Bridging Research, Technology and Real-World Applications
Generative AI-enhanced Knowledge Management in Business (GAIK)
GAIK-project explores how generative AI can transform knowledge processes 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 productivity when assisted by generative AI (Brynjolfsson, 2025)
However, tangible business value from Generative AI implementation projects is still limited
“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
The primary project goal: Creation of the open-source toolkit for knowledge-focused GenAI solution development and implementation
Target audience: Small and Medium-sized companies (SMEs)
Research, co-Development and Innovation activities are integrated in the project
Methodology: Action Design Research
GAIK Consortium
The project is implemented by the consortium of four companies and three academic partners with expertise in business digitalization, knowledge management, data science, GenAI, and natural language processing.
Companies are involved in the co-development of the toolkit with universities.
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
