Elisabetta Benedetti, PhD, is a strategic studies, exercise design and defence professional currently working at NATO’s Joint Force Training Centre (JFTC) in Bydgoszcz, Poland. Her professional and academic work sits at the intersection of military exercise design, decision-making, wargaming, artificial intelligence, complex adaptive systems and human behaviour. Her current interests focus particularly on how emerging technologies affect the way military organisations understand complex environments, make decisions and adapt, and on how these effects can be meaningfully observed and assessed.

At JFTC, she works as a Content Analyst and has undertaken a range of responsibilities across the design, development and execution of NATO exercises. These have included serving as Chief Content, Deputy Chief MEL/MIL ad interim and JEMM Manager ad interim. Her work involves translating training requirements and objectives into exercise structures capable of generating meaningful decisions and observable outcomes, while maintaining coherence between scenario development, exercise control, participant activity and assessment.

Her recent work has increasingly focused on decision-centric exercise design. Rather than approaching an exercise primarily as a sequence of scripted events, this work considers how operational problems, information, constraints, competing priorities and consequences can be structured to create environments in which participants must interpret changing conditions, make decisions and adapt. This has included the development of vignette-based approaches, decision points, inject structures, exercise-control mechanisms and assessment frameworks intended to preserve traceability between training objectives, participant actions and resulting effects.

A related area of interest is the assessment of organisational performance and adaptation. Her work explores the relationship between Measures of Performance, Measures of Effectiveness and Measures of Adaptation, particularly in complex exercise environments where successful completion of an activity does not necessarily demonstrate that the desired effect has been achieved or that an organisation is capable of adapting when assumptions and conditions change. This interest increasingly intersects with Operations Research and Analysis and with the methodological challenges created when artificial intelligence becomes part of the system being observed.

Her work with artificial intelligence extends beyond the use of generative tools. She is particularly interested in the implications of AI for human judgement, organisational behaviour and analytical validity. As AI moves from information processing towards analytical and decision support, distinguishing between human performance, AI performance and the performance of the combined human–AI system becomes increasingly important. This raises practical questions for military experimentation and exercise assessment: what is actually being measured when AI contributes to an output or decision, how should that contribution be recorded, and what conclusions can legitimately be drawn about organisational capability and adaptation?

She has complemented this practical work with formal education in artificial intelligence and data science. In 2026, she completed the Applied AI and Data Science programme at the Massachusetts Institute of Technology, including a capstone project focused on transforming large volumes of unstructured information into actionable intelligence using generative AI. She has also studied Artificial Intelligence in National Security at King’s College London and completed IBM training in Generative AI for Business Leaders. Her continuing professional development reflects an interest not only in the technical capabilities of AI, but also in its operational application, limitations, governance and interaction with human decision-makers.

Wargaming constitutes another major strand of her professional development and practice. She completed the Military Operations Research Society (MORS) Wargaming Certificate Course, the King’s College London Wargaming and Strategy course, the UK Defence Academy Wargaming Practitioner course, and NATO training in Alternative Analysis. She has applied these approaches to military exercise design, scenario development and analytical activities, including work involving opposing-force perspectives, structured decision problems and the exploration of alternative courses of action. These experiences have reinforced her interest in wargaming not simply as a training mechanism, but as a method for exploring decisions, assumptions, interactions and adaptation under uncertainty.

Her academic background provides a complementary theoretical foundation for this work. She holds a PhD in Strategic Studies from the University of Turin. Her doctoral research examined terrorism and terrorist networks through the perspective of complex systems, exploring forms of organisation and interaction that cannot be adequately understood through simple linear models of cause and effect. This research developed a longstanding interest in complexity, networks, emergence, adaptation and the relationship between individual behaviour and system-level outcomes.

She also holds postgraduate qualifications in international military strategic studies and has pursued academic study in psychology and counselling. Her study of psychology has added another dimension to her work on military decision-making, particularly in relation to cognition, behaviour, judgement, uncertainty and human interaction with technology. The convergence of psychology, complex systems and artificial intelligence now informs her interest in human–AI systems and in understanding how technology changes not only the speed or quality of an output, but also the cognitive and organisational processes through which that output is produced.

Before and alongside her current work, she has accumulated experience across academia, defence and international security. She has worked as a university lecturer and professor and has experience communicating complex subjects to audiences with different professional, educational and linguistic backgrounds. This academic experience has contributed to her approach to exercise design, particularly her emphasis on defining what participants are expected to learn or demonstrate and on designing environments in which those outcomes can be observed rather than assumed.

Her NATO experience includes participation in exercises and activities across different NATO command environments and functional contexts. She has contributed in roles involving political advice, grey-cell activity, civil-military interaction, exercise control, scenario development and subject-matter expertise. This exposure to different headquarters and exercise structures has provided a broad perspective on how organisations interpret problems and translate information into decisions under conditions of uncertainty.

She also has a military background through service in the Italian Army Reserve and has undertaken military professional education, including joint-level studies. Combined with her academic and NATO experience, this has shaped an interdisciplinary approach to defence problems in which operational practice, behavioural factors and analytical methods are considered together rather than as separate domains.

Her current research direction brings these strands together around the problem of human–AI strategic adaptation. As AI becomes increasingly embedded in military organisations, she is interested in moving the discussion beyond whether AI can produce faster or better outputs. The more consequential question is how the introduction of AI changes the wider system: what humans continue to do, which cognitive activities are transferred to machines, how trust develops, how organisations respond when AI is uncertain or wrong, and how these changes affect resilience and the capacity to adapt.

Within military exercises, these questions have direct implications for Operations Research and Analysis. Introducing AI into an exercise does not simply introduce a new capability; it may change the object being measured. An apparently improved decision or output may represent better human performance, effective exploitation of technology, superior AI performance, or an emergent property of the combined human–AI system. Understanding these distinctions is essential if exercise observations are to support valid conclusions about readiness, effectiveness and adaptation.

Her broader professional objective is therefore to contribute to approaches that connect exercise design, OR&A, behavioural science, wargaming and artificial intelligence. She is particularly interested in developing practical methods for studying human–AI interaction within realistic military decision environments and in ensuring that technological experimentation remains analytically traceable and operationally meaningful.