Objective This study compares the psychometric properties of scale structures developed by human experts and various generative artificial intelligence (GenAI) models to measure motivation for using GenAI in education. Method Grounded in a theoretical framework, six different item pools were generated by human experts and GenAI prompt bots based on five different language models. Based on the reviews conducted by human experts and GenAI bots, the three forms demonstrating the strongest psychometric performance were selected for the final analysis. Result The confirmatory factor analysis indicated that all three scale structures demonstrated acceptable and good model fit, and high internal consistency and convergent validity. The high correlations between the structures revealed that both human- and AI-generated factors measure similar structures. Conclusions GenAI models can generate psychometrically sound scales; however, human expertise remains essential for ensuring theoretical depth and cultural contextualization. Therefore, hybrid human–AI approaches appear to offer the most robust outcomes.

This study aimed to compare scores obtained for evaluating maxillary left canine tooth models prepared from soap in a dental morphology course using different artificial intelligence (AI) models and dental educators with the same rubric, and to evaluate the feedback generated by the AI models. Assignment models prepared by students were scored by ChatGPT 5.2, Gemini 3 Pro, and Grok 4.1 AI tools, and by dental educators using the same evaluation criteria. The quality of feedback generated by AI models was evaluated by experts using the Global Quality Scale (GQS). Data were analysed using SPSS v27.0, and normality was assessed using the Shapiro-Wilk test. Statistical differences between the three AI tools and expert scores were examined using the Friedman Test and Bonferroni-corrected multiple comparisons, and agreement among evaluators was assessed using Kendall's W coefficient. A significant difference was found between the AI models and expert ratings (p < 0.001), with all AI models receiving higher scores than the experts. Significant differences were also found among the AI models' GQS scores (p < 0.001); Gemini 3 Pro produced the highest feedback quality, while ChatGPT 5.2 produced the lowest. AI models can be used as supportive tools in the assessment and feedback processes in dental education; however, in terms of contextual awareness and personalised feedback, they are not yet at a level to replace expert evaluations.

The increasing growth in the volume of biomolecular data has introduced significant challenges for extracting meaningful molecular-level insights, particularly in predicting interactions between biological sequences such as DNA, RNA, and proteins. These interactions are fundamental to complex biological processes, including gene regulation and immune response. Artificial Intelligence (AI) has played a major role in advancing discoveries in this field, enabling the identification of novel interactions, as demonstrated by various predictive modeling studies. Despite the growing number of scientific publications in this domain, accessibility to practical computational tools has not progressed at the same pace. Existing studies differ substantially in availability: some provide only methodological descriptions, others release source code exclusively for experimental reproducibility, and only a limited number deliver fully automated solutions ready for broad use. Given this context, this paper investigates state-of-the-art studies in biological sequence interaction prediction, emphasizing the public accessibility and usability of available tools, especially for researchers who are not experts in AI or computational methods. We compile and discuss the input requirements of current tools, along with the types of outputs they generate, enabling users to better understand the scenarios in which each solution can be effectively applied. Furthermore, we analyze accessibility-related aspects to support informed selection of tools according to user expertise, ranging from web-based servers with pretrained models that require minimal computational skills to fully end-to-end frameworks capable of training new models on user-defined datasets, though often lacking user-friendly interfaces.

Purpose The purpose of this viewpoint is to contemplate AI (artificial intelligence) in assessment within a higher education setting from the position of an academic with limited AI expertise. The piece is to prompt thought, discussion and action within a highly contemporary debate. Design/methodology/approach The approach is UK academic based using a reflexive/autoethnographic form of discussion based on the author’s 20+ years of higher education experience. Crucially, the position presented is one of limited AI expertise, which is hoped to offer accessibility to the topic and suggestions for likeminded academics. Findings The viewpoint presents a somewhat neutral perspective of AI in higher education assessment from an experienced academic with limited AI expertise. Recommendations are offered to those akin to this position: (1) stay in touch with AI news and changes relating to assessment approaches and potential impacts within higher education, (2) voice concerns/questions and become an active learner and (3) develop new AI skills. This advice is offered within a context of difficult current challenges within higher education and therefore a need to be more proactive in such times. Originality/value A viewpoint like this offers the possibility to discuss relevant issues not only in a timely fashion but also in a highly accessible way. It brings together some key insights from a fairly neutral perspective so that academics can tap into key discussion points to take forward into their own actions.

