Innovation-driven business growth: from experiments to organisational impact

In the coming years, competitive advantage will increasingly depend on organisational capability: the ability to sense opportunities, use knowledge and technology, and turn learning into renewal. Companies will still compete through products and technologies, but lasting growth will depend on how systematically they connect innovation with strategy, customer value and implementation. 

Business renewal now takes place in an environment shaped by uncertainty, technological disruption, sustainability pressures and rapidly changing competence needs. Employers expect significant changes in skills by 2030. Analytical thinking, technological literacy, AI and big data, creative thinking, resilience, flexibility and lifelong learning are among the capabilities gaining importance in this transformation (World Economic Forum 2025). 

European competitiveness policy reflects a similar concern. Europe must close the innovation gap, support companies’ ability to scale, accelerate the use of advanced technologies and strengthen the conditions for business renewal (European Commission 2025a). The issue is not only technological. It is also organisational, strategic and educational: how can companies build the capabilities needed to turn change into growth? 

The capability challenge has become more urgent as artificial intelligence has moved from a specialist technology to a mainstream business concern. According to Stanford HAI (2025), 78 % of organisations reported using AI in 2024, compared with 55 % the previous year. For companies, the critical question is no longer simply whether they use AI, but whether they can use it in ways that strengthen decision-making, innovation and value creation. 

Innovation as organisational capability 

For many companies, the challenge is not a lack of ideas. The more difficult question is whether they have the organisational capability to identify opportunities, use knowledge effectively, test new solutions and translate learning into business decisions. Innovation-driven growth should therefore not be understood as a sequence of separate projects. It is increasingly a strategic capability embedded in how companies learn, decide, collaborate and renew their business. 

A capability perspective matters because renewal does not happen automatically when new technologies or ideas become available. Recent research on SMEs’ digital transformation shows that dynamic capabilities can support digital leadership and digital culture, both of which are needed when companies try to move from technology adoption to broader organisational renewal (Held et al. 2025). In SMEs, the link between capability and renewal is particularly relevant because renewal often depends on flexible structures, practical experimentation and the ability to mobilise people around change. 

For growth-oriented companies, RDI becomes highly relevant because it gives renewal a practical structure rather than leaving development work at the level of ideas. RDI should not be seen only as a higher education or research policy concept. In business practice, it provides a way to explore new opportunities, gather and interpret evidence, develop solutions, test assumptions and evaluate impact. 

RDI creates value when it is translated into decisions, experiments, partnerships and measurable business outcomes. Innovation is not only about creativity or ideation. It is about the disciplined capability to move from uncertainty to insight, from insight to experimentation and from experimentation to implementation. 

Foresight must become action 

Strategic foresight is often presented as a way to help organisations prepare for change. It can broaden thinking, reveal weak signals and help decision-makers consider alternative futures. Robinson and Doherty (2025) describe strategic intelligence tools, including horizon scanning and foresight, as instruments that can support the governance of emerging technologies and help decision-makers understand uncertainty before it becomes a mainstream challenge. 

However, foresight alone does not create growth. Reports, scenarios and trend analyses may increase awareness, but they do not automatically lead to pilots, competence development, investment decisions or new business models. SMEs illustrate the problem clearly: resources for strategic development, technology scouting and innovation management are often limited. 

Merikoski and Heikkinen (2026) make this gap visible in their practice-based case on shared RDI infrastructures in South Savo. They argue that foresight becomes meaningful for SMEs when it is embedded in structures that reduce uncertainty and support action. In their analysis, shared RDI infrastructures, digital innovation hub activities, brokerage, differentiated service pathways and ecosystem events can function as environments where foresight is translated into experimentation, capability building and step-by-step RDI pathways. 

The South Savo case has relevance beyond one region because innovation-driven growth requires more than future-oriented information. Companies also need environments, people and processes that help them answer practical questions: What does a trend mean for our business? What could we test first? What kind of knowledge or partner do we need? How can we reduce the risk of experimentation? What would count as evidence that the idea is worth developing further? 

Foresight creates value only when it becomes actionable. Companies need professionals who can translate future signals into development agendas, experiments, decisions and measurable renewal. 

Data, AI and strategic judgement 

The growing use of data and AI creates new opportunities for companies. The capability gap becomes visible when tools are expected to create value without changes in strategy, workflows, leadership and organisational learning. As AI use expands, the strategic challenge for organisations is shifting from adoption to impact. Agentic AI is attracting growing attention, but many organisations are still learning how to move from isolated experiments to scalable business value. Scalable value depends on aligning AI with strategy, operating models, talent, data practices and systematic scaling (McKinsey & Company 2025). 

AI does not reduce the need for human judgement; it makes that judgement more important. Technology can accelerate analysis, ideation, communication and decision support, but leaders and specialists still need to frame the right questions, evaluate outputs critically, understand business context and make responsible choices. Without critical judgement and understanding of business context, AI may remain a productivity tool rather than becoming a driver of innovation-driven growth. 

Recent research on SME digital transformation also frames digital and AI capability as a managerial and organisational issue, not only a technical one. Kahveci (2025) argues that digital transformation in SMEs requires more than the adoption of digital tools. It involves digital awareness, strategic roadmapping, implementation and continuous improvement. 

