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.

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.

For the engineers, the task was:

• 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

And for the automation QAs:

• Write three automated functional test scripts for specified scenarios
• Test these functions using the provided test web shop application

The tools used by the AI-assisted team were:

• 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

More than anything, companies should be considering how they can achieve a better balance in their workforce. UK-based businesses will always need to have employees in this country, especially those who are in customer-facing roles. However, when it comes to other positions, such as software engineers, it’s even more important to take a carefully considered view on where to base them.

Near-shoring – or, partner-shoring, where a client and near-shore partner work closely together as a single team to achieve common goals – is a solution that can not only help to keep costs down, but also reduce your risks and increase flexibility when it comes to resourcing.

It’s not just about the money…

One of the most obvious and immediate benefits of partner-shoring is financial, which can be a huge upside, especially when a lot of businesses are just beginning to emerge from some challenging years.

What’s more, partner-shoring can offer a greater degree of certainty, where your labour costs are fully known. Any company that has just hired a lot of UK-based employees will now be reeling from the shock of the Budget, as few anticipated the NI hike, or for it to be quite so extreme. And, of course, there’s no certainty that the costs to employers will stop there – there may well be further increases in taxation to come.

By partner-shoring, it’s possible to agree costs in advance and only pay for the work that is produced for you – so, for example, not having to cover people’s sick leave or parental leave which can often be an unknown quantity.

Having clarity on costs in this way makes it a lot easier for companies to plan ahead, particularly in these uncertain times when a wide range of external factors beyond mere NI rises – such as volatile market conditions – can create significant challenges for businesses.

Added to this is the enormous benefit of flexibility. The ability to scale up and down relatively painlessly is a valuable attribute for many businesses, and having a near-shore partner allows you to do this seamlessly, while the responsibility of employee protections remain the concern of the business you’re partnering with.

Acting responsibly for maximum value

That said, it’s important to emphasise that outsourcing some of your company’s labour requirements shouldn’t mean acting irresponsibly or turning to an unethical supplier that exploits its workforce.

In the outsourcing sector, there are undoubtedly bad actors in relatively deregulated markets who will underpay their employees and offer them few, if any, benefits in order to keep costs as low as possible.

In my experience, there is no value in this for anyone involved. Companies that do not invest in their workforce will earn very little loyalty from their employees. This can lead to not only lower productivity and poor outputs, but also high attrition rates that result in large amounts of key knowledge being lost on a regular basis.

On the other hand, near-shore firms that invest in their people and constantly work hard to ensure they have good employee experiences will always deliver fewer risks and greater benefits – and therefore greater value – to their clients. And that’s exactly what we believe in, here at Damilah. We invest heavily in training to ensure we enable our people to reach their full potential, as well as offering an attractive benefits package. By doing this, our employees pay us back many, many times – and our clients also reap the rewards of this.

In particular, we are proud that our annual staff turnover rate is just 2%. This saves on the expense of recruitment and onboarding (thus helping to keep costs lower for our clients), ensures that essential knowledge and experience remains within the company and is there for our clients’ benefit, and increases the quality, consistency and speed of our outputs.

Look before you leap

One final point to make is that, although the severity of the measures regarding NI may have come as a shock to many, you should avoid making a knee-jerk reaction.

It may be tempting, for example, to immediately cut your UK workforce or instantly put a freeze on recruitment, and transfer as many roles as possible overseas. While this may ultimately be the wisest course of action for certain businesses, it’s important to take a considered approach to finding the optimum balance in your workforce, rather than immediately jumping to the first solution that springs to mind. It’s also worth taking the time to find the right partner to work with.

Here, the benefit of experience and expertise can really pay dividends and help you to deliver the value for money and flexibility your company requires.

At Damilah, we have decades of experience that we are happy to share, as well as a team of highly skilled and motivated near-shore employees who partner effectively with our clients to deliver outstanding outcomes.

To find out more about how you can minimise the impacts of the Budget on your business, get in touch now to discuss our range of services.

Iain Bishop, founder and CEO, Damilah

But these aren’t just heads. These are people – and because you’re a good manager, you care about them. You’ll have spent time helping them develop their careers, improving their technical and other skills; and you’ll have got to know them as individuals and maybe socialised with them, their partners and families.

When the scale-down happens, in the first instance you may need to put a lot of roles at risk, creating widespread uncertainty as people worry about their livelihoods. Then you may have to lose some outstanding colleagues whom you’ve nurtured over the years and may count as friends.

It’s really, really hard.

What’s more, you may get away with this once by explaining it’s a one-off situation that won’t happen again. But what if it’s not? Rarely will you be able to go through a similar process again without risking the best of your team quitting and leaving behind a demoralised group with whom you’ve destroyed all trust and the great working relationships you once fostered.

I’ve been there myself several times. So, over the years, I’ve developed a highly effective strategy for avoiding this kind of problem, that allows you to scale up and down quickly and painlessly. It involves a three-pronged resourcing strategy: your own on-shore team, a partner near-shore team, and your own near-shore team.

