For most navigation apps, the shortest or fastest route is the default choice. Romanian startup Defensive Pedal is taking a different approach: helping cyclists reach their destination using the safest route instead.

Among the startups showcased in the Techsylvania Startup Alley, Defensive Pedal has developed a navigation app that analyzes road infrastructure, traffic conditions, weather, and dozens of other variables to recommend routes that minimize cycling risk rather than travel time.

“We’re essentially building Google Maps for safe cycling,” said Victor Rotariu, founder of Defensive Pedal, during an interview with Startups&TheCity.

A risk algorithm built on European cycling data

At the core of Defensive Pedal is a proprietary risk-scoring algorithm developed using European cycling accident data and extensive rider feedback.

According to Rotariu, the first versions of the model were validated with input from over 2,400 cyclists, while the current algorithm evaluates approximately 80 different factors that influence cycling safety.

Most of these variables relate to infrastructure, including:

• road type;
• roundabouts;
• tunnels;
• parks;
• road curvature;
• traffic models;
• weather conditions.

Rather than relying solely on mapping data, the platform combines multiple public datasets with its own analytical model to generate a safety score for every road segment.

According to the team, routes generated by Defensive Pedal are currently 2.1 times safer than the default cycling routes in Romania. The claim is based on an internal validation in which the startup compared 10,149 routes generated by the Defensive Pedal algorithm with Google Maps routes between the same randomly selected origin and destination pairs across Romania. The safety assessment used the company’s proprietary risk model, which incorporates cyclist risk factors identified in official European research on the causes of cycling accidents. 

AI continuously improves route recommendations

Although the core risk model is deterministic, Defensive Pedal adds an AI layer that continuously refines its recommendations.

As cyclists use the app, they can report unsafe locations, while anonymous usage data helps the system understand whether riders follow the suggested routes or choose alternatives. This feedback loop enables the algorithm to identify potentially dangerous areas that may not be evident from public data alone.

“The AI helps us continuously improve the quality of the safety ratings,” Rotariu explained.

Already live in Romania

The Android version of Defensive Pedal has been available for about two weeks and currently covers the entire Romanian road network. The iOS version also launched last week.

The startup has intentionally kept marketing efforts limited during the initial rollout, focusing instead on validating the product and collecting user feedback.

Even so, the platform has already attracted a few hundred active users from word of mouth alone, while approximately 900 cyclists have signed up for early access and are expected to join.

Helping cities improve cycling infrastructure

The technology is designed not only for individual cyclists but also for urban planners.

Defensive Pedal is collaborating with the World Bank on cycling consultancy projects, using its risk algorithm to identify dangerous streets and intersections at a highly detailed level.

Rather than simply highlighting that one city is safer than another, the platform can pinpoint exactly where infrastructure improvements would have the greatest impact on cyclist safety.

Rotariu said the company can already calculate safety scores for individual cities and plans to publish a city ranking covering most Romanian and European cities in the near future.

Expanding across Europe

While Romania serves as the startup’s launch market, the ambition is significantly broader.

The team plans to expand across the European Union within this year. Since much of the required infrastructure and traffic data is publicly available, adapting the platform to new countries is technically straightforward, although each market requires local validation, cyclist feedback, and adjustments to national traffic regulations.

A mixed business model

Defensive Pedal plans to monetize the platform through several complementary revenue streams.

The navigation app will remain free for basic users, while premium features are planned under a subscription model. Beyond consumer subscriptions, the company also sees commercial value in aggregated cycling intelligence.

Anonymous mobility insights can help outdoor brands, bicycle retailers, and local authorities better understand where cyclists travel and how infrastructure influences riding behavior, while municipalities can use the data to prioritize investments that improve cycling safety.

As cycling continues to gain momentum across European cities, Defensive Pedal is betting that safer navigation—and better data—can become essential infrastructure for both riders and the organizations building more bike-friendly urban environments.

The post Defensive Pedal wants to make cycling safer with AI-powered navigation, starting from Romania appeared first on Startups&TheCity.

Artificial intelligence dominated the agenda at the fifth edition of UNCHAIN Festival, which brought together more than 1,000 financial and technology leaders from over 40 countries in Oradea. Across two days, 170 international speakers debated topics ranging from agentic AI and digital identity to stablecoins, the Digital Euro, fraud prevention, and the future of banking.