Brain-related disorders include multiple sclerosis, epilepsy, stroke, Parkinson's disease, and Alzheimer's are among the most significant issues facing modern medicine. Despite enormous advances in neuroimaging, genetics, and computer analysis, successful care and early diagnosis are still difficult due to overlapping symptoms, data heterogeneity, and the complexity of brain activity. This chapter examines the primary clinical, technological, and ethical challenges in diagnosing and treating neurological diseases. It highlights the limitations of current diagnostic tools, treatment-related challenges,and the need for integrated approaches that incorporate data-driven insights, artificial intelligence, and clinical expertise. In addition to highlighting emerging research subjects including neuroinformatics, wearable monitoring devices, and individualized treatment, the debate offers a forward-looking perspective on improving outcomes in brain healthcare.

Artificial intelligence (AI), including machine learning, natural language processing, and large language models, may support implementation practice and research in tasks such as evidence synthesis, determinant assessment, strategy selection, monitoring, adaptation, and theory development. However, these applications of AIdo not form a single, uniform category. They span a continuum from practice-facing applications that support local implementation work to research- and methods-facing applications that support evidence generation and synthesis. The guidance on how to classify, evaluate, and report these uses of AI remains limited. The AI Methods for Implementation Science (AIM-IS) program aims to develop, validate, and maintain a suite of products to guide the responsible use of AI across implementation practice, implementation research, and bridging use cases. AIM-IS is a multi-phase, multi-method methodological development program. The unit of analysis is the AI-for-implementation use case: a specific AI capability supporting a defined implementation practice or research task within a workflow, decision point, and governance context. Phase 1 is a living scoping review mapping published AI use cases in implementation science, including how they are evaluated and what risks they raise. Phase 2 is a qualitative interview study with implementation researchers, practitioners, AI experts, community members, and data infrastructure and governance experts to refine use cases and identify feasibility constraints, outcome priorities, and reporting needs. Phase 3 will integrate findings from Phases 1 and 2 to develop thedraft AIM-IS products, including a framework, ataxonomyof use cases, guardrailsfor responsible use, apractical guide, outcome domains, and reporting items. Phase 4 will use an eDelphi process and consensus meeting to refine and finalize these products. Phase 5 will conduct usability testing to improve clarity and ease of use, resulting in the finalizedAIM-IS products. AIM-IS is informed by implementation science, sociotechnicalsystems, equity, and responsible AI frameworks, and includes a living-update approach to support ongoing refinement. TheAIM-IS programwill deliver a suite of products, including a framework, toolkit and reporting standard, to support the specification, governance, evaluation, and reporting of AI in implementation science. Together, these products aim to strengthen transparency, comparability, accountability, and attention to equity in how AI is used by implementation practitioners andresearchers over time. Open Science Framework, March 15, 2026: https://doi.org/10.17605/OSF.IO/BX35K.

Artificial Intelligence (AI) is increasingly shaping leadership effectiveness in manufacturing by enabling data-driven decision-making and enhancing collaboration with AI systems. In emerging economies such as Egypt, however, AI adoption remains uneven across organizational functions, limiting its potential to strengthen leadership practices. Empirical research on AI adoption in developing manufacturing contexts is scarce. This study addresses this gap through a literature review and exploratory research involving 60 senior leaders from 15 manufacturing firms across eight industries, including automotive, electronics, home appliances, glass and crystal, steel, chemicals, textiles, and food processing. Findings show that AI adoption is highest in strategic planning (21–25%) and customer analytics (16%), moderate in operational areas such as production, quality, and supply chain management (10–18%), and lowest in workforce analytics (3%) and innovation/R&amp;D (2–5%), revealing a fragmented adoption landscape that limits leadership integration. Key barriers include legacy systems, limited data infrastructure, fragmented governance, and organizational resistance. To address these challenges, a structured brainstorming process engaged executives, managers, and AI experts to generate, refine, and prioritize initiatives, resulting in a phased, KPI-driven framework for AI-enabled leadership that integrates digital capabilities, organizational alignment, ethical practices, and strategic governance. The study demonstrates that cross-functional collaboration, ethical oversight, and iterative implementation can transform isolated AI initiatives into sustainable strategic enablers, enhancing leadership effectiveness, operational efficiency, workforce engagement, and long-term competitiveness. These findings provide actionable guidance for advancing AI adoption in manufacturing and highlight directions for future research.

Artificial intelligence in clinical nutrition. A narrative review.

