Focusing only on productivity gains and technology adoption may risk missing the changes needed in the organization itself. AI’s value therefore depends on how employees learn to use and apply it in practice. To successfully integrate AI in organizations, leaders should consider how AI supports their strategy and impacts the existing ways of working.

Authors: Emmi-Karoliina Kaikkonen & Sajal Kabiraj

Artificial intelligence adoption in European enterprises

The share of artificial intelligence (AI) adoption in European enterprises has increased, with 55.03% of large enterprises reported using AI in 2025 (Eurostat 2026). These tools offer organizations new opportunities in reducing costs through productivity gains and automation. However, does adopting new technology mean organizations are ready to adapt with it? European organizations considering AI adoption described lack of relevant skills as the most frequently reported barrier to adoption (Eurostat 2026).

When AI experimentation remains individual

Informal learning has become important enabler of AI adoption. Kaikkonen (2026) discovered that although formal learning resources were available, employees most often discovered new tools through colleagues. Practical use cases encouraged more experimentation and helped employees understand how AI could be applied in their roles.

However, individual experimentation does not always directly translate to shared organizational learning at broader level. Even though employees were experimenting and trying to find useful ways to leverage AI, the successful experiences were not shared across functions or teams. As a result, useful AI practices stayed limited to the groups who had found them, while other parts of the organization remained unaware of these possibilities. (Kaikkonen 2026)

When successful experiments stay isolated, organizations can miss opportunities to integrate AI into broader business processes. Turning individual experimentation into shared organizational capability allows businesses to build on employee learning, rather than rely on isolated pockets of expertise.

Future of knowledge work and sustainability

Sustainability has an important role to play in impacting the future of work in organizations. The future of knowledge work is often the result of circular economy-based models, mitigation of climate-change norms and green transition (World Economic Forum 2025). Organizations adopting AI based on sustainability principles can truly harness the power of data, digitalization and accelerate the transformation through shared digital capabilities like platforms and advanced computing. Sustainability in AI adoption would ensure that investments and development activities are aligned strategically, prioritized by human and societal needs, and can be converted into scalable capabilities with measurable dimensions.

Speed does not equal better quality

By reducing repetitive work, AI can free employees to focus on more meaningful and strategic tasks (Gupta 2025). However, this raises the question: Will time savings create space for high-value activities, or simply increase expectations for output?

Faster task completion may not necessarily change the nature of work. Without changes to existing workflows and expectations, employees could produce more rather than work differently (Tinkoff et al. 2026). Therefore, to gain the advantage from AI, organizations should reconsider how work is structured and how productivity gains are used.

Output quality also matters. AI makes it easier to produce large amounts of content, but increased amount of content may not create greater value (Tinkoff et al. 2026). If employees generate more information than colleagues can use, the result may not be better decision-making but information overload.

AI literacy therefore becomes essential. As AI-generated content in organizations increases, human review and decision-making may become the next bottlenecks. This requires organizations to develop employees’ critical AI skills to ensure that faster output translates into better quality work, rather than simply more content.

The value of human skills in AI enabled work

With AI being integrated into routine tasks, organizations should consider how this impacts skill and career development. Entry-level roles provide practical experience, helping employees build their skills and judgement. (Gupta 2025). If more of these tasks are taken over by AI, alternative ways to gain experience required for career development must be developed elsewhere.

Knowledge work may gradually shift from performing tasks to evaluating and managing AI systems. Employees might spend more time managing AI agents and ensuring their output aligns with business needs. This could create new roles, such as AI integrators who connect technology with business requirements, while also changing workforce structures and management responsibilities. (Gupta 2025). As AI-generated content increases, critical thinking and discernment may become even more valuable. However, these skills require experience and cannot be developed simply by adopting AI technology.

Organizations must therefore consider the impact to talent development and career paths. If AI reduces the time spent on foundational tasks, employees will need alternative opportunities to develop expertise. Future roles should combine human judgement with technical capabilities, instead of assuming that adopting AI will automatically develop these skills.

Moving from adoption to holistic adaptation

Gaining value from AI requires more than technological investment. Leaders must consider how AI changes organizational dynamics, while establishing clear responsibilities for scaling successful practices. This requires collaboration across business and technology functions, supported by processes and resources that enable turning experimentation into lasting organizational change.

Additionally, organizations must enable employees to share what they have learned about applying AI in their business context. Although, solutions may differ depending on teams, sharing practical examples can help others learn and build on the existing knowledge (Kaikkonen 2026). Defining what success with AI looks like is equally important. Tool usage and volume of output may demonstrate activity, but they do not necessarily reflect business value. Instead, organizations should better identify the business problems AI is meant to solve and assess if its use delivers meaningful results. Future of knowledge work should focus on achieving just, lasting and sustainable growth by solving systemic and technological challenges and thus creating a positive and real impact for all stakeholders.

As AI technology evolves, organizations must be able to adapt to their approach. If competitors have access to similar tools, technology itself is not enough to provide lasting competitive advantage (Chen et al. 2022). What may be the key differentiator is how creatively and effectively organizations are able to combine AI capabilities with their people and processes. Therefore, future competitive advantage may depend on how well organizations can learn to apply AI to create unique value.

References

Chen, D., Esperança, J. P., & Wang, S. 2022. The Impact of Artificial intelligence on Firm Performance: An application of the Resource-Based View to E-Commerce Firms. Frontiers in Psychology. Vol.13, 884830. Cited 26 October 2026. Available at https://doi.org/10.3389/fpsyg.2022.884830

Eurostat. 2026. Use of artificial intelligence in enterprises. Cited 24 October 2026. Available at https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Use_of_artificial_intelligence_in_enterprises

Gupta, N. 2025. The rise of AI Copilots: Redefining Human-Machine collaboration in knowledge work. International Journal of Multidisciplinary Research in Science, Engineering and Technology. Vol. 08 (5). Cited 26 October 2026. Available at https://doi.org/10.15680/ijmrset.2025.0805185

Kaikkonen, E. 2026. Organizational Factors Influencing Employee AI Adoption: A Qualitative Case Study in a Multinational Organization. Master’s Thesis. LAB University of Applied Sciences, Master of Business Innovation Culture and Creativity. Cited 24 October 2026. Available at https://urn.fi/URN:NBN:fi:amk-2026101727922

Tinkoff, D., Van Der Veken, L. & Chui, M. 2026. The state of AI in 2026: On the road to ROI. McKinsey & Company. Cited 25 October 2026. Available at https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai#/

World Economic Forum. 2025. The Future of Jobs Report 2025. World Economic Forum.  Cited 25 October 2026. Available at https://www.weforum.org/publications/the-future-of-jobs-report-2025

Authors

Emmi-Karoliina Kaikkonen is a Master of Business Administration (MBA) student in the Business Innovation, Culture and Creativity programme at LAB University of Applied Sciences, Finland.

Sajal Kabiraj, PhD, works as Principal lecturer at LAB School of Business, LAB University of Applied Sciences, Finland.

Illustration: https://unsplash.com/photos/robot-and-human-hands-reaching-toward-ai-text-FHgWFzDDAOs (Unsplash licence)

Reference to this article

Kaikkonen, E.-K. & Kabiraj, S. 2026. Rethinking the future of knowledge work: considerations for organizations adopting AI. LAB Pro. Cited and the date of citation. Available at https://www.labopen.fi/lab-pro/rethinking-the-future-of-knowledge-work-considerations-for-organizations-adopting-ai/