But after speaking with executives from Visa, Finshape, Creatio, Tremend, AMICredit, and disruptive strategy training & consultant Andrew Taylor, along with other companies building financial technology across Central and Eastern Europe, one thing became clear: the most interesting conversations were no longer about AI itself.

No one questioned whether artificial intelligence would reshape financial services. That debate appears to be over.

Instead, discussions focused on a different set of questions. How do banks integrate AI into decades-old core systems? How do they ensure compliance with increasingly complex regulations? How can organizations deploy AI without compromising trust, security, or customer experience? And perhaps most importantly, what happens to employees as intelligent agents begin taking over an increasing share of routine work?

Although each interview explored a different corner of the financial ecosystem—from payments and lending to insurance, CRM platforms and digital strategy—the same themes surfaced repeatedly. Together, they paint a picture of an industry moving beyond experimentation and into a far more demanding phase: implementation.

AI is no longer the question. Implementation is.

Perhaps the strongest common thread across all eleven interviews was how much the conversation around AI has matured.

Only a year or two ago, discussions at financial conferences often revolved around whether banks should embrace generative AI at all. At UNCHAIN Festival 2026, that question had effectively disappeared. Executives no longer debated the technology’s potential. Instead, they discussed deployment models, governance frameworks, customer trust, integration with legacy infrastructure, and measurable business outcomes.

Catalin Cretu, General Manager of Visa for Romania, Bulgaria, Croatia and Slovenia, pointed to changing consumer behaviour as evidence that the market has already moved forward.

“More than 50% of online users now use AI tools to search for products and compare prices,” he explained.

For Visa, that behavioural shift is driving the development of what the company calls agentic commerce, AI-powered assistants capable of searching for products, comparing offers, and eventually completing purchases on behalf of consumers. Rather than replacing existing payment infrastructure, these AI agents rely on trusted payment networks to authenticate users, prevent fraud, and execute transactions securely. Pilot projects are already underway, with broader deployment expected over the coming year.

The discussion at Finshape reflected a similar shift, but from the perspective of traditional banking.

“Banks are no longer asking whether AI works,” said Dan Toderici, Country Manager Romania at Finshape. “They’re asking how to restructure their systems so AI can actually function.”

That distinction is significant. The competitive advantage is no longer simply adopting AI. It lies in building the organisational and technological foundations that allow AI to deliver value consistently and securely.

Anton Solodkyi, Customer Success Director at Creatio, observed the same evolution while working with banks deploying AI-driven CRM and loan origination solutions. The technical capabilities already exist, he explained. The challenge is integrating them into highly regulated environments without disrupting existing operations.

Taken together, these conversations suggest that the financial sector has entered a new phase of AI adoption. The technology itself is no longer the limiting factor. Success increasingly depends on execution—how institutions redesign processes, prepare data, train employees, and establish governance models that allow AI to operate safely at scale.

In other words, the competitive question has shifted from “Should we use AI?” to “Can our organisation actually support it?”. That challenge extends beyond technology. As Carlos Parker, Head of Arxia’s AI Acceleration Program, recently told Startups&TheCity, the future of AI inside companies may depend less on the models themselves and more on leadership, organizational culture, and change management. Through the AI Acceleration Program, Arxia focuses on helping organizations bridge exactly that gap—moving from AI experimentation to AI embedded in everyday business operations.

Legacy systems—not AI—have become the industry’s biggest bottleneck

If there was one challenge mentioned more often than any other during the interviews, it wasn’t artificial intelligence itself. It was everything that came before it.

For decades, banks and insurance companies have built technology stacks by adding new systems on top of existing ones. The result is a complex web of core banking platforms, customer databases, CRM systems and internal applications that were never designed to support AI-driven decision making.

Several interviewees described this legacy infrastructure as the single greatest obstacle to AI adoption.

Marius Scarlat, Head of Product Management CEE at Tremend, sees this challenge every day while helping banks modernize their digital services. Although AI has already been successfully deployed in projects such as customer onboarding and relationship management for major European financial institutions, including Lloyds Bank, implementation is rarely straightforward.

“Banking is one of the most regulated and traditional industries,” Scarlat explained. “Technology alone isn’t enough. You have to earn trust, understand existing processes and modernize them carefully.”