Artificial intelligence (AI) promises to accelerate and democratize medical imaging, yet low- and middle-income countries (LMICs) face distinct barriers to adoption. This perspective identifies those barriers and proposes an action-oriented roadmap. Insights were synthesized from a Johns Hopkins Science Diplomacy Hub workshop (18 experts in radiology, AI, and health policy) and a scoping review of peer-reviewed and grey literature. Workshop discussions were transcribed, thematically coded, and iteratively validated to reach consensus. Five interlocking barriers were prioritized: (1) infrastructure gaps-scarce imaging devices, unstable power, and limited bandwidth; (2) data deficiencies-small, non-representative, or ethically constrained datasets; (3) workforce shortages and brain drain; (4) uncertain ethical, regulatory, and medicolegal frameworks; and (5) financing and sustainability constraints. Case studies from Nigeria, Uganda, and Colombia showed that low-field MRI, cloud-based PACS, community-engaged data collection, and public-private partnerships can successfully mitigate several of these challenges. Targeted policy levers-including shared procurement of low-cost hardware, regional AI and data hubs, train-the-trainer workforce programs, and harmonized regulation-can enable LMIC health systems to deploy AI imaging responsibly, shorten diagnostic delays, and improve patient outcomes. Lessons are transferable to resource-constrained settings worldwide. Question How can LMICs overcome infrastructure, data, workforce, regulatory, and financing barriers to implement artificial-intelligence tools in clinical medical imaging? Findings Our multinational consensus identifies five obstacles and maps each to actionable levers: low-cost hardware, regional data hubs, train-the-trainer schemes, harmonized regulation, blended financing. Clinical relevance Implementing these targeted measures enables LMIC health systems to deploy AI imaging reliably, shorten diagnostic delays, and improve patient outcomes while reducing dependence on external expertise.

The artificial intelligence (AI) field holds significant promise to revolutionize healthcare, including transfusion medicine (TM). This study explored AI use in TM, education and research among International Society of Blood Transfusion (ISBT) members. A mixed methodology was employed. A survey was conducted June to November 2024. Eighteen participants were interviewed. A total of 218 ISBT members from 67 countries responded to the survey, 43.5% of which use AI. Most users (91.1%) have used Generative AI (GenAI); 82.3% indicated they were self-taught. Application to clinical TM was reported by 54.4%, and 87.3% reported a positive impact. A third of respondents (32.7%) indicated the use of AI in their institutions, commonly GenAI tools. More than two-thirds indicated use in TM education, research or both, and 71.1% indicated a positive impact on their institution's operations. Use in education included preparing lectures and generating questions. Use in research included brainstorming ideas, statistical analysis, coding, data interpretation, manuscript drafting and proofing. Survey respondents reported various challenges in adopting AI, including lack of access to AI resources or expertise (78%), cost (74%), difficulty in hiring AI professionals (73%) and data privacy concerns (72%). Concerns raised during interviews included accuracy of information, regulatory constraints and risks on intellectual ability and employment. There is general interest in the use of AI in TM practice, education and research. Barriers to adoption include access to the technology and lack of AI professionals. Educational resources must be expanded. Regulatory constraints and privacy and trust concerns need to be addressed.

Artificial intelligence (AI) is becoming an integral part of modern medical technologies, especially in the field of disease diagnostics. In recent years, its application in ophthalmology has become broader, and it affects an increasing number of nosologies. This review article covers the issues of AI terminology, historical aspects of the use of AI in medicine in general and in ophthalmology in particular, and highlights modern achievements and scientific developments in this field. The future of AI in ophthalmology and the prospects for the development are discussed. Ophthalmologists, researchers, and artificial intelligence experts are the target audience for this article.

The rapid spread of misinformation in digital communication environments presents significant challenges to information integrity, particularly in emerging media ecosystems such as Nigeria. Recent developments in generative artificial intelligence (GenAI) have introduced new possibilities for detecting and managing misleading information across digital platforms. This study investigates how generative AI can contribute to governing information integrity within Nigeria’s misinformation ecosystem from a communication perspective. Using an exploratory qualitative approach, the study draws on in-depth interviews with experts in artificial intelligence, machine learning, digital media, and information governance. The findings reveal that generative AI can enhance the monitoring of misinformation by identifying misleading narratives, analyzing persuasive message patterns, tracking the spread of viral content, and supporting real-time verification processes in journalism and fact-checking. However, the study also shows that the effectiveness of AI technologies depends on contextual adaptation, ethical governance, and collaboration among stakeholders. AI systems alone cannot fully address misinformation challenges without the support of media literacy initiatives and institutional communication strategies. The study concludes that generative AI can play a significant role in strengthening information integrity within digital public communication.

