For business leaders looking to stay ahead of the curve, AI is no longer an option; it’s a necessity. However, like any powerful tool, its effectiveness depends on how well it is understood and applied within the context of the business. For instance, organising work to leverage both AI agents and human intelligence creates hybrid teams that can accomplish tasks with greater agility and creativity.
1. Unlock the right AI strategy
When beginning to explore the potential of AI, one of the most crucial decisions is identifying the right AI strategy. Before diving into AI, businesses must first evaluate their readiness.
“A well-thought-out AI strategy can serve as a catalyst for innovation and growth. The right time to invest in AI depends on several factors, particularly the maturity of the organisation’s operations and the nature of its challenges,” says Mika Ruokonen, Industry Professor of Digital Business at LUT University.
Ruokonen emphasises that AI adoption shouldn’t be seen as a quick fix or a “one-size-fits-all” solution. Instead, it should align with the company’s long-term vision.
For businesses that are still refining their core operations, AI can be a powerful tool to enhance efficiency and optimise processes.
“For businesses that are still refining their core operations, AI can be a powerful tool to enhance efficiency and optimise processes. However, it should be integrated thoughtfully, not as a standalone technology, but as part of a broader transformation strategy. Companies that are not yet prepared for a technological change or that struggle with data quality may face more challenges than benefits,” he clarifies.
Companies in the early stages of AI adoption are better off focusing on small, manageable use cases. For instance, AI can be deployed in repetitive, data-driven tasks where immediate value can be generated. However, businesses should be cautious about over-investing in AI too early, as this can lead to wasted resources if the technology is not yet mature within the organisation.
“In the beginning, companies might have unrealistic expectations regarding AI’s potential, which can lead to disappointment. In time, however, AI can provide significant results. By starting small, organisations can learn from early experiences and then scale their AI efforts as they build both the technical infrastructure and the organisational readiness to embrace it,” Ruokonen adds.
2. Build ultimate hybrid teams
As AI technology becomes increasingly embedded in business operations, AI-driven companies must also understand how to organise and manage work. Here come into play AI agents, which are systems capable of performing long sequences of complex tasks and autonomous decisions. This important technology allows employees to have more time to focus on higher-level decisions and creative problem-solving.
For example, in sales operations, AI agents can automate task sequences like CRM data inserting and analysis, freeing up sales teams’ time for customers.
“The introduction of AI agents does not necessarily or automatically mean replacing human workers. It actually promotes the creation of what are called ‘hybrid intelligence teams,’ combining the strengths of human intelligence with AI capabilities,” states Markus Mäkelä, Head of AI at LUT, who has also worked as a professor and business leader earlier in his career.
With hybrid intelligence teams, the question of implementing AI becomes not only inserting the technology into the organisation’s various elements but also training the employees in effective ways of interacting with AI.
“Such a holistic integration often requires aligning the organisation’s strategy, processes, AI technology, and structure with the strengths of its people. As it happens, this type of smart ‘fit’ – a deep organisational complementarity – is still fairly rare in business life. But when done with skill, including agility, the resulting synergy can produce competitive advantage with some significant sustainability,” Mäkelä adds.
Achieving such powerful but flexible integration forms a ‘congruence’ of a company’s business model. However, design choices related to this can and often do go wrong. For instance, users tend to place too much trust in their new AI companion – to treat these tools as if they were people.
“If AI tools are developed to react like humans, employees will start to view them as humans, often without understanding how these systems actually work. This lack of understanding can lead to a failure to fact-check the material provided or recognize that large language models tend to hallucinate,” Dominik Siemon, Assistant Professor for Systems and Service Development, points out.
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3. Look for new growth opportunities with LUT University
AI doesn’t just promise efficiency—it delivers tangible solutions to complex industrial and societal challenges. At LUT University, AI in the form of machine learning is turned into impactful tools. Utilising the latest research results and collaboration with industries allows businesses to leverage AI to unlock new opportunities for innovation and growth.
“The key is aligning AI capabilities with specific challenges where properly founded benefits are available. This ensures solutions that are both effective and sustainable,” explains Lasse Lensu, Professor of Machine Vision and Data Analysis at LUT.
LUT’s Department of Computational Engineering has made a breakthrough in optimisingindustrial processes by computer vision and mathematical modelling. Digital imaging and AI are applied to assess logs of wood before sawing them, optimising the yield and quality of the end products in the timber industry. In addition, these technologies are applied to the analysis of industrial and laboratory processes to enhance production efficiency and improve quality control.
The key is aligning AI capabilities with specific challenges where properly founded benefits are available.
According to Lensu, healthcare is another field where AI has made significant strides.
“By using AI in medical image analysis, we have developed automatic grading of eye diseases such as diabetic retinopathy, which is a crucial step in monitoring disease progression and preventing vision loss. Similarly, AI has been applied to traffic monitoring and assessing the condition of road infrastructure, ensuring better road safety through timely maintenance,” Lensu says.
Beyond industrial and healthcare applications, the research work extends into environmental monitoring and conservation. For instance, AI-powered imaging systems assist in the automatic analysis of plankton populations, providing timely data for ecological studies. The researchers have also contributed to the identification of the endangered Saimaa ringed seals, a vital step toward protecting this unique species native to Finland.
More information:
Mika Ruokonen
Dominik Siemon