That gradual approach reflects the reality of enterprise banking. Replacing core systems is rarely an option, so most institutions are looking for ways to introduce AI without disrupting the infrastructure they already depend on.

Dan Toderici described a similar situation from Finshape’s perspective. Rather than replacing legacy platforms, the company builds a middleware layer that sits between existing banking systems and modern AI-powered applications. Its role is to organize, clean and contextualize fragmented customer data before it reaches AI models.

Without that intermediate layer, even the most advanced AI solutions struggle to produce reliable results.

Anton Solodkyi, Customer Success Director at Creatio, believes this explains why many AI initiatives fail to move beyond the proof-of-concept stage.

“Organizations often focus on the AI itself,” he said. “But the bigger challenge is understanding and documenting the business process you’re trying to automate. If the process isn’t clearly defined, an AI agent won’t fix it.”

His observation highlights a recurring misconception surrounding AI adoption. Automation doesn’t compensate for operational complexity—it amplifies it. Organizations with fragmented processes simply expose those weaknesses faster when AI enters the equation.

The same challenge extends beyond banking.

Sándor Székely, who develops embedded insurance solutions in Hungary, said that launching new AI-powered insurance products is rarely limited by the technology itself. Instead, the biggest delays occur inside insurance companies that must integrate new digital products into legacy policy administration systems built years—or even decades—ago.

The technology is ready.

The infrastructure often isn’t.

Across banking, insurance and enterprise software, the pattern was remarkably consistent: AI is exposing technical debt that many financial institutions have accumulated for years. Before organizations can become AI-first, they first need to modernize the digital foundations on which AI depends.

In the AI era, clean data has become a competitive advantage

Closely connected to legacy infrastructure is another theme that surfaced repeatedly throughout the interviews: data quality.

Every executive agreed that AI is only as good as the information it receives.

That may sound obvious, but in financial services it represents one of the industry’s biggest transformation challenges.

Banks typically operate dozens of disconnected systems built over many years. Customer information is scattered across different databases, product lines and business units, making it difficult to create the unified view that modern AI applications require.

“Banks are investing heavily in AI,” Dan Toderici explained, “but before AI can generate meaningful insights, the data has to be structured, cleaned and connected.”

Finshape addresses this challenge through a customer data platform that creates a unified customer profile across multiple banking systems. Once that foundation exists, AI can deliver personalized financial recommendations, targeted marketing campaigns and intelligent customer interactions.

One of the company’s flagship examples is its collaboration with Raiffeisen Bank Romania. What began as a digital loyalty application has evolved into an AI-powered personalization platform with more than one million active users. The system analyzes customer behavior and transaction history to deliver highly personalized offers inside the bank’s mobile application—demonstrating that AI creates value only after the underlying data has been properly organized.

Creatio faces similar challenges from the CRM side.

According to Anton Solodkyi, many organizations underestimate how much preparation is required before AI agents can automate business workflows.

“Structured information is the foundation,” he explained. “Without it, even the smartest AI agent has nothing reliable to work with.”

Tremend has reached the same conclusion internally. Beyond client projects, the company is increasingly using AI to accelerate software development and modernize legacy code through proprietary tools such as Backlog AI and Slingshot. Even there, however, human review remains essential to validate AI-generated outputs before they become production-ready.

Taken together, these conversations suggest that data has become the new competitive advantage in financial services.

The race is no longer about who has access to the latest language model. Those models are becoming widely available.

The real differentiator is who has built the cleanest data architecture—and therefore who can generate the most reliable, secure and context-aware AI experiences.

AI is changing work, not eliminating it

One of the clearest divergences between public perception and real-world deployment of AI in financial services is what actually happens inside organizations once automation is introduced.

The expectation is often substitution. The reality, across multiple interviews, is reconfiguration.

At AMICredit, the introduction of AI agents fundamentally changed how work is distributed inside the company—but not in the way many might expect. Rather than reducing headcount, the organization expanded.

Karoly Totos, CEO of AMICredit, described a system where AI handles repetitive, structured tasks such as onboarding and customer communication, while human employees shift toward higher-value operations.

“We’re hiring more people, not fewer,” he explained. “AI agents handle repetitive tasks, making the whole organization more productive.”

The impact is measurable. Onboarding workflows that previously consumed up to 40% of employee time have been largely automated. IT costs have been reduced by nearly half, while customer retention in AI-assisted flows has increased by 15–20%.

