel knew of others who simply enjoyed coding for the sake of coding.
Leaders need to understand the motivations of individuals in their teams to bring them on the AI journey. As stated in our Engineering 2028 report, the demand for software is growing with the rise in productivity gains from AI. However, no one shied away from the fact that some people will lose their jobs. Those who are using AI now have a greater advantage and will succeed longer term.
Engineers’ roles are changing rapidly. Job descriptions still haven’t changed but engineers must accept that they will spend most of their time refining agents rather than writing features directly. Those who refuse this kind of role will eventually need to look to other professions, as traditional engineering roles disappear.
This doesn’t mean giving up. Leaders can have a huge impact on adoption rates. Encourage those who have embraced AI early on to become AI champions in the team. Given them the licences they need to try out what they want to do. Then get them to share their successes. This kind of positive reinforcement is a tried and tested method of increasing technology adoption, and it works with AI too.
Speed vs quality
The tension between speed vs quality with AI is widely discussed. The software development lifecycle (SDLC) is changing, with bottlenecks moving from coding to reviewing.
“We are in a point in history where you can go from vibe coding to production at blistering speed.” – Alttaf Hussain, Director of Engineering, Yoti
Especially in private-equity backed companies, AI is being jumped on to pick up the pace of software creation. However, the panel was concerned that what comes before and after code generation has not kept pace. Requirements, review and governance are still reliant on humans. With AI possibilities of creating more code than ever, organisations need to consider that there is still a barrier to achieving results at speed, unless we want sub-standard products.
Human oversight will still be critical for the foreseeable future, as AI’s limits with judgement are clear. One example shared by Vishal Manani was of an AI agent writing brittle test and then changing the application’s code to make the test pass. This caused an outage. You can speed up one area of software development, but it will result in other areas slowing down.
The human moat
When asked about what stays with us, the humans, the panel agreed on one area above all: accountability.
“When things go wrong, who owns it, monitors it, reviews it?” – Lucinda Faucher, VP of Product, Valve
Chris Parsons referenced Tristan Harris’ documentary as an extreme example of where lack of accountability can lead to with AI. If companies argue that they are not responsible for someone’s “AI psychosis”, despite creating the AI caused it, who takes responsibility for disastrous consequences?
Traditionally, the person who writes the code takes responsibility for its accuracy. With AI, the lines are blurred. It can build fast, but it might build entirely the wrong thing. Again: the SDLC bottleneck is simply moved – not eliminated. AI needs regulatory compliance and security oversight built in from the start, with a “human in the loop” throughout the SDLC.
Measuring success
According to Lucinda Faucher, nothing has really changed when it comes to measuring success. Metrics like revenue generation and customer satisfaction is what any business and its stakeholders will care about. Ultimately, what we build needs to solve our customers’ problems. Otherwise, what’s the point?
A question came in from the audience around token usage being used as a measure of success by Meta, and whether the panel saw that as a good metric. Chris Parsons disagreed – proxy metrics may be useful to measure steps to getting to where you need, but technical metrics don’t have the same meaning any more. Bottleneck identification is more critical to address than lines of code or token counting.
Engineering 2028 Leading Human + AI Teams Responsibly
A joint Damilah & CTO Craft survey of senior technology leaders
The panel’s recommendations around AI orchestration were relevant to any organisation. Here are the five top tips we summarised from the event:
Start with low-risk, controlled workflows in non-safety-critical areas, as AI is still not ready to review AI-generated code without humans in the loop
Build evaluation frameworks before scaling AI adoption, as AI output is non-deterministic, and decide “what good looks like” before implementation
Create an AI champion programme, led by your early adopters, to bring more of your people on the AI journey
Focus on your requirements quality and formalising human checkpoints to speed up the SDLC
Establish clear governance and accountability structures with your leaders and stakeholders from the start, as you can expect things to go wrong sometimes
Following the event, several people felt that the conversation was just getting started. How do we persuade our leaders and stakeholders that we may need to slow down to go faster? How do we get consensus on “what good looks like” before implementation? If you’re a technology leader who would like to discuss practical next steps around leading human + AI teams responsibly, follow our Linkedin and subscribe to our email newsletter to hear about our events before anyone else.
To introduce the Byte, I shared findings from our Engineering 2028survey. Almost all survey respondents were already using AI. Half reported the greatest benefits in AI being increased speed and innovation.