Rather like the way in which we all use cloud-based services to expand and contract the tech resources we need as and when required – and only pay for what we use – this strategy enables you to grow and reduce your team in a flexible way while optimising costs.

The difference, of course, is that we’re talking about people here – humans who have feelings, livelihoods and dependents – meaning the stakes are so much higher than when we’re dealing with tin and wire.

So, here’s my advice on how to make this strategy work to everyone’s advantage.

1. Establish your core on-shore team

This is the obvious place to start. This team will tend to be comprised of people who may have been with your organisation for some time. They are likely to have deep domain knowledge and a lot of the technical skills and understanding required to build and maintain your products.

2. Work with a partner to integrate a near-shore team

Here at Damilah, we call this ‘partner-shoring’. This means leveraging the cost benefits of near-shoring by partnering with a firm that can provide a high-quality team capable of collaborating with your own in a seamless fashion.

The costs won’t be as low as with your own near-shore team (see below), as the partner firm will need to take a margin. But the advantages of using a third-party to help you navigate the challenges of near-shoring are many.

First and foremost, it allows you to scale up rapidly with people who are known entities and ready to go from day one. And you don’t need to worry about aspects like recruitment, HR, career development and so on, as the partner firm will deal with those. You’ll just need to manage the projects.

It also gives you the flexible bandwidth to handle the peaks and troughs of workloads in a cost-effective way, as you can bring people on- and off-line as required. As well as giving you the mechanism to scale up quickly, it protects you from the risk of having to scale down in the future, as this is the first team you can cut, and with the least amount of pain. In general, their jobs are more likely to be safe as they can be deployed on other accounts within their company as it seeks new clients.

Additionally, it enables you to build an understanding of the different culture, legal frameworks and myriad other challenges of running a team in an off-shore location – which leads onto the final step…

3. Build your own near-shore team

This may be less expensive than working with a near-shore partner, but it’s far harder to do.

The big advantage, beyond cost, is that it can give you access to the kinds of talented people that are more challenging to find in your own country. For example, it may be easier to recruit a team that is younger, with a better gender balance, and with technical skills that are harder to hire back home.

This kind of team can add a huge dose of energy and enthusiasm to your home team – and a smart blend of mature, on-shore experience with youthful, off-shore passion and determination can be powerful.

Building up a strong near-shore capability can be time-consuming – think nine to 12 months minimum to become established. But once you get there, it can add a lot of value to your business.

Making it work

The key to success is to ensure all three teams blend effectively. And the way to do that is to foster a culture of transparency and collaboration, where autonomous teams with the same goals and mindset will work closely together to deliver outstanding outcomes aligned to your business requirements.

It’s therefore important not only to look for compatibilities in ways of working, but also in ways of problem-solving.

Face-to-face contact is always helpful here – both in the office and outside. If two people have shared a beer in the past, when there’s a challenge it’s always easier for them to pick up the phone and thrash it out than if they’ve only ever communicated by email or Slack.

Put simply, your near-shore teams should feel like extensions of your home team.

We can help you learn more about the best ways to plan, implement and maximise value from this three-team strategy. To find out more, get in touch now.

And the following articles in this series will help you build a greater understanding of the challenges of near-shoring and how to create maximum value by doing it:

• The future of near-shoring: ‘partner-shoring’

• The challenges of setting up a near-shore team

• How to select the right near-shore partner

• How ‘partner-shoring’ can support a scale-up

Iain Bishop, founder and CEO, Damilah

But how can you tell one potential partner from another? Here are some key questions to ask during the selection process:

How do you know you can trust them?

Nothing matters more than this. If you have any doubts whatsoever, walk away. A good first step towards finding a reliable partner is to seek recommendations, demand references, and ask their customers questions like: What are they like to work with? Have they delivered the right outcomes for you? How have they dealt with problems? How collaborative are they?

How good are their technical skills really?

Every potential partner will tell you that they have amazing technical skills. Really, really talented people. The best in the industry. But how do you know how good they are in reality? It’s important to talk to the technical leadership to see if they genuinely understand your technology and what good looks like. It can also be worth interviewing the people who’d be working in your team. 

Do they understand how to build secure systems?

Today, all systems need to be secure – and by using tooling as part of the build process, teams can ensure security is embedded throughout the development process. So, are all the people in your partner’s team security aware? And are they given appropriate training on the subject?

Are they focused on delivering value?

Too many near-shore teams will prioritise billable hours over delivering genuine value to their clients. However, for both you and your potential partner, value – rather than actual cost – should be top of mind. In particular, going with the cheapest option might not always result in the best value for your organisation. If your partner doesn’t have the right technical or collaborative skills, for example, poor outputs and delays could end up costing you dear in the long-run.

Is there a commercial awareness right across the business?

The advent of the public cloud has introduced a completely novel dimension to designing new systems. Architectures must be cost-effective in their use of cloud services, which means those designing them must have a current knowledge not only of the services available, but also the pricing implications which may depend on usage patterns.

Are they in a suitable near-shore location?