BackgroundAdvancements in artificial intelligence (AI) are transforming health care, particularly through AI-driven clinical decision support systems (AI-CDSS) that aid in predicting disease progression and personalizing treatment. Despite their potential, adoption remains limited due to clinician concerns about algorithm misuse, misinterpretation, and lack of transparency.ObjectiveThis qualitative study explores the informational needs and preferences of clinicians to better understand and appropriately use AI-CDSS in decision-making. In parallel, this study explores AI experts’ perspectives on what information should be communicated to enable safe and appropriate use of AI-CDSS.MethodsA qualitative description design study was conducted using semistructured interviews with 16 participants (8 clinicians and 8 AI experts). Discussions focused on experiences with AI, informational needs, and feedback on existing reporting standards, including Model Cards, Model Facts, and the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis–Artificial Intelligence (TRIPOD-AI) checklist. The transcripts were analyzed through codebook thematic analysis.ResultsFour key themes were identified: (1) clinicians need clear information on training data, its origin, size, and inclusion and exclusion criteria, to judge model applicability; (2) performance metrics must go beyond the area under the curve (AUC) and be clinically relevant to support informed decisions; (3) limitations and warnings about inappropriate use should be specific and clearly communicated to prevent misuse; and (4) information should be presented in layered, customizable formats within existing clinical software, avoiding unnecessary jargon, and allowing optional deeper explanations. While each of the reviewed reporting standards offered strengths, none were considered sufficient alone. Participants recommended a combined and clinician-centered approach to information delivery. Alignment of reporting standards with clinical workflows and decision thresholds was thought to be crucial to bridge the usability gap.ConclusionsTo improve AI-CDSS adoption in clinical practice, reporting standards must be designed for better clinician comprehension and usability. Enhancing transparency, particularly regarding training data and performance, can likely help clinicians assess AI-CDSS more effectively. Information should be delivered in an accessible, layered format, fitting clinical workflows. Co-creation with clinicians throughout AI-CDSS development was a cross-cutting theme, highlighting its importance in ensuring tools are not only technically sound but also practically usable. Future research should explore how to structurally report on performance and validation metrics for clinician understanding and assess the impact of information provision on AI-CDSS adoption.

Background: Adapted Physical Activity (APA) prescription is a complex decision-making process that integrates clinical guidelines and individual patient characteristics and remains strongly dependent on clinician experience. Generative artificial intelligence (AI) has recently emerged as a potential decision-support tool in exercise prescription; however, its interaction with professional expertise is still unclear. This study compared the perceived quality of APA protocols developed by expert professionals, novice professionals supported by AI, and AI operating autonomously across multiple orthopedic conditions. Methods: In this observational cross-sectional study, five real orthopedic prescriptions (scoliosis, low back pain, osteoporosis, high risk of falls, and osteoarthritis) were used to generate three APA protocols per condition: expert professional (EP), novice professional with AI support (NAI), and AI alone. All protocols were created using an identical standardized prompt and anonymized. A multidisciplinary panel of 135 professionals blindly evaluated the protocols using a structured questionnaire assessing effectiveness, safety, appropriateness, clarity, and progression. Overall quality scores were compared using Friedman tests with post hoc Wilcoxon signed-rank tests. Results: Across all conditions, EP protocols achieved the highest quality scores, followed by NAI, while AI-alone protocols consistently received the lowest ratings (all p < 0.05). NAI protocols showed intermediate performance, partially reducing the expertise gap. Post hoc analyses showed that EP protocols received significantly higher rating than AI protocols in all conditions (p < 0.01). NAI protocols received significantly higher rating than AI protocols in most conditions (p < 0.01) except osteoporosis (p = 0.362). Differences between EP and AI were most pronounced for safety (p < 0.01), appropriateness (tailoring p < 0.01), and progression (p < 0.05), whereas EP–NAI differences were smaller and condition-dependent. AI-alone protocols showed greater variability across pathologies. Conclusions: Professional expertise remains the main determinant of APA protocol quality. AI support can improve protocol structure and perceived quality when used by novice professionals but does not replace expert clinical reasoning. AI-generated protocols without human oversight are not yet suitable for autonomous APA prescription, supporting a complementary, expertise-dependent role of AI in exercise programming.