I shared how productivity expectations are rising more sharply among teams that have more AI experience. This lies in contrast to the usual technology “hype cycle” trends, where increased utilisation leads to disillusionment.
Teams tend to start using AI tools following a co-pilot model. They use them to accelerate the way they have always worked, but as experience increases, they find new ways of working to fully utilise the AI tools.
There has always been latent demand for more software in product software companies. AI removes the barrier to product creation by creating code at lightning speed, leading to increased demand.
This all paints a rosy picture… so what’s going to slow us down?
Capability gap: Engineering is faster than ever, making leadership harder. AI adoption is a change management process.
Operating model gap: In such a fast-changing environment, standards and process can’t keep up. There are fragmented tools and shadow AI usage.
Governance gap: Compliance and security concerns, IP leakage and other risks are emerging. Accountability and ownership are unclear.
We can’t simply lock down AI – so how do we, as CTOs and technology leaders, control what’s going on?
“The problem with autonomy without alignment is that it doesn’t scale.”
The path to engineering orchestration
As competition, stakeholder expectations, and advances in AI technology increase, we need to facilitate the safe adoption of AI technologies. We need to create an environment where we won’t get in the way of progress and increased productivity. But we need to take safety, compliance and consistency seriously.
Technology leaders need to change the way we work: we need to move to a model of engineering orchestration. This means using a platform that allows us to manage the use of agentic AI workflows while retaining oversight and control over what is produced.
The human controls agentic workflows across every aspect of the software development lifecycle (SDLC). All standards, such as technical, regulatory and design systems, are included in the orchestration platform. This is the shift from doing work to designing systems that do work.
Changing skills priorities
As engineering teams become orchestrators, team sizes will reduce and individuals will become more versatile. New roles will appear such as Product Engineer – engineers who understand customer/end-user needs, as well as being able to orchestrate the solutions. Other Engineers need to develop system thinking. They need to understand the bigger picture around architecture, non-functional requirements and design patterns.
As development cycles shrink, miscommunication will have an even greater negative impact, so leaders need to consider cultural differences, as well as time zone and proximity even more carefully.
“Being fluent in AI is becoming as important as being able to type on a keyboard.”
Skills needed are evolving. Prompt engineering is a basic need for people involved in software engineering. A key differentiator will be commercial understanding: really understanding the value we bring to our companies and customers. The best engineers will be creative and curious – exploring what’s possible and approaching problems from a different angle.
The “human moat” surrounding orchestration and governance is a set of unique, irreplaceable human skills: strategy, leadership, empathy, ethics and creativity.
Our role as leaders is to hire for, grow and retain these skills, understanding that our own role is being redesigned. We need to:
Move on from managing tasks to designing systems
Raise AI fluency across our organisations
Strengthen governance without slowing innovation
Build product-focused, commercially aware teams
What AI orchestration looks like in practice
Our CTO, Aleksandar Karavasilev, demonstrated to the CTO Craft community Damilah’s multi-agent platform (DMAP) to bring AI orchestration to life. Here are the principles behind our AI orchestration platform:
Start with context, not prompts DMAP begins with an explicit project context: the technology stack, governance constraints, standards, and which models handle which tasks. Every agent working on your project operates from the same rules.
Run work through a structured workflow DMAP supports agentic workflows within all stages of the SDLC, such as requirements, design, development, testing and validation.
Humans in the loop by design Work can be paused, reviewed, approved or rerouted at any stage. You define where human sign-off is required, what a pass looks like, and who is accountable.
Every decision is auditable DMAP keeps a full record of what ran, what was produced, what a human approved, and why anything was rerouted.
Cost management With model costs changing often, it is critical to monitor token usage and costs. DMAP includes this on a dashboard for full visibility and control.
Engineering 2028 Leading Human + AI Teams Responsibly
A joint Damilah & CTO Craft survey of senior technology leaders
Those who are continuing to add AI on top of their existing SDLC, relying on experienced people to absorb the extra review and governance load, will soon struggle. Those who redesign how work flows through the system entirely will remain competitive.
At Damilah, we are helping companies understand the barriers to AI orchestration. If you are looking into making the change, get in touch today to set up a conversation with me and the team.