But the deeper change is not operational efficiency. It is the redefinition of what human work inside financial organizations actually is.

A similar shift is visible in broader discussions about the labor structure of financial services.

Visa’s perspective on agentic commerce highlights a future where AI systems increasingly handle discovery and comparison tasks on behalf of users. Andrew Taylor, who studies strategic disruption, frames this shift in broader terms: routine cognitive tasks are gradually moving toward machine execution, while humans transition toward supervision, validation, and system-level decision-making.

Across all interviews, a consistent pattern emerges: AI is not removing humans from the system—it is moving them up a layer in the decision hierarchy.

Human oversight remains a structural requirement, not a temporary phase

Despite rapid advances in automation, no interview suggested a future in which financial systems operate without human intervention.

Instead, a consistent governance model appears across banking, insurance, CRM systems, and payment infrastructure: AI proposes, humans decide.

At Creatio, this principle is embedded directly into CRM and workflow automation systems. AI agents assist in loan origination and customer service processes, but final decisions remain with human operators.

Anton Solodkyi described this as a design constraint rather than a limitation.

“AI generates recommendations, but humans make the final decision,” he said.

The reason is not technological weakness but regulatory and operational reality. In highly regulated industries such as banking, full autonomy introduces unacceptable levels of risk—both in terms of compliance and reputational exposure.

Tremend’s experience reinforces this view. Even in advanced implementations such as AI-driven onboarding and relationship management systems used by major European banks, human validation remains a core component of the workflow.

Visa’s experiments with AI-driven commerce also depend on human confirmation before transactions are executed, particularly in early deployment phases.

Across all cases, the same architectural principle appears: AI systems are being designed as decision-support layers, not decision-making authorities.

Regulation is no longer a barrier—it is a design constraint

If earlier phases of fintech innovation often framed regulation as friction, the interviews suggest a more mature relationship has emerged between AI systems and regulatory frameworks.

Rather than slowing adoption, regulation is increasingly shaping how AI systems are built from the ground up.

In insurance, compliance requirements were a central factor in deployment timelines. Sándor Székely noted that achieving regulatory approval for AI-driven insurance products required extensive validation processes, including data privacy assessments and alignment with national authorities.

Even when the underlying technology is ready, institutional approval cycles can extend implementation timelines to several months.

Similarly, Finshape’s work with banks across Central and Eastern Europe shows that GDPR compliance and data governance are no longer treated as external constraints but as embedded design parameters in system architecture.

Narcis Nagy highlighted a related dynamic: organizations are under pressure to demonstrate AI capability quickly, but must still align with strict European regulatory frameworks such as the EU AI Act.

This creates a structural tension between speed and compliance—but also a clear directional shift. Instead of resisting regulation, companies are increasingly designing systems that assume it from the start.

The result is a quieter but significant transformation: regulation is becoming part of the AI architecture itself.

The pressure to adopt AI is driven by competition, not strategy

While much of the discussion around AI adoption focuses on technological readiness, one of the more subtle insights from UNCHAIN 2026 is that the primary driver of adoption is not internal transformation agendas.

It is external pressure.

Organizations are not adopting AI because they have fully mapped out long-term strategies. They are adopting it because competitors already have.

Narcis Nagy, General Manager & Cofondator at RELOAD CONCEPT described this dynamic directly, noting that the pressure to implement AI often comes from the market rather than internal leadership teams.

“The pressure comes from competition,” he explained. “Not from within the company itself.”

This creates a paradox. Companies accelerate AI initiatives not necessarily because their systems are ready, but because standing still is no longer an option.

Andrew Taylor’s strategic framing adds another layer to this observation. In environments where technological change is accelerating, organizations increasingly shift from predictive planning to adaptive response. Strategy becomes less about forecasting outcomes and more about reacting to systemic shifts initiated elsewhere in the ecosystem.

Across both perspectives, the implication is clear: AI adoption is becoming less of a strategic choice and more of a competitive necessity.

Conclusion: finance is being rebuilt, not disrupted

Across the eight interviews spanning payments, banking infrastructure, insurance, CRM systems, and strategic consulting, a coherent picture emerges.

Financial services are not undergoing simple technological disruption. They are undergoing structural reconstruction.