Artificial intelligence (AI) is transforming education by intelligent tutoring, predictive analytics, intelligent sequencing of content, intelligent development of feedback and less famous but more recent generations of AI chatbots, article summarization, document writing, and expert simulators. However, the problem of educational deployment is not just a technical issue. It is a socio-technical design dilemma and entails pedagogy, teacher workload, equity, data control, scholarly integrity, accessibility and social trust. In this paper, a more advanced review-and-framework paper on the AI in education with a clear journal orientation is developed. Instead of purporting a live field trial, the article asserts a concept scoping synthesis of highly instrumental literature and policy reportages as well as a deployable reference architecture, algorithmic procedures, notebook-based prototyping commodiatives, and explicitly outlined illustrative analytics. There are four contributions of the manuscript. To begin with, it synthesizes the recent work around the areas of personalization, assessment, learning analytics, generative support, and governance. Second, it suggests a multi-level structure that links the learner information, instructional regulations, machine learning algorithms, retrieval enhanced generation, instructor control, and monitoring. Third, it demonstrates the adaptive recommendation and retrieval-grounded feedback generation pseudocode and provides evidence in the form of mock Jupyter notebooks, diagrams, and graphs with which institutions can examine such systems. Fourth, it is converting up-to-date ethical and policy discussions into an action plan in terms of a governance checklist and a roadmap of gradual deployment. The main point is that educational AI of high worth will be created not through the replacement of educators but through the systemate engineering of human-AI co-operation where the intent of instruction, transparency, and accountability can be seen throughout the system lifecycle.

Introduction: Ongoing healthcare shortages necessitate robust remote surveillance models like the electronic Intensive Care Unit (eICU) to manage patients at risk of deterioration. While automated alerts can flag physiological decline, they often lack the clinical context needed for rapid and effective intervention, contributing to clinician cognitive burden and delays in decision-making. To address this gap, our framework utilizes a large language model (LLM), deployed on a scalable cloud platform, to synthesize patient data from a standardized, interoperable electronic health data source. Methods: The cloud based LLM using Gemini 2.5 was developed by integrating Google’s Vertex AI platform with our health system’s Fast Healthcare Interoperability Resources (FHIR)-native data store. We used the Successive Approximation Model (SAM) to guide the LLM development, and implemented a zero-shot, question-answering methodology, which circumvents the need for task-specific prompt fine-tuning. An interdisciplinary team of clinicians, artificial intelligence experts co-developed a user interface and optimized the prompt to generate patient summaries. Results: Triggered by deterioration alerts and using single-line, natural language queries, the LLM proved highly performant, generating summaries from (>45 documents) in less than one minute. The evaluation was conducted by a panel of clinicians and was structured using the Kirkpatrick model. For Level 1 (Reaction), clinicians reported a highly positive response to the summaries ease of use, while Level 3 (Behavior) feedback confirmed the tool’s potential to significantly enhance clinical workflow and decision-making speed. Completeness was rated as sufficient in 99% of cases, with all major diagnoses correctly identified. Crucially, zero instances of critical data hallucination were observed. High temporal accuracy (>99%) was observed with all major clinical events placed in the correct chronological order. Conclusions: This work establishes a robust, cloud-based framework capable of generating real-time clinical patient summaries. Unlike traditional LLM, this zero-shot, question-based method offers a significant leap in efficiency and scalability over fine-tuned models and provides a framework for multimodal LLM.

Patients have largely been excluded from discussions on the use of their health data in developing medical artificial intelligence (AI), despite being directly affected by its integration into care. This study assessed ophthalmology patients' perspectives on AI to inform patient-aligned development and implementation. We conducted a cross-sectional survey across ophthalmology clinics in a large academic hospital system in New York City. Consecutive patients were approached in waiting rooms by a research coordinator to maximise sociodemographic diversity and minimise bias from digital literacy or access. The survey, developed by experts in AI, ethics, ophthalmology and survey methodology, was administered via paper and Qualtrics. It addressed attitudes towards AI in clinical scenarios, willingness to share various types of personal data for AI model development and understanding of AI in ophthalmology. Among 403 respondents, 67% reported a low or no understanding of AI, and 71% expressed interest in learning more. Patients prioritised physician involvement and transparency. Comfort decreased with task complexity: highest for screening, lower for diagnosis and lowest for treatment/surgery. For model development, patients were more comfortable sharing de-identified optical coherence technology or lab data than facial images or genetic data. 90% felt consent was always necessary when using personal data to train AI models. These findings highlight the need for patient education and robust data consent protocols. Implementing an opt-out system for retrospective data use may enhance trust while supporting innovation. Integrating patient perspectives into AI governance can foster trust and transparency in ophthalmology and beyond.