Iain Bishop CEO, Damilah
Watch the recording
CTO Craft Con London felt different this year. A year ago, discussions were around AI pilots, with people getting excited about the companies who were taking the next step with AI.
This year, there was a mix of nervousness around the dramatic changes we are already seeing and competitiveness amongst those who were steaming ahead (primarily AI-native start-ups).
We partnered with CTO Craft to survey technology leaders about how engineering might look in two years’ time, leading to the report, Engineering 2028: Leading Human + AI Teams Responsibly. What struck us at the London conference was how closely the report aligned to what so many people told us about their experiences.
“The shift in mindset over the past year has been striking. At CTO Craft 2025, many leaders were still cautious about AI and how it would impact our industry. This year, everyone agreed that AI is already changing the industry, and at speed. After only a year, we’re mostly hearing about how companies are using AI in practice.” – Aleksandar Karavasilev, CTO at Damilah
Headcount is not shrinking as AI increases output
According to our Engineering 2028 report, increased AI-enabled productivity drives increased demand for more software. Ideas that were sitting in a “maybe later” pile are becoming viable and roadmaps are rapidly stretching. Companies need to maintain their headcount to keep up with the latent demand for new features and products.
The CTOs we met at CTO Craft were from a range of companies. Those at investor-backed software companies largely agreed with what was said in the report, as they face increased pressure to deliver AI-based solutions. At more established or non-software companies, issues were still around legacy systems preventing advancement with AI, meaning humans are more in need than ever. And at the opposite end, there were software start-ups with a couple of people able to create a brilliant product from scratch with AI, which would have previously taken months and a critical mass of software engineers.
Speed is shifting the bottleneck in the SDLC
“For so long, engineering has been the bottleneck in the software development lifecycle (SDLC). As that constraint starts to ease, pressure moves into other parts of the lifecycle, especially discovery, alignment, and review.” – Iain Bishop, CEO at Damilah
In Iain’s talk at CTO Craft, he highlighted the findings of the report Engineering 2028 around AI transforming the SDLC. New features or products are possible to generate in a fraction of the time with fewer people involved, but they are not perfect and not necessarily built for purpose. So, while AI rapidly accelerates code creation, removing a major bottleneck in the SLDC, when it comes to code review, we see new delays.
Moving towards AI orchestration
This displaced bottleneck is changing how software engineering teams work every day and how they are structured. In our Engineering 2028 report, organisations earlier in their AI journey reported using AI to help engineers move faster on individual tasks. Organisations further along the journey are redesigning the teams altogether.
“Many of the presentations I saw at CTO Craft validated and reinforced our view that the future of engineering is orchestration. Companies are increasingly using AI platforms to support how work flows through teams.” – Iain Bishop.
Software engineers need to become “AI orchestrators”, setting tasks for multiple agents. The need for human review of AI’s output increases, as well as checking agents’ processes throughout the development process.
Engineering 2028 Leading Human + AI Teams Responsibly
A joint Damilah & CTO Craft survey of senior technology leaders
An interesting talk from Sonar compared the difference between different LLMs when it comes to creating code. Newer models create significantly more lines of code. And the more code you produce, the more bugs will be produced. Sonar proved this and, while the bugs were minimal, they do stack up when it comes to writing millions of lines of code in seconds. This means newer LLMs inevitably produce more bugs.
So, speed is improving, but quality is not automatically keeping pace. This backs up what respondents felt in our survey with CTO Craft, where only 8% are seeing AI generate higher quality outcomes. More code means more to review, so governance needs to accelerate in line with the speed of AI’s coding. Governance needs to be built into how teams operate from the start.
What AI cannot replace
As Iain said in his talk: “AI builds the how. We own the why.”
The combination of judgement, strategy, empathy, and a deep understanding of both the customer and the business determines whether good work gets built at all. After the Women in Tech breakfast, Damilah’s Head of Marketing, Julia Valentine, reported:
“One woman I spoke to said that ‘soft skills’ sounds like ‘easy’, i.e. the opposite of hard. But the one thing AI can’t do is ‘soft skills’. People who excel at soft skills (these days, often women) will be more desirable in tech leadership than those with ‘hard skills’, such as coding. Could this be an opportunity to seize gender equality in tech?”
AI is highly effective at execution, but it does not understand context in the way people do. It does not carry accountability, nor does it understand what matters most to the business or customers.
