Tekoälyagenttien ja agenttisen tekoälyn hyödyntäminen monimutkaisissa B2B-myynnin operaatioissa
Year: 2026
Type: dissertation
Abstract: Business-to-business transaction represents both significant and constantly evolving market. As of late, many companies offering B2B products and services have seen increasing competition and buyer demands while striving to meet these challenges with increasing use of digital technologies. On the other hand, artificial intelligence could be seen as the key transformative technology of this decade. The development of generative AI has been further spurred through companies’ continuous quest for efficiency gains and has the concepts of AI agents and agentic AI. These closely intertwined technologies possess similar capabilities to generative AI coupled with ability to perceive digital inputs beyond human prompts, reason with the support of context related information and act within digital environments thus representing technological capability leap through perceive-reason-act (PRA) loop. This thesis explores the question of how AI agents and agentic AI could be utilized in the context of complex B2B operations. The question is approached to qualitative multi-case study where empirical evidence is gathered though 10 semi-structured open-ended interviews conducted with business decision makers of B2B suppliers. Empirical evidence is further supported with literature review addressing both technological aspects of AI agents and agentic AI, and business dynamics of complex B2B sales operations. As the result, this thesis proposes nine agent hypothesis that could be utilized to meet challenges and goals observed in B2B sales operations. The propositions detail how emergent capabilities can be used while pointing out context specific considerations. As the by product, this thesis produces empirical evidence extending the conduct of B2B sales operations, recognized challenges and operational target states.
Source: Aaltodoc (Aalto University)
Author: Takala, Tuomas
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7154667035