Artificial intelligence (AI) is rapidly advancing respiratory disease management, from diagnosis to population lung health. This scoping review synthesizes the most promising uses of AI in respiratory medicine, with a particular focus on pulmonologists and family physicians interested in lung health. In diagnostics, deep-learning systems streamline chest-imaging workflows by triaging radiographs, detecting COVID-19 pneumonia, and classifying lung nodules on CT. In pulmonary function testing, algorithms detect technical errors and classify spirometric patterns, some claiming to outperforming pulmonologists. Acoustic analysis of cough, breathing, and speech captured on smartphones or wearables offers non-invasive decision support. For monitoring and prediction, AI helps shorten weaning from mechanical ventilation and guides closed-loop strategies for acute respiratory distress. In chronic care, connected devices integrated with environmental data help to forecast asthma and COPD exacerbations, while telehealth and predictive models enable earlier, more personalized interventions. Additional gains are emerging in paediatrics, sleep medicine, lung ultrasounds, and public health. Realizing these benefits will require rigorous multicentre validation and real-world evidence. It will also require proactive bias detection and mitigation with inclusive sampling and equity audits. High-quality, interoperable data and explainable models are needed to enable human oversight. Practical issues such as digital literacy, device access, and usability for children, older adults, and other vulnerable populations also matter for applications requiring patient interaction. With sustained collaboration among clinicians, engineers, AI experts, industry, regulators, and scientific societies, AI can increase the time invested in a satisfactory clinician-patient relationship. With all likelihood, AI can also measurably improve efficiency and accuracy across multiple domains of respiratory care.

BackgroundFor therapists, the spoken word of their patients is among the most important foundations for clinical assessment. At the same time, it is hardly possible to monitor patients continuously and closely in sufficient numbers, for example, to ongoingly assess the risk of suicide in therapeutical conversations. Natural Language Processing (NLP) involves the use of Artificial Intelligence (AI) to analyze human language. Combining it with AI speech processing methods, we obtain multimodal methods which can automatically process large volumes of speech and language data to extract diagnostic information and therefore support individualized treatment plans. Thus, in NLP/multimodal methods, we see the opportunity to significantly improve patient care.MethodsThe SPEAK-SAFE project, implemented by clinicians and clinical researchers from the University hospital in Frankfurt in collaboration with the AI experts from the TU Darmstadt, aims to create the first German psychiatric corpus for evaluating and developing multimodal and NLP models to optimize diagnostic processes in psychiatric, psychosomatic, and psychotherapeutic care. Therefore, we will collect therapist-patient dialogues during therapy sessions. This sensitive data necessitates robust privacy. To meet this requirement, all collected data is pseudonymized, to ensure that no personal data is part of the evaluation and training of the AI models.DiscussionDuring the implementation of our research project, we were faced with challenges regarding the security of patient privacy and the technical implementation of therapy recordings toreassure sufficient data quality for the data analysis. Therefore, in addition to improve the suicidality prediction with multimodal methods we will develop an end-to-end-workflow for further AI-research in the clinical context.Clinical Trial Registration: https://drks.de/search/de/trial/DRKS00027878, identifier DRKS00027878.

The integration of Artificial Intelligence (AI) has sparked a significant strategic shift within the management consulting sector creating a divide in trust. AI surely does enhance the efficiency by automating the processes such as data analysis but the lack of transparency creates a rift making the erosion of data privacy as the most important concern. Data is surely the most important asset of any business consulting firm. This risk jeopardises the client confidentiality which is the cornerstone of the consulting relationships. This makes it mandatory for the consulting firms to incorporate the specialized AI governance into their administrative operations. This study explores the intersectionality between AI, Data Privacy and Strategic Consulting with a special focus on how the European Union’s regulatory framework is pushing the global firms to adapt their business models in order to attract the talent that combines AI expertise with crucial interpersonal skills thereby enhancing the role of the Hybrid Consultant. The results of the study indicate that the EU’s risk based extraterritorial regulations serve as regulatory mandate. The analysis reveals that AI is not a threat of job displacement but a catalyst for role transformation, automating repetitive tasks and challenging the traditional consulting pyramid model. The firms are compelled to shift their service offering towards responsible AI advisory and governance, placing ethical compliance as the core strategic differentiator for sustaining long term market relevance and client confidence.