As Hywel Carver, CEO at Skiller Whale, put it: “The one thing AI cannot do that humans can is empathy.”
How engineering teams are evolving
As AI takes on the kinds of tasks engineers used to focus on, engineers now need to get a better grasp on product, even transforming from software engineers into product engineers. This means understanding what both customers and what the business want to achieve, connecting technical decisions to commercial outcomes.
Leaders need to understand this shift to develop the right people in their teams, as well as hire the right people into new roles that may arise. CTOs’ and CPOs’ roles are becoming more closely aligned, and we are seeing the rise of the CTPO role.
As Iain put it in his talk, “Engineering in 2028 isn’t a tooling upgrade. It’s a leadership redesign.”
The organisations that come out ahead over the next few years will be those that have genuinely rethought how their teams are organised around them: moving from the focus on AI tools to AI orchestration.
What Will AI-Enabled Teams Really Look Like?
Shifting Roles, Smarter Tools, and the Future of Software Delivery
KEY TAKEAWAYS
Introduction
This roundtable brought together CTOs, heads of engineering, architects, and product leaders from across sectors—finance, retail, government, education, and energy—to explore how technology teams will evolve over the next two years as AI becomes a core enabler of productivity and creativity.
Rather than trading notes on which tools are trending, the discussion focused on people, culture, and capability: how to steer teams through this transformation responsibly. There was a strong sense that AI is revolutionising the how of engineering, but not the why.
As one CTO said: “AI can write the code, but it can’t design the solution, own the outcome, or understand what the customer truly needs.”
The central thread of the morning was clear: AI will change the texture of work and accelerate delivery, but the human element—judgment, empathy, and collaboration—will only grow in importance.
Will Developers Disappear?
Few participants believed AI would replace engineers outright. Instead, the consensus was that roles would evolve.
“I’d be very surprised if we wipe out software developers in two years,” one engineering manager remarked. “It’s moving fast, but the tools still produce too much that’s subtly wrong in ways that’ll hurt you later.”
While AI is now taking on more junior and mid-level coding work—automating refactors, bug fixes, and boilerplate—participants agreed that experienced human oversight remains critical. The bigger question was how to ensure junior developers continue to learn the craft. One CTO captured the concern well:
“If AI does all the grunt work, where do the juniors cut their teeth?”
Several argued for creating structured apprenticeships and guided learning environments where early-career engineers can pair with AI tools while still learning principles of architecture, quality, and design.
The Changing Shape of Teams
The conversation repeatedly returned to how AI is collapsing traditional boundaries. Titles like front-end, back-end, tester, and DevOps are blurring. Teams are becoming cross-functional units focused on problems, not functions.
“We’re hiring problem solvers, not just coders,” one CTO explained. “They’ll do whatever’s required—design, infrastructure, deployment, even user research—because AI makes it possible to wear multiple hats.”
The archetype of the future is the “product engineer”—someone equally comfortable exploring business context, shaping solutions, and delivering production-quality systems with AI as a partner. Architects, QA, and SRE roles will continue to grow in strategic importance, ensuring accountability and coherence as delivery accelerates.
The Human Element: Judgment, Empathy, and Curiosity
Every leader emphasised that AI doesn’t eliminate human creativity—it demands more of it. The ability to ask good questions, challenge assumptions, and apply judgment has become even more valuable.
“Curiosity is the new superpower,” one CTO said. “The people who’ll thrive are those who keep testing the boundaries of what these tools can do—and when to stop trusting them.”
Curiosity, empathy, and communication are now as vital as technical skill. Engineers must understand how their work fits into business value and user experience.
AI amplifies output—but only humans can decide what matters and why.
Governance and Accountability
The hardest friction isn’t technical—it’s institutional. Risk, legal, and compliance teams are anxious about ownership, data exposure, and IP liability.
“Our biggest problem is fear built on lack of understanding, not lack of capability,” said one chief architect.
Several companies are creating AI governance frameworks: approved tool lists, role-based permissions, “human-in-the-loop” release gates, and model benchmarking standards. Others are reframing compliance by demonstrating that AI can mitigate existing risks—speeding up regulatory delivery, identifying vulnerabilities, and improving audit trails. As one participant summarised:
“Governance shouldn’t be a brake. It should be a seatbelt—something that lets you go faster safely.”
