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 process product 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

“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.  

North Macedonia is a good example of this dynamic. Despite a wealth of technical capability, the local industry has historically tilted heavily towards traditional IT outsourcing, exporting talent to build products for international clients. 

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. 

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. 

I remember examining one trust’s rules on preventing staff from working too many long days in a row. These rules keep patterns safe, but they aren’t one-size-fits-all and often require a more nuanced approach. For example, a high-intensity inpatient ward needs much tighter guardrails than, say, a 9-5 community service.

And that means everything can get very complex, very quickly.

Until now, trusts have frequently needed to find workarounds to accommodate such scenarios, since most software providers don’t offer this level of configurability – largely because older legacy systems simply can’t manage that kind of complexity.

But, recently, we’ve been asking ourselves: do we need to be so rigid? Particularly because the integration of AI into software development processes is opening up a whole new world of possibilities that can help providers solve challenges like these.

So, we’re now experimenting with automation that uses AI to let units set their own workflows without needing a developer to change the core code. The goal here is to build a system flexible enough to handle such demands – turning what used to be a technical blocker into a genuine innovation.

Leaning into the challenges

The kind of challenge and solution outlined above is just one example of the way AI is starting to become a powerful problem-solving tool for rostering software providers.

In our experience, the most common challenges are:

• Inherited technical debt: Providers often find themselves dealing with outdated legacy systems or needing to re-platform the whole system. This can feel like that well-used analogy of changing the engine on an aircraft while it’s still flying.
• Integration and interoperability: To reduce complexity and ease end users’ workloads, it’s vital for rostering systems to integrate seamlessly into other operations, such as HR, finance/payroll and compliance.
• Security and compliance: Since many end users are working in essential services, getting this right, so it’s easy to ensure staffing levels are always correct, is non-negotiable, both ethically and legally.
• End-user reassurance: Typically, administrators can find it hard to visualise the solutions they need, or may have little trust in technical solutions as they’ve been let down previously. It’s vital, therefore, to have the capability to work closely with users to give them the confidence they need.
• Scaling quickly and effectively: Often rostering platforms will need to scale rapidly – which can be highly challenging if they’re built on outdated or niche technology.

These real-world headaches mean that, all too all too frequently, many end users have to struggle with inflexible, underperforming tools or the burden of manual admin, while managing unpredictable demand, unforeseen scenarios and frequent last-minute changes.

A better way of rostering

Based on our experience of working closely with rostering software providers, we have a clear picture of these major pain points and how such issues are all too common across many industries.

To achieve outcomes like this, AI tools offer the opportunity to massively shorten feedback loops when developing and iterating new versions of existing systems. It’s even possible for providers to undertake rapid prototyping and validation in a live environment. This could involve working directly with end users to build multiple iterations of a system in order to demonstrate exactly how a solution will work in practice.

Additionally, AI’s ability to analyse vast data sets can deliver valuable predictive analytics, such as an understanding of when staff absences or customer/patient demand are most likely to be at their peak. Based on historical data patterns, it can make intelligent suggestions for administrators to choose from.

Or, as in the example at the beginning, AI’s capacity to take on previously impossible challenges enables developers to solve major technical problems that previously would have been considered far too time-consuming and financially unfeasible. An AI-powered tool can also help to ensure rostering is done as fairly as possible, without any human biases. With a good dose of transparency, it can show staff how and why rostering decisions are made so no one feels they are being singled out for unfair treatment – for example, by demonstrating that everyone will end up working an equal number of public holidays throughout the year.

Human accountability

Despite the undeniable advantages that AI can bring to rostering tools, for the foreseeable future there will always need to be human involvement throughout the process. The overriding aim should be to improve productivity by reducing development or admin time, rather than to replace human judgement.

In the first instance, we would always recommend that AI features be introduced gradually into rostering tools and with full transparency – not only to ensure maximum effectiveness, but also to help build trust among users.

Before moving to anything near full automation, a tool should offer explainable alternatives to users – for example, by suggesting several options with a rationale of why it has proposed each one. It would then be up to the human administrator to select the most appropriate, all the while helping the AI to learn what works best in that specific organisation’s context.

Transparency and explainability are also crucial when it comes to ensuring all outputs lead to full compliance, particularly in sectors like healthcare and education, where regulatory observance and safety are critical.

The path forwards

The future of AI-powered, demand-driven rostering and rostering software development – using systems that are accurate, compliant and sensitive to end users’ needs – is now within reach.

To get there, it requires a highly collaborative approach between end users and software developers who have in-depth product and domain knowledge and are powered by expert engineers with advanced AI skills.

To find out more about how we can help you remove the pain from rostering software development with our AI-powered solutions, get in touch now.

Iskra Ristovska, Principal Product Owner at Damilah

When it comes to thinking about new products, we often wish for more time to conduct interviews, analyze data, and explore competitors. But these tasks take up a lot of time. That’s where ChatGPT comes in handy—it can save us time and make our processes more efficient.

Let’s dive into the world of product discovery by tapping into ChatGPT’s vast pool of data and capabilities. This guide will walk you through how to use ChatGPT for a more efficient product discovery process, helping you prepare for the development of innovative products.

• 1. Start your discovery by focusing on either your business goal or the problems you’ve noticed for a specific group of people

Prompt you can use: “I have a business objective and I’m looking to identify potential customer needs aligned with my goal. My objective is [specify your goal]. Could you please investigate the potential pain points experienced by this specific target group.”