Artificial intelligence is already embedded across multiple layers of the financial system. But its integration is constrained by legacy infrastructure, fragmented data, regulatory frameworks, and institutional trust.

As a result, the dominant model emerging across the industry is not autonomous AI-driven finance, but supervised intelligence operating within tightly governed systems.

Visa’s agentic commerce, AMICredit’s AI-driven onboarding, Finshape’s data infrastructure, Tremend’s enterprise deployments, and Creatio’s CRM agents all point toward the same reality: AI is becoming embedded, but not independent.

And perhaps the most important insight from UNCHAIN 2026 is this:

The defining challenge of the next phase of financial transformation is not building more capable AI systems.

It is building financial institutions capable of absorbing them.

Read also: Riding the AI Wave: Why the Real Challenge Is Not Technology, but Change, a related interview on how organizations can navigate the shift from AI experimentation to execution.

The post Beyond the AI Hype: What Fintech Leaders Revealed About the Future of Financial Services at UNCHAIN Festival 2026 appeared first on Startups&TheCity.

AI is no longer a futuristic concept—it is a global tsunami hitting every industry from the United States to Chile, Uganda, and Romania. Yet, while executives rush to adopt AI just to keep up with the competition, the real battle is being fought on the old battlegrounds of digital transformation: people first, processes second, technology third.

This contrast between market rush and operational reality was the catalyst for a recent workshop I’ve attended, led by Carlos Parker, head of Arxia’s AI Acceleration Program, in Cluj-Napoca, Romania. Working with a local consultancy firm, the discussion proved that while AI models are evolving faster than ever, the real art lies in learning how to surf the wave rather than fighting it.

Carlos, who has delivered workshops across Europe, Latin America and Africa, says the conversation around AI has become universal. From Romania and Ukraine to Chile and Uganda, businesses everywhere are talking about artificial intelligence. Yet, according to him, the technological challenge itself is not the hardest part. 

“The real issue is not the technology itself,” he explains. “It’s about operationalizing it. How do companies integrate AI into their workflows? How do they measure return on investment? How do they help employees adapt?”

In his view, AI is not fundamentally changing the classic rules of digital transformation. The same principles still apply: people first, processes second, technology third.

AI Is an Accelerator, Not the Core

One of the strongest ideas emerging from my discussion with Carlos was that AI should not be treated as a goal in itself.

“The core has always been process optimization,” Carlos says.“You spend far more time defining workflows, defining policies, and managing organizational change than teaching people not just how to prompt, but also how to integrate and orchestrate AI tools effectively.”

This perspective contrasts sharply with the current market trend, where many organizations rush to “adopt AI” without clearly defining why they need it in the first place.

According to him, many executives are under pressure to demonstrate AI adoption simply because competitors are doing the same. Some companies already demand internal AI KPIs, even when employees are using AI for tasks that provide little practical value.

“The fact that you can use AI doesn’t mean you should,” he argues. “The important question is: does it actually improve efficiency or solve a real problem?”

Surfing the Wave Instead of Fighting It

Asked whether people should fear AI, Carlos used a simple metaphor.

“AI is like a wave,” he says. “You can try to fight it, or you can surf it.”

He believes the acceleration of AI development is unlikely to slow down. Governments, corporations and entire regions now see artificial intelligence as a strategic race tied to competitiveness, economic power and geopolitical influence.

“The United States, China and Europe all understand this is a priority,” he explains. “Once countries start investing resources at this scale, it becomes very difficult to stop.”

Competition itself becomes the engine. If one country or company believes another is advancing faster, everyone feels pressured to continue investing.

Beyond Automation: The Bigger Philosophical Question

The conversation also moved far beyond productivity and automation.

Carlos believes society is underestimating the deeper implications of AI — especially if artificial general intelligence (AGI) eventually becomes reality.

“If AI becomes better than humans across all economic domains, then we enter a completely different scenario,” he says. “And we don’t really have political or economic frameworks prepared for that.”

Unlike previous industrial revolutions, which created new categories of jobs alongside automation, AGI raises a more difficult question: what happens if machines can improve, maintain and optimize themselves?

Modern economic systems — whether capitalist or socialist — are fundamentally built around human labor. If work itself is no longer necessary, entire ideological structures may need to be reconsidered.

“How much meaning in our lives is connected to the work we do?” he asks. “What happens if that disappears?”