From Tools to Platforms
The group warned against uncontrolled “tool sprawl.” Hundreds of disconnected AI apps create fragmentation, data risk, and confusion. Many are now consolidating around agentic platforms that combine flexibility with control—enabling teams to plug in models, enforce policies, and swap components safely.
“We don’t let every team choose their own CRM; why would we let them choose their own AI stack?” one CTO asked.
These emerging AI operating environments balance innovation and governance. Over time, they’ll evolve into multi-agent systems, where AI components handle specific roles—code review, compliance, testing—under human orchestration and oversight.
Proximity, Collaboration, and Speed
AI is reshaping the pace of work. Teams can now ship prototypes and validate ideas at unprecedented speed—but that velocity brings new coordination challenges. Participants agreed that proximity is becoming more, not less, important.
“Customers want answers now, not after someone in another time zone wakes up,” one delivery lead said. While remote and offshore teams remain valuable, they must be self-contained and empowered, not reliant on serial handoffs.
AI makes it possible for smaller, tighter, cross-functional groups to deliver end-to-end. But success depends on communication, shared context, and immediacy of feedback—qualities easier to sustain when teams are close to each other and to their users.
As one CTO put it, “AI may erase distance in code, but not in understanding. Proximity is still what creates trust, clarity, and speed.”
Legacy Systems and Reverse Engineering
Participants shared how AI is transforming work on legacy systems—mapping ancient codebases, diagnosing defects, and even converting entire applications.
“We migrated a five-year Python system to C# in a month using AI,” one CTO recounted.
But all agreed that architectural judgment remains a human responsibility: deciding what’s worth rebuilding versus replacing.
The Multi-Agent Future
Looking ahead, many expect the rise of multi-agent systems—AI agents working collaboratively across roles and contexts.
“We’re moving from assistants to collaborators,” one participant said. “Dozens of agents will handle testing, compliance, documentation—while humans set the rules and decide what’s safe to ship.”
This evolution raises deep questions about accountability and verification. The group agreed: no matter how advanced the tools become, humans remain the final line of trust.
Key Takeaways
AI will reshape roles, not remove people. Engineers remain central—owning design, ethics, and accountability. The future belongs to product engineers and technical leaders who can orchestrate AI, not compete with it.
Human curiosity and breadth are strategic advantages. The best technologists will pair analytical depth with creativity, empathy, and adaptability. Curiosity is what keeps teams learning as tools evolve.
Proximity drives performance. Speed and quality depend on teams being close—to each other and to their customers. AI may accelerate delivery, but true understanding still requires human connection, conversation, and shared context.
Governance must empower, not constrain. Treat compliance frameworks as guardrails, not red tape. Maintain human checkpoints for quality, security, and ethical integrity.
Build for the agentic future. Consolidate tooling into managed AI platforms, design for human oversight, and prepare for multi-agent workflows that handle routine tasks while people focus on direction and design.
Closing Thought
The roundtable left everyone with plenty to reflect on. The consensus? We’re at the beginning of a generational shift. AI won’t replace engineering teams—but it will reshape them. The organisations that succeed will be those that invest in people, build cross-functional trust, and prepare now for the platforms, roles, and rhythms of the AI-native future.
Next, we’ll be launching a survey to explore what engineering teams could look like in 2028—asking industry leaders how they envision the structure, roles, and skills of future AI-native teams. We’ll share the results at CTO Craft Con in London in March 2026.
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.
At Damilah, we believe that company culture is not just about how we work, it is also about the moments that happen around it. This summer, we added a couple of new experiences to the mix.
In traditional IT outsourcing, the product owner role tends to shrink to one task: backlog management. Requirements come in, user stories go out. That model is starting to show its limits. There is growing recognition that product owners in outsourced teams need more than backlog management skills and that the role itself needs to evolve.
When tech ecosystems are shaped by outsourcing and staff augmentation, software development teams risk becoming expert executors. But, especially in the age of AI, what they need is a strong product culture: a mindset that focuses on user outcomes, not just shipped features.
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Delivering Great Software on Time
They’ll be able to test those products quickly, then easily adapt them based on insights gained and iterate at pace.
