Supporting SME companies in mapping out AI potential: a Finnish AI development case

dc.contributor.authorJafarzadeh, Pouya
dc.contributor.authorVähämäki, Tanja
dc.contributor.authorNevalainen, Paavo
dc.contributor.authorTuomisto, Antti
dc.contributor.authorHeikkonen, Jukka
dc.contributor.organizationfi=data-analytiikka|en=Data-analytiikka|
dc.contributor.organizationfi=tietojärjestelmätiede|en=Information Systems Science|
dc.contributor.organizationfi=tietotekniikan laitos|en=Department of Computing|
dc.contributor.organization-code1.2.246.10.2458963.20.68940835793
dc.contributor.organization-code1.2.246.10.2458963.20.70128852004
dc.contributor.organization-code1.2.246.10.2458963.20.85312822902
dc.converis.publication-id457336151
dc.converis.urlhttps://research.utu.fi/converis/portal/Publication/457336151
dc.date.accessioned2025-08-27T23:41:07Z
dc.date.available2025-08-27T23:41:07Z
dc.description.abstract<p>Products and services relying upon Artificial Intelligence (AI) have moved from mere concepts to reality. However, challenges still exist in applying AI technologies to traditional industrial and service enterprises. Two central problems are a proper understanding of the opportunities AI could bring to the business processes and making the business logic and data sources transparent to AI experts. As small and medium-sized enterprises (SMEs) are considered the economic backbone of many countries, this paper studies how to support SMEs in understanding the potential of AI in their business and how to prepare their data and requirements for a possible AI project. For this purpose, we first proposed the Cross-Industry Standard Process for Data Mining (CRISP-DM) an industry-proven way to apply AI solutions. The weight was in early business and data understanding. Then, we performed data visualization and developed some machine learning methods for 11 SMEs in South-western Finland as case studies to get more ideas for improving their business using AI. Two surveys probed the possible changes in AI practises of companies.<br></p>
dc.identifier.eissn1573-7047
dc.identifier.jour-issn0892-9912
dc.identifier.olddbid204419
dc.identifier.oldhandle10024/187446
dc.identifier.urihttps://www.utupub.fi/handle/11111/52621
dc.identifier.urlhttps://link.springer.com/article/10.1007/s10961-024-10122-5
dc.identifier.urnURN:NBN:fi-fe2025082786436
dc.language.isoen
dc.okm.affiliatedauthorJafar Zadeh, Pouya
dc.okm.affiliatedauthorVähämäki, Tanja
dc.okm.affiliatedauthorNevalainen, Paavo
dc.okm.affiliatedauthorTuomisto, Antti
dc.okm.affiliatedauthorHeikkonen, Jukka
dc.okm.discipline113 Computer and information sciencesen_GB
dc.okm.discipline512 Business and managementen_GB
dc.okm.discipline113 Tietojenkäsittely ja informaatiotieteetfi_FI
dc.okm.discipline512 Liiketaloustiedefi_FI
dc.okm.internationalcopublicationnot an international co-publication
dc.okm.internationalityInternational publication
dc.okm.typeA1 ScientificArticle
dc.publisherSpringer Nature
dc.publisher.countryUnited Statesen_GB
dc.publisher.countryYhdysvallat (USA)fi_FI
dc.publisher.country-codeUS
dc.relation.doi10.1007/s10961-024-10122-5
dc.relation.ispartofjournalJournal of Technology Transfer
dc.source.identifierhttps://www.utupub.fi/handle/10024/187446
dc.titleSupporting SME companies in mapping out AI potential: a Finnish AI development case
dc.year.issued2024

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