Location: Online
Teaching Language: Turkish (with English subtitles)
Organizing Institution: TÜBİTAK
Level: Intermediate
Format: Asynchronous
Industrial domains: Cross-sectoral; public administration; research and innovation; customer service; finance; operations; digital transformation; and technology management
Course description:
Organisations increasingly experiment with artificial intelligence, yet adoption does not automatically translate into measurable value. This course equips managers and decision-makers with a practical framework for determining whether a proposed business problem is genuinely suitable for AI and whether the organisation is ready to proceed. It treats AI primarily as a management and problem-selection decision, rather than as an algorithm or software-purchasing exercise. Participants examine the AI adoption-value gap; compare predictive AI, generative AI, rule-based software, conventional automation, and process improvement; and assess candidate initiatives across data readiness, business impact, technical feasibility, organisational preparedness, workflow integration, human oversight, and risk. Real-world cases illustrate recurring failure patterns such as technology-first projects, poorly defined outcomes, pilot limbo, weak baselines, and probabilistic tools placed in deterministic tasks. The course concludes with a ten-question selection checklist, a Problem Selection Canvas, a four-way decision matrix - start, prepare, postpone, or do not use AI - and a 30-day executive pre-assessment for making disciplined project decisions.
Prerequisites:
Participants should have a basic understanding of AI concepts and familiarity with organisational processes, project management, or managerial decision-making. Programming experience is not required.
Learning Outcomes:
After completing the course, participants will be able to:
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formulate a clear problem statement with users, workflow context, baseline, target metric, and potential harm;
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classify a candidate use case and select an appropriate solution family;
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evaluate whether the available data and organisational conditions support a pilot;
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define success criteria, human review points, safeguards, ownership, and stop conditions; and
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recommend whether to start, prepare, postpone, or avoid an AI initiative and outline a 30-day pre-assessment.
Agenda:
Module 1: The AI adoption-value gap (adoption versus measurable value, why pilots stop before production, RAND's management-rooted failure factors, and the technology-first trap)
Module 2: Which problems are suitable for AI? (five characteristics of an AI-suitable problem, when to use rules, six problem classes, predictive versus generative AI, four filtering questions, and the role of context)
Module 3: Feasibility assessment (the five-dimensional framework, data readiness, baselines and business impact, technical feasibility, organisational readiness, workflow integration, risk, compliance, and total cost)
Module 4: Case lessons and the selection guide (Air Canada, McDonald's and Klarna, seven failure patterns, corrective design, the start-prepare-postpone-do not use AI matrix, the ten-question checklist, the Problem Selection Canvas, and a 30-day executive pre-assessment). Closing, three key messages, BSC AI Factory support, references and conclusion
Instructor Name: Dr. Duygu ÇAKIR
Dr. Duygu Çakır is an academic in the Department of Software Engineering at Bahçeşehir University. She has extensive experience in academic research, software engineering education, and research and development, focusing on artificial intelligence, computer vision, automated machine learning, AI governance, and bias mitigation. She has contributed to and managed government-supported and industry-collaborative projects and has taught programming, data structures, computer graphics, data science, and responsible AI at undergraduate and graduate levels. Dr. Çakır holds a national patent on mobile eye tracking and received an IEEE Best Paper Award in 2021. Her current work focuses on the responsible and value-oriented adoption of AI in organisations. Alongside her academic work, she actively contributes to advancing AI governance practices for SMEs.
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Disclaimer
Only participants who live and/or work in EuroHPC JU participating countries can take part in the event (https://eurohpc-ju.europa.eu/about/discover-eurohpc-ju_en).
For registration purposes, only official email addresses will be accepted. Personal or generic email addresses (e.g., Gmail, Hotmail, Yahoo) are strictly not permitted.
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Acknowledgements
This event was supported by the BSC AI Factory project, which received funding from the European High-Performance Computing Joint Undertaking (JU) under grant agreement No 101234399 and Spain, Portugal, Romania and Türkiye. The JU receives support from the European Union’s Horizon Europe Programme.