Companies will be able to rapidly adjust their products, create multiple variants, make huge pivots – or anything in between – in response to changes in market conditions and new opportunities, all the while creating outputs of higher quality.
And all this will be possible at significantly reduced cost – thus negating the need for large amounts of high-risk, early-stage funding. Instead, investors will be able to place their bets on an MVP, or adaptation of an existing product, that is already proven to be successful and scalable.
In all of the above, the potential value will be created by the smart integration of the right AI tools to accelerate and refine processes at every step.
However, this isn’t the distant future. In many cases, it’s already happening.
Yet for many firms – particularly those backed by private equity (PE) investors – the reality isn’t always matching up to the vision.
So why does this gap exist?
Overcoming the blockers to AI-driven value
Because a typical PE investor will be looking for a substantial return over a period of three to five years, many initial investments will have been made prior to the explosion of generative AI tools that are now massively disrupting almost every industry. Usage of these tools may not have been part of the original investment thesis – but they absolutely need to be now.
However, too many organisations are failing to keep ahead of the game and unlock the enormous potential value that AI tools can deliver.
In our experience, the main blockers are as follows:
1. Time constraints
We are seeing many companies suffering from a simple lack of bandwidth to explore how best to leverage the capabilities of AI.
This is often compounded by intense pressure to deliver results against the original investment thesis. Pressure builds on top of pressure as new opportunities for growth are discovered or acquisitions are made which then need to be carefully integrated. This can leave little room for reflection or experimentation with new tools.
Add to this the rapid pace at which AI tools are constantly evolving and improving, and it can seem almost impossible to keep up – let alone move ahead of the game – when it comes to understanding the best ways to deliver practical, value-driving applications of the technology and successfully roll them out across the organisation.
2. Hype versus reality
As has always been the case whenever a new, heavily hyped technology floods the market, most AI tools are currently not mature enough to deliver fully on the promises made by their vendors. There is no doubt whatsoever that AI is a game-changer. But being able to work out the difference between the sales pitch and the practical reality can be challenging and requires deep expertise.
3. Security, legal and compliance challenges
Legitimate concerns exist around issues such as security, regulatory compliance and IP-protection – many of which are yet to be clarified and resolved. While most AI tools offer, for example, zero data retention and assurances around IP, understanding and mitigating these requires time, experience and focus – for example, in ensuring the tools are correctly configured to be fully compliant.
Overall, it’s vital to address these issues. And soon.
If your company takes too long to release new products or new features, you may quickly find yourself in trouble as your time to value becomes severely eroded. Your new competitors – who could be almost anyone armed with the right AI tools and the ability to use them effectively – can already enter the market and pull the rug from under your feet, able to adapt to market demands and seize opportunities far faster than companies with legacy platforms and products.
Learning from best practice
So how can you start turning AI into tangible value?
They would also be able to consult on best practice, drawing on their own experiences in the field alongside those of similar clients they have supported.
The aim would be for them to help you deliver high-quality outcomes using the most effective AI-enabled processes and techniques.
To address legal and security issues, it’s essential to have an AI policy in place that creates the appropriate guardrails for safe and compliant usage of AI. Above all, we strongly recommend that absolutely everything always remains subject to human accountability – so have your people reviewing and refining every AI output at every stage of your processes. Here at Damilah, we do – and always will.
Last, but not least, there needs to be a shift towards outcome-focused roles. In other words, enabling AI tools to handle more of the laborious, time-consuming, detailed technical work – thus allowing a skilled human workforce to maintain oversight while concentrating on value creation and strategy.
New pricing models and investment strategies
And here’s one further thought. It’s likely that agentic AI tools will soon become prevalent in many organisations, which may have a profound effect on commercial pricing models.
Instead of the traditional pay-as-you-go or seat-based SaaS pricing structures, we may soon find outcome-based pricing models becoming the norm – that is, where fees are based on successful delivery and results.
This, in turn, would require PE houses to review – and radically adapt – their investment strategies, resulting in major impacts on the organisations they back.
It’s too early to predict precisely how this AI-powered future will unfold. But one thing is certain: all organisations – and particularly those funded by PE – need to remain fully alert to the fundamental shifts that are occurring and be nimble enough to rapidly adjust. Those that don’t risk becoming dead in the water.
We can help you transform your AI vision into genuine value . To explore the possibilities, get in touch now.