Eastern Europe’s Unexpected Advantage

One particularly interesting part of the discussion focused on Eastern Europe and its relationship with technology adoption.

While AI adoption among businesses remains cautious, Carlos sees the region as having a major long-term advantage due to its strong engineering and technical culture.

Countries such as Romania, Ukraine, Estonia and Moldova inherited a deep emphasis on science and engineering education — something he believes still shapes society today.

“In Eastern Europe, engineering and technology are culturally valued,” he says. “You see it in the number of programmers, in technical education, and even in the role of women in engineering compared to many Western countries.”

Romania, in particular, was mentioned as a country that historically adapted quickly to new technologies. From internet infrastructure to software development talent, the region may ultimately prove more flexible than some Western European economies traditionally perceived as more advanced.

At the same time, resistance to change remains universal.

“People adopt technology slowly everywhere,” he notes. “A lot of it is cultural. Change always involves risk.”

AI Adoption Is Ultimately About Trust

One of the workshop’s recurring themes was that companies are often less afraid of AI itself and more afraid of uncertainty.

Managers understand that change is coming, but many do not know where to begin.

That is why, Carlos argues, successful AI implementation should resemble a gradual organizational transformation rather than a simple software rollout.

“You don’t just buy licenses for everyone and expect magic to happen,” he explains. “Successful AI adoption requires a strategic, company-wide transformation process implemented gradually. You start with focused teams or use cases, validate what actually works in practice, measure results, build internal know-how, and then scale adoption step by step across the organization.”

In other words, the future of AI inside companies may depend less on the models themselves and more on leadership, organizational culture and change management.

The technology is already here. The harder question is whether people, businesses and institutions are ready to evolve with it.

And this is precisely the gap that Carlos aims to address through Arxia’s AI Acceleration Program — a framework designed to help companies move beyond experimentation and actually operationalize AI inside their organizations.

The post Riding the AI Wave: Why the Real Challenge Is Not Technology, but Change appeared first on Startups&TheCity.

News about Romanian IT companies operating on the African continent are rare, as companies are lacking knowledge of this complex market and its opportunities. So it came quite as a surprise to find out that last year, the Rwandan government started several projects of IT capacity building with Arxia, a company based in Cluj, Romania. In 2019, Arxia’s CEO, Daniel Homorodean also led an economic mission in Africa, together with several colleagues from Cluj IT Cluster, establishing connections and creating business opportunities with relevant partners in Kenya, Uganda and Rwanda. With the assistance of the Romanian embassy in Nairobi, the delegation was received by ministries, parliamentary commissions and other high level institutions. The Romanian delegation also met local IT companies and tech startups in the quest to create cooperation bridges between the Romanian and African IT industries. Daniel is also involved in defining standards, consulting and implementing systems to support e-government and e-administration processes in Romania.

We spoke with Daniel about the opportunities Africa offers to European IT companies, helping develop the African ecosystems.

IT capacity building in Rwanda

Our engagement with the Rwandan government started in 2018, when I’ve traveled to Kigali on behalf of the TYPO3 Association as part of the project meant for international expansion of TYPO3, a robust and secure open source CMS framework, yet not well known outside its core German-speaking (DACH) market.

The Rwandan national IT agency was quickly convinced of the benefits of TYPO3 and committed to use it to power all their national institutional portals, over 300 of them, from ministries and universities to embassies and city halls. Such an endeavor requires proper know-how and long term effort and this challenge offered us the chance to employ our vision in Africa: sustainable and efficient development happens when a country has acquired the know-how and has the ability to implement and maintain their projects, not being dependent on outside suppliers for solution and service providing. Therefore, our role should be one of enablers, facilitating know-how transfer and the development of their national internal capacity and foster the development of their own national technical community around TYPO3. This was our proposal.

This led starting our first project in Rwanda, aiming to support, mentor and coach a Rwandan team of developers from public institutions and from private software agencies to learn TYPO3 and to develop the institutional web portals. The second project soon followed, expanding our collaboration with a coaching project on software integration architecture, helping Rwanda to develop the software interoperability framework between the institutions of the country. For both projects, currently ongoing, I am glad to have found reliable partners with whom Arxia shares the delivery effort. On the software integration project, we make a team with another company from Cluj, Nordlogic. On the TYPO3 project, we team up with Macopedia from Poland. I believe in this partnership model and in its potential to expand, both in Rwanda and in other countries. I go by the TYPO3 motto, “inspiring people to share”, because there is a lot of opportunity in Africa and we can create much more value if we work together.