Future business professionals face a dual competence requirement. They need sufficient data and AI literacy to understand technological possibilities and limitations. At the same time, they need strategic, ethical and organisational judgement to connect AI-supported work to business renewal, customer value and long-term competitiveness. 

Growth is collaborative 

Innovation-driven growth is also increasingly collaborative. Companies rarely create and scale innovations alone. New value is often built through interaction with customers, suppliers, technology partners, higher education institutions, public actors, financiers and international networks. Collaboration and ecosystem competence have become central to business renewal. 

Recent research links strategic foresight, knowledge management and open innovation to new product development success. Mubarak et al. (2025) show that strategic foresight and knowledge management can support open innovation and improve new product development outcomes. For companies, renewal depends not only on internal expertise, but also on the ability to build relationships, absorb external knowledge and turn it into practical innovation. 

At the regional level, higher education institutions have a specific role. They are not only providers of graduates. They can also act as RDI partners, capability builders and ecosystem orchestrators. Through applied research, development environments and collaboration platforms, they can help companies access new knowledge, test technologies, develop innovation processes and build connections that individual firms may not be able to create alone. 

Merikoski and Heikkinen (2026) place intermediary support at the centre of shared RDI infrastructures. Their analysis shows that SMEs often need more than general information about AI, automation or digital transformation; they need support in interpreting relevance, identifying first steps, finding partners and accessing experimentation environments. 

Innovation-driven growth can be understood as an ecosystem competence. Companies need professionals who can work across organisational, disciplinary and cultural boundaries; translate different forms of expertise into shared development agendas; and move ideas from discussion to implementation. 

Four capabilities for growth 

The changing business environment points to four capabilities that are particularly important for innovation-driven business growth. 

First, companies need strategic renewal capability, which refers to the ability to connect innovation with strategy, customer value, competitiveness and growth. Innovation should not be treated as an isolated development activity, but as part of how the company defines its future direction. 

Second, companies need RDI-based development capability, which means the ability to use research, development and innovation as a systematic process for creating knowledge, testing solutions and building evidence for business decisions. RDI capability strengthens companies’ ability to move beyond assumptions and base renewal on structured learning. 

Third, companies need data-informed and AI-supported decision-making capability. This includes the ability to use data, analytics and AI to support learning, foresight, experimentation and value creation. The capability also requires critical thinking, ethical awareness and understanding of business context. 

Fourth, companies need collaborative implementation and impact capability. Ideas create value only when they are implemented. This requires the ability to engage people, build partnerships, manage change, communicate across boundaries and evaluate whether development work produces meaningful business and societal impact. 

Together, the four capabilities shift the focus from individual innovations to organisational renewal. They help companies ask a more strategic question: do we have the capability to turn ideas, data, technologies and partnerships into implemented business growth? 

Education for innovation-driven growth 

For higher education, innovation-driven business growth is both a business challenge and an educational challenge. Future business leaders and specialists need learning environments that combine innovation, strategy, RDI, data, AI and collaboration in practical development contexts. Education should not only transfer knowledge about innovation. It should help professionals build the capability to lead renewal in uncertain, technology-intensive and networked business environments. 

The key design principle is integration. Strategic leadership, innovation management, RDI, data-informed decision-making and collaborative development should not be treated as separate topics. They need to be connected to authentic organisational development challenges where professionals learn to define problems, gather evidence, test solutions, evaluate impact and communicate results. 

Strong educational relevance requires a close connection between education and working life. Students should be able to apply concepts to real development challenges, use RDI methods to structure inquiry and experimentation, and develop solutions that are relevant for companies and organisations. Through such learning arrangements, higher education can support not only individual competence development, but also the renewal capacity of companies and regional innovation ecosystems. 

The forthcoming Innovation-Driven Business Growth MBA at South-Eastern Finland University of Applied Sciences offers one example of an educational response to the need for integrated innovation capability. The programme is designed for professionals who want to lead research, development and innovation in organisations and translate these activities into strategic renewal and business growth (Xamk 2026). Its educational logic connects RDI, innovation management, strategic leadership, data-driven decision-making and collaborative development to authentic organisational development contexts. 

The issue is not only a curriculum question. The broader challenge concerns what business education must support. If companies are expected to grow through innovation, professionals need the ability to move between strategy and experimentation, data and judgement, technology and people, ideas and implementation. Moving between strategy and experimentation, data and judgement, technology and people, ideas and implementation forms the core competence of innovation-driven business growth. 

From capability to impact 

Innovation-driven growth begins with ideas, but business impact emerges only when organisations build the capability to learn, experiment, decide and collaborate systematically. RDI, data and AI create value when they are connected to strategic renewal, human judgement and practical implementation. 

Companies that build these capabilities are better equipped to respond to uncertainty, recognise opportunities, test new solutions and turn partnerships into business renewal. For higher education, the implication is equally clear: learning must help professionals practise the movement from analysis to action, from experimentation to implementation and from individual competence to organisational impact. 

The movement from experiments to impact is not automatic. It requires capabilities that must be intentionally developed, practised and led.

ChatGPT was used to support language editing and to check the fluency and grammar of the text.