Bridging Research, Technology and Real-World Applications
Generative AI-enhanced Knowledge Management in Business (GAIK)
GAIK-project explores how generative AI can transform knowledge management in businesses through collaborative research between academia, technology providers, and industry partners.
Generative AI (GenAI) has significant potential to increase the productivity of knowledge work
Example experiments: consultants using AI were significantly more productive – they completed 12.2% more tasks on average, and completed tasks 25.1% more quickly (Dell’Acqua, 2023)
Example cases from business practice: Customer-support agents at a large firm selling business-process software demonstrated a 15% increase in productivitywhen assisted by generative AI (Brynjolfsson, 2025)
“Only 26% of companies have advanced beyond the proof-of-concept stage to generate value” Source: BCG’s report (de Bellefonds et al, 2024)
“Despite $30–40 billion in enterprise investment into GenAI, 95% of organizations are getting zero return.” Source: MIT report (Challapally et al, 2025).
GAIK’s Approach
Knowledge management (KM) remains one of the most persistent challenges facing modern businesses. Despite rapid advances in generative AI, most companies struggle to effectively implement these technologies for knowledge-intensive processes. The project utilizes cutting-edge generative AI (GenAI) technologies to address significant challenges in KM and access, aiming to reduce information bottlenecks and enhance the productivity of knowledge workers in SME companies.
GAIK Consortium
The project is implemented by the consortium of five companies and five academic partners with expertise in business digitalization, KM, data science, GenAI, and natural language processing as well as user companies from the manufacturing, healthcare and well-being, and construction sectors. The toolkit targets the identified use cases tailored to contemporary business requirements, as reflected by the needs analysis of the partner companies.
Three fundamental KM-processes:
- Knowledge generation: auto-generate business reports, sales proposals, marketing materials, project proposals etc.
- Knowledge capture: extract information from business documents, videos, voice recordings, emails, and meeting recordings etc.
- Knowledge access: quick, precise, and intelligent access to organizational knowledge (document repositories, databases, wikis, CRMs, intranet etc.)
Key research questions we address:
- How can generative AI be made accessible to businesses without extensive technical expertise?
- What frameworks enable scalable knowledge management solutions across different industries?
- How do we bridge the gap between cutting-edge AI research and practical business applications?
- What reusable components can accelerate AI adoption in knowledge work?
Project Facts
Project Lead: Haaga-Helia UAS
Duration: 2/2025–1/2027
Partners: University of Helsinki, Tampere University, Azets Insight Oy, Lotus Demolition Oy, Luvata Oy, QAdental (Suomen Kotilääkäripalvelu Oy), TIB – Leibniz Information Centre for Science and Technology in Germany, 3IA Côte d’Azur Interdisciplinary Institute for Artificial Intelligence in France, MCI | The Entrepreneurial School in Austria
Co-funding: ERDF