• 2. Understanding the problem or the unsatisfied need

Prompt you can use: “Can you please act as a Product Manager and explore the actual problem underneath these pain points? Please use the ‘5 Why’s’ method to find out why this problem is happening. Once you figure out the main reason, summarize your findings in a clear problem statement.”

• 3. Defining Jobs-to-be-done (JBTD)

Prompt you can use: “Could you please identify the top 5 crucial tasks (JBTD) that my target customers need to accomplish when dealing with the problem? I aim to gain a better understanding of the key activities customers will undertake, when facing the problem.”

• 4. Defining User Personas

Prompt you can use: “Could you please outline the three user personas who are likely to be my most frequent customers?”

• 5. Preparing empathy map for our target customers

Prompt you can use: “Would you be so kind as to create an empathy map for my target customers? I’m eager to gain a deeper understanding of their thoughts, feelings, what they see and hear, and what they say and do.”

• 6. Going through an Ideation process

Prompt you can use: “Picture yourself in a room with your team, including Software Engineers, UX/UI Designers, QA Testers, a Product Owner, and a Scrum Master. During an ideation process, what would be the three standout ideas as potential product solutions for addressing this customer’s problem?”

• 7. Defining a value proposition for our product

Prompt you can use: “Can you please define our value proposition for the first product solution? Describe how it meets customer needs, its unique benefits, and what makes it better than similar products on the market.”

• 8. Doing a market research and competitive analyses

Prompt you can use: “Can you explore other companies in the market selling similar products? Summarize their strengths and weaknesses and identify areas where we can gain a competitive advantage.”

• 9. Defining functionalities

Prompt you can use: “Please outline the key features for the product solution. Consider functionality, user experience, and any unique aspects that will set our product apart in the market. “

• 10. Defining the MVP

Prompt you can use: “Can you now please define the features and functionalities that are essential for our Minimum Viable Product (MVP)? Focus on the core elements that will allow us to deliver value to our users quickly and efficiently.”

To show that these prompts truly make a difference, we put them to the test in a real-life example. In this case study below, you can follow how we turned an idea into a possible product solution, with a clear explanation of our thought process.

At the end we’ll say that ChatGPT can be a really valuable asset in product discovery, helping us save significant time. However, it’s important to remember that while ChatGPT and AI, in general, can be incredibly helpful, they don’t guarantee the automatic success of our products. Success still relies on thoughtful execution and a comprehensive approach beyond the capabilities of AI alone.

Olgica Strezoska, Principal Product Owner at Damilah

To avoid building products that nobody wants, extensive Product Discovery is essential before embarking on the actual development process.

What is Product Discovery?

Product discovery is a dynamic and iterative process that lays the foundation for successful product development. It is the process that helps product teams understand the real problems and needs that people have and then figure out the best ways to solve them before starting development.

The concept of product discovery originated during the 1990s. During that era, companies allocated substantial portions of their marketing budgets to persuade customers of their product’s necessity. Unfortunately, this led to a lot of very expensive failures, as products often made it to full release before companies realized that people just didn’t need or want them.

The main goal of product discovery is not to ship features but rather to promote a continuous environment of learning that will help improve the product incrementally and consistently.

Product discovery is all about de-risking. As Marty Cagan puts it in his book “Inspired”, the purpose of product discovery is to address these four critical risks:

1. Value risk – Will the customer buy this or choose to use it?
2. Usability risk – Can the user figure out how to use it?
3. Feasibility risk – Can we build it?
4. Business viability risk – Does this solution work for our business? 

The product Discovery process can be divided into these 4 phases:

1. Understanding Users

The first phase is all about user research and building empathy for our users. We must be able to put ourselves in users’ shoes, and that can be achieved only if we truly get to know them, listen to what they are saying, and learn about their habits, desires, and frustrations.

To achieve this understanding, several steps can be taken:

• Conduct Surveys and User Interviews: Engaging with the target audience through surveys and user interviews provides valuable insights into their preferences, pain points, and aspirations. This step helps in identifying genuine problems that need solving.

• Continuously Ask “Why” (The Five Whys method): By consistently questioning the reasoning behind the product, teams can uncover deeper insights and align their solutions more effectively with user needs.

• Observe users and create empathy maps: By synthesizing and visualizing the data from user research and interviews, empathy maps can help us identify key insights. 

2. Defining the Problem

With a better understanding of the users, the next step is to precisely define the problem or need our product intends to solve. That can be done by analyzing the user research data we gathered and finding patterns that emerge from this data (using the Affinity mapping technique). By prioritizing the most important problems for users, the team can decide which user problem they want to focus on and clearly define their hypothesis before jumping into solutions.

3. Ideating and Prioritizing

Once we know what the user problem is, we should continue to the Ideation phase so that we can slowly come to the solution of the problem. This phase consists of gathering different ideas from the team (using brainstorming, mind mapping, or similar techniques) on how we can respond to the users’ problem or need. At the end of this phase, we have to prioritize the idea for which we are most confident it will bring value to the users so that we can continue prototyping and testing our hypothesis.

4. Prototyping and Validation

The final stage is about building a quick prototype to be tested and validated to gain confidence that the right product is going to be moved into the product delivery process. Prototyping ensures that the product solution aligns with the identified customer need and that users can navigate through the potential solution effectively.

Olgica Strezoska, Principal Product Owner at Damilah