تصمیم گیری استراتژیک در عصر هوش مصنوعی (مقاله علمی وزارت علوم)
درجه علمی: نشریه علمی (وزارت علوم)
آرشیو
چکیده
بیش از 80 سال از اولین تلاش ها برای ایجاد هوش مصنوعی می گذرد؛ اما تعییراتی که در سالیان اخیر ایجاد شده، باعث شده است هوش مصنوعی پیشران تحولات سریع، عمیق و بنیادینی در جهان باشد. افزایش تصاعدی حجم داده هایی که خلق و جمع آوری می شوند، پیشرفت چشمگیر فنی در پردازش داده و ارزان شدن زیرساخت های نگه داری و پردازش داده در کنار هم باعث شدند تا شاهد تحول بنیادینی در هوش مصنوعی باشیم. این پژوهش به دنبال بررسی ردپای این تغییرات در تصمیم گیری استراتژیک است. تصمیم گیری استراتژیک همیشه یکی از موضوعات مورد توجه محققین در تحقیقات فرایندی استراتژی بوده است. نوعی از تصمیم گیری که پیچیده، غیرتکراری و جامع است و تأثیرات بلندمدت و غیرقابل تغییری می گذارد. توجه به تصمیم گیری استراتژیک در سطح حکمرانی می تواند نقش مهمی در بهبود فرایندهای تصمیم گیری و به تبع آن بهبود فضای حکمرانی داشته باشد. تحقیقات انجام شده درباره تصمیم گیری استراتژیک داده محور با توجه به نوظهوربودن پدیده، تحقیقات پراکنده ای هستند. بنابراین؛ این پژوهش با هدف ایجاد یک تصویر جامع از تصمیم گیری استراتژیک داده محور با تمرکز بر پژوهش های پیشین انجام شد. برای انجام این پژوهش روش فراترکیب انتخاب شد. در فرایند جستجو 88 مطالعه از منابع علمی شناسایی شد که با بررسی و غربال این مطالعات نهایتاً 36 مطالعه مرتبط مورد بررسی قرار گرفت. در نهایت با تجزیه و تحلیل مطالعات هدف، 102 کد، 25 مقوله فرعی و 4 مقوله اصلی شناسایی شد. چهار مقوله اصلی در این پژوهش عبارتند از شرایط، ویژگی ها، ابعاد و پیامدها که هریک به شناخت وجوهی از پدیده تصمیم گیری استراتژیک داده محور کمک می کنند.Strategic Decision-Making in the Age of Artificial Intelligence
IntroductionOver the past decade, artificial intelligence (AI) and big data have evolved into groundbreaking paradigms, influencing virtually every aspect of human life. Significant advancements in data processing capabilities, a dramatic drop in storage costs, and the exponential growth of data have paved the way for the development of digital tools, turning digital transformation into a reality. From 2000 to 2017, data processing power increased by a staggering 10,000-fold, while storage costs plummeted by 3,000 times. Furthermore, by 2025, the volume of generated data is projected to grow more than 90-fold. These rapid advancements have made strategic decision-making a key area of focus in management and governance.Strategic decision-making has always been significant due to its complