AI Readiness for Financial Forecasting
Hietanoro, Riku (2025-06-12)
AI Readiness for Financial Forecasting
(12.06.2025)
Julkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty.
suljettu
Julkaisun pysyvä osoite on:
https://urn.fi/URN:NBN:fi-fe2025062674552
https://urn.fi/URN:NBN:fi-fe2025062674552
Tiivistelmä
This thesis investigates organizational AI readiness for adopting artificial intelligence in financial forecasting processes. Despite growing interest in AI-driven forecasting, organizations struggle to bridge the gap between technological aspirations and implementation capabilities, with only 26% successfully integrating AI at scale. This research addresses this critical gap by examining what factors characterize organizational AI readiness for the adoption of AI in financial forecasting processes.
The study builds upon existing theoretical frameworks, specifically the Technology–Organization–Environment (TOE) framework (Tornatzky & Fleischer, 1990) and Jöhnk et al.'s (2021) AI readiness model. Using qualitative methodology, semi-structured interviews were conducted with five finance professionals from diverse industries including software, social services, financial services, and business consulting. Data analysis employed Braun and Clarke's thematic analysis approach to identify patterns and themes characterizing AI readiness.
The analysis revealed five main themes encompassing seventeen distinct readiness factors: (1) Technological Infrastructure and Data Readiness, (2) Human Skills and Cultural Attitudes, (3) Leadership and Strategic Alignment, (4) External Environment Constraints, and (5) Perceived Value and Fit of AI Solutions. Four novel factors emerged that extend existing frameworks: Data-Governance Maturity, Trust and Explainability Concerns, Proof-of-Concept & Value-Validation Capability, and Cross-Border Regulatory Alignment. The research also refined understanding of existing factors, revealing nuances such as generational divides in AI attitudes and the specific constraints of legacy spreadsheet-dependent systems.
The findings demonstrate that AI readiness for financial forecasting extends beyond technological preparedness to encompass human, organizational, and regulatory dimensions that existing frameworks only partially address. Financial forecasting's unique characteristics—combining quantitative analysis with qualitative judgment under strict regulatory oversight—create distinct readiness requirements. The research provides actionable insights for organizations, emphasizing the need for strong data governance foundations, human-centric AI strategies that prioritize transparency, and sophisticated regulatory navigation capabilities. The study contributes to AI-readiness theory by showing how domain-specific requirements shape readiness in ways general technology-adoption frameworks cannot fully capture. It proposes a comprehensive conceptual framework for assessing and enhancing organizational preparedness for AI adoption in financial forecasting.
The study builds upon existing theoretical frameworks, specifically the Technology–Organization–Environment (TOE) framework (Tornatzky & Fleischer, 1990) and Jöhnk et al.'s (2021) AI readiness model. Using qualitative methodology, semi-structured interviews were conducted with five finance professionals from diverse industries including software, social services, financial services, and business consulting. Data analysis employed Braun and Clarke's thematic analysis approach to identify patterns and themes characterizing AI readiness.
The analysis revealed five main themes encompassing seventeen distinct readiness factors: (1) Technological Infrastructure and Data Readiness, (2) Human Skills and Cultural Attitudes, (3) Leadership and Strategic Alignment, (4) External Environment Constraints, and (5) Perceived Value and Fit of AI Solutions. Four novel factors emerged that extend existing frameworks: Data-Governance Maturity, Trust and Explainability Concerns, Proof-of-Concept & Value-Validation Capability, and Cross-Border Regulatory Alignment. The research also refined understanding of existing factors, revealing nuances such as generational divides in AI attitudes and the specific constraints of legacy spreadsheet-dependent systems.
The findings demonstrate that AI readiness for financial forecasting extends beyond technological preparedness to encompass human, organizational, and regulatory dimensions that existing frameworks only partially address. Financial forecasting's unique characteristics—combining quantitative analysis with qualitative judgment under strict regulatory oversight—create distinct readiness requirements. The research provides actionable insights for organizations, emphasizing the need for strong data governance foundations, human-centric AI strategies that prioritize transparency, and sophisticated regulatory navigation capabilities. The study contributes to AI-readiness theory by showing how domain-specific requirements shape readiness in ways general technology-adoption frameworks cannot fully capture. It proposes a comprehensive conceptual framework for assessing and enhancing organizational preparedness for AI adoption in financial forecasting.