Here, you’ll need to consider issues like cultural alignment and time zone differences. It’s really hard to make agile development processes work well if your teams’ working days barely overlap or your potential partner doesn’t have an ethos of close collaboration.

Do they have good English language skills that run deep in the business?

Check that everyone speaks good enough English to enable peer-to-peer contact between your team and theirs. If all communication has to go through a project manager, as they’re the only one who speaks your language, misunderstandings and delays will become inevitable.

Do their people collaborate easily?

Having a naturally collaborative culture will make communication at all levels much more straightforward. Everyone needs to be able to communicate easily and transparently so that issues and opportunities can be discussed openly.

The best way to find answers to all the questions above, and reassure yourself that you’re selecting the right partner, is to spend time with them. Find out if they’re happy for you to visit their offices as often as you wish. Insist on meeting the people you’ll be working with – and not just the senior management and sales team – and ask them some tough questions. The more time you spend doing this, the more you’ll find out what the business is really like to work with.

Finally, once you’ve selected your potential partner, or drawn up a shortlist, it’s often a good idea to put them to the test before committing to a contract. For example, you could give them a problem to solve – and even if it’s not a live issue, the way they go about finding a solution can be very revealing as to the way they operate.

Alternatively, you could engage them on a small, live project and measure them on how they handle it. This is a very low-risk way of assessing whether the potential partner is likely to be a good fit for your own organisation. Here at Damilah, we’d be more than happy to discuss any of the above questions with you and explain our ‘partner-shoring’ model to you – where our near-shore team will collaborate seamlessly with you to deliver high levels of shared value.

To find out more, get in touch now to discuss our range of services.

Iain Bishop, founder and CEO, Damilah

Here’s how you can make it work:

The scale-up challenge

Often private equity-backed owners will be looking to invest in the product to significantly grow revenue. Typically, this may involve transforming a monolithic on-premise software product to a modern SaaS offering, or extending the product into other markets.

Whatever the plan may be, it’s likely to create a need to rapidly expand the engineering team. This could be in terms of head count, and potentially also to bring in new expertise if the scale-up involves a significant technology shift and the necessary know-how doesn’t already exist within the business.

Going outside the business to solve the problem

A highly effective way to solve this problem is to bring in expertise from outside, as hiring and building a team internally can be time-consuming, costly and risky.

That’s why turning to a near-shore partner, with flexible teams and talented people who have done this many times before, can reap big rewards. They will be able to offer, for example, experience and understanding of how to use public cloud in a cost-effective way, how to build cloud-native SaaS products, and so on – while maximising the benefits of modern standards and approaches, such as automation, continuous testing and rapid delivery cycles. This can be a very cost-effective way of putting an outstanding final product into users’ hands as quickly as possible.

An added advantage can be the way in which you can easily scale the development team up or down. For example, a private investor may often begin to reduce their levels of investment after year two or three as they look to grow their bottom line prior to exiting after five years or so. In turn, that may require a scaling back of the engineering team, which is harder to do if it involves laying off internal employees.

Setting up autonomous teams

None of this is likely to work well unless you create autonomous teams that remove as many dependencies as possible – as dependencies are the biggest productivity killer when it comes to software development. If completing a task depends on input from another team or individual, working at pace frequently becomes impossible – which is exactly the opposite of what a major scale-up needs.

Instead, an autonomous team will have all the skills and experience necessary to forge ahead successfully and at pace. They will be able to handle everything from talking to end users about their needs, and creating a roadmap and product strategy, right through to production and deployment, with all the necessary DevOps, engineering, testing, and product people in between that are required to get the job done.

The Spotify model

In my opinion, autonomous teams are the only way to scale rapidly – and using the Spotify model is an example of a good way to build and maintain such teams.

At the top, there is a tribe leader, who may be responsible for multiple teams, each one containing all the skills necessary to complete a project. The tribe leader is empowered to make decisions and move people around their teams as necessary.

Then, across those teams, there are special-interest groups called guilds. These are collections of people who have the same areas of expertise, and they are used for sharing knowledge and advice across teams. Each guild will have a leader, who is a specialist in their field, but may not be from the management team.

And there are also chapters, which are a way of managing and mentoring individuals, without the need for multiple layers of line management.

Overall, this model is an effective way of running large teams, because the CTO can focus on supporting the tribe leaders and then only managing by exception – in other words, not having to focus too much on details unless there is a specific problem, in which case they can go in deep to help find a solution.

Leveraging the benefits of near-shoring

The crucial factor to bear in mind throughout all of the above is that it is not necessary for an team to be physically co-located. As long as its members have the necessary autonomy and the ability to collaborate seamlessly, they can be anywhere. This allows you to leverage the cost and flexibility advantages of partnering closely with a near-shore company – or ‘partner-shoring’, as we like to call it.

Over the years, I’ve helped many businesses to rapidly scale up and significantly increase their value by deploying these methodologies.

To find out more about how we at Damilah can help you to do the same, get in touch now to discuss our range of services.

Iain Bishop, founder and CEO, Damilah