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AI IN THE BOARDROOM: DIRECTOR SENSEMAKING IN UKRAINIAN CORPORATE GOVERNANCE
(Manuscript, 2026) Skorupych, Artem
While artificial intelligence increasingly influences organizational decision-making, how corporate board directors make sense of AI for governance purposes remains underexplored, particularly in non-Western and high-stress contexts. This study investigates three questions: how boards currently engage with AI, how board composition and organizational context shape directors' perspectives, and what governance practices directors identify as necessary for responsible adoption. Using a qualitative design, the research collected data through semi-structured interviews (n=5) and written qualitative surveys (n=17) with Ukrainian board directors across banking, technology, energy, healthcare, and other sectors. Analysis followed
Gioia-inspired methodology, progressing from first-order concepts through interpretive themes to aggregate theoretical dimensions. Findings reveal directors hold a dialectical understanding of AI—simultaneously recognizing its potential to address cognitive constraints (information overload, backward focus, data fragmentation) while creating governance risks (explanation difficulties, accountability ambiguity, judgment erosion). Board composition, particularly the mix of technical and traditional expertise, systematically shapes these perspectives, while Ukrainian wartime conditions create paradoxical pressures making AI both more urgent and more risky. Directors converge on governance practices emphasizing human-in-the-loop principles, formal frameworks, transparency, and director capability-building. The study contributes to bounded rationality, upper echelons, and socio-technical systems theories while demonstrating how extreme contexts function as theoretical microscopes, revealing dynamics relevant to boards globally.
IMPROVING TASK COMPLETION PREDICTABILITY AND WORK VISIBILITY IN OUTSOURCED IT PROJECTS USING PROCESS FORMALIZATION AND AI – ASSISTED COMMUNICATION AUTOMATION
(Manuscript, 2026) Diak, Petro
Outsourced IT projects often suffer from unpredictable task completion, limited real-time visibility, and coordination inefficiencies caused by inconsistent status updates and fragmented communication. In the studied environment, Data Engineers and QA engineers update work items in Azure DevOps irregularly, follow individual reporting routines, and frequently communicate via ad hoc messages in Microsoft Teams. As a result, task boards do not reflect the actual state of work, architects and leads are overloaded with coordination tasks, and project managers lack timely information for decision-making. This reduces delivery predictability and erodes client confidence.
This Capstone addresses these issues by developing a Task Visibility and Predictability Framework that combines process formalization with AI-assisted communication automation. Using qualitative methods and semi-structured interviews, the study applies open and axial coding to identify causes of visibility breakdowns and coordination overload. Based on Agile governance and project-management practices, the framework establishes clear reporting responsibilities, standardized update routines, and structured communication rules.
The framework introduces formal update triggers, minimal documentation standards, and role-specific responsibilities. It also incorporates lightweight AI-assisted mechanisms—such as reminders, stale-task detection, and Azure DevOps–Microsoft Teams integration—to support timely updates without relying on complex models or large datasets.
Its application shows improved task visibility, fewer outdated task states, reduced need for status meetings, and lower coordination burden. The findings suggest that even simple automation, when aligned with clear processes, can significantly enhance transparency, predictability, and execution reliability in outsourced IT projects.
AN AI-DRIVEN PREDICTIVE MODEL FOR SCREENING LEGAL PROFESSIONALS IN INTERNATIONALLY ORIENTED UKRAINIAN IT COMPANIES
(Manuscript, 2026) Dzoban, Volodymyr
This applied research remedies a pressing talents issue for legal departments in Ukrainian IT companies because typical credentials are poor predictors for success in a fast-moving technology sector. The author created and tested a screening tool based on large language model and statistical modelling techniques to better evaluate candidates in an initial assessment stage. Using a dataset of 269 legal professionals who were 178 candidates for a major Ukrainian IT company and 91 professionals in the LinkedIn networking group, this study applied logistic regression analysis and narrative coding to explore predictors for success measured as acceptance of a job offer, retention for 24 months, and delivery of satisfactory performance.
Results demonstrated that a background in the information technology industry, English language skills, foreign transaction experience, interest in technology, and a business focus were strong predictors for success, while traditional credentials such as university name, prior employment with a law firm, and membership in a bar association lacked predictive power. The model has 78.4% classification accuracy with 81.2% sensitivity and 75.6% specificity in cross-validation with AUC=0.78. It was used for creating a screening tool based on GPT for candidate screening where output classifies candidate data into organized assessments with scoring points for strong traits, areas of concern, and interview recommendations. Pilot testing showed a 60% reduction in screening time while maintaining quality.
This study addresses a repeatable approach for statistical model development in the context of a particular legal staff environment and a usable screening tool for the technology sector legal recruitment. The results of this research challenge traditional forms of credentialism associated with legal recruitment and establish that culture fit factors potentially outperform legal credentials.
MANAGEMENT TRANSFORMATION IN THE UKRAINIAN VACATION SHORT-TERM RENTAL MARKET: LEVERAGING AI TOOLS FOR MARKETING AS A COMPETITIVE ADVANTAGE AND RESOURCE OPTIMIZATION
(Manuscript, 2026) Danchak, Nestor
The rapidly growing artificial intelligence industry is transforming hospitality operations, with applications spanning automated customer service, content generation, and workflow optimization. While large hotel chains have more resources to adopt enterprise AI solutions, small vacation rental operators managing cabins, cottages, and nature-based properties face critical challenges: dependence on online travel agencies (OTAs) extracting 15-25% commission fees while controlling customer relationships, combined with resource constraints limiting
competitive response capabilities.
This capstone develops a management framework enabling small Ukrainian vacation rental operators to leverage accessible AI tools for marketing automation, platform independence, and resource optimization. The research employs qualitative methodology: empathy mapping and
customer journey analysis. Applying them across nine in-depth interviews with vacation rental guests. Analysis identifies five distinct guest segments (Aesthetic Sensualists, Comfort Planners, Nature Explorers, Festive Socializers, Retreat Seekers) and three universal friction points:
insufficient online brand presence, slow communication response times, and content-audience mismatch.
The resulting framework integrates four automated components implemented through a phased 12-week roadmap, reducing weekly operator time to 2-3 hours while targeting 30-40% direct booking conversion within six months. While specific AI tools evolve rapidly (ChatGPT, Make.com, and Google Vision AI may be superseded), the framework emphasizes goal-oriented methodology: defining clear key performance indicators (KPIs), validating tool effectiveness against strategic objectives, and continuously adapting technology choices to serve business outcomes rather than pursuing technology for its own sake.
This research proposes an actionable framework enabling resource-constrained operators to potentially achieve competitive advantages through strategic AI adoption. The framework design suggests significant time savings and platform independence opportunities.
ЗВІТ БІБЛІОТЕКИ ЗА 2025 РІК
(2026) Гужва, Алла