10s of millions of African SMEs will need digitalize their business

I have lately given quite a strong focus on developing relations in Africa. If you ask me, “Why Africa?”, there are two reasons for me. One is very personal and this relates to the passion that I have developed for the continent. I have been travelling in African countries for over 12 years. I love the energy, the colors, the rhythms, the openness and the directness of the people. I love Africa. I wanted for a long time to be more than just a tourist there.

Moreover, I see a tremendous business opportunity. By 2035 more than half of all the people who would be able to work in the world will actually be Africans. In the next few years, I am expecting tens of millions of African SMEs to digitalize their business, to start using the internet and software applications. This will generate a huge new market.

Africa is growing in population with more than 2.5% per year, meaning that by 2050 the Africans will double in number. They will reach 2.5 billions. This population is very young. It’s already getting quite well educated. It has a lot of energy and a lot of willingness to work and to progress. This will be the greatest global pool of workforce and talent.

It is clear for now that the African states themselves are not yet in the position of sustaining this tremendous demand of growth through proper digital systems, and that they need help, help that I believe that we can provide, in order to ensure and accelerate their progress. That is why I believe that the opportunity for us right now is great in Africa, and the moment is for us to go there.

IT can help tremendously in the sustainable development of African countries

I consider IT as a support industry for the growth and development of ecosystems. And as I’ve said at a recent presentation that I’ve held at the African Union in Addis Ababa in last November, all the countries, especially the African ones, need to have the ability to manage and control the data, the ability that right now eludes them because they do not have a proper informational infrastructure.

This means the ability to manage the spatial and territory information, the population data and the data about the movement of the people, the information about businesses and their dynamics, about financial transactions and taxation. Everything requires a proper infrastructure, technical infrastructure that allows the management of such information. This is why the digitalization and overall the IT support is essential for sustainability and development of those countries. And I would say that without the proper approach in this direction, the African countries run in the high risk that the tremendous opportunity of growth can transform into a danger.

It is very problematic when the young population willing to work is not able to find the context of opportunity in the country. Proper employment of IT support can help tremendously in this direction. This does not mean delivering off-the-shelf systems, but developing strategies, policies, acquiring capacity to implement and maintain complex applications, and developing tailored solutions that are adapted to their specific goals and needs. This is the approach that we promote and that we are ready to support with all our experience.

The aim is to develop a network of partnerships, which would enable tackling strategic projects

We already have started projects in several countries in Africa. I’ve mentioned the first two of them that we implement in Rwanda. We have advanced discussions with central institutions from Uganda and Ethiopia and good perspectives as well in Kenya.

This spring, from February to April, I will travel to Africa again, in at least four countries. On one hand to expand the relations already established in East Africa, on the other to explore new partnerships on the other side of the continent, in particular in Ghana and in Guinea.

My aim is to develop a network of partnerships, which would enable us to tackle strategic projects in various domains that range from public policy, standardization, implementation of systems, capacity building and training in various technical domains. It is clear that myself, my company, Arxia, or even overall the active group of companies that we’ve brought together in the Cluj IT Cluster requires a larger capacity in order to convert and to expand the high level of opportunity. Everything that I aim to do in Africa is based on a partnership approach.

The great opportunity in Africa also comes with a strong challenge. We face substantial competition, from more developed countries and strong multinationals, that aim to sell their solutions on the continent. They have an efficient political lobby and lots of money to invest. Many times their approach may not be clean nor guided by creating a lasting benefit for their target clients, however we are for now in disadvantage as we do not have the financial means, nor the political support, to compete with them.

Besides the principle-based approach that we promote we also need to foster the development of a support framework. That is why I have launched the initiative to develop a national strategy for the internationalization of the Romanian IT and to promote its implementation as national priority, since IT indeed is one of the most valuable assets on which we can build value as a country. There is much more to come and I would be happy to report back as we progress in creating more and more value in Africa and elsewhere.

To learn more about Daniel’s future plans and the opportunities he explores in Africa, connect with Daniel Homorodean on LinkedIn.

The post Romanian companies get involved in IT capacity building in Africa appeared first on Startups&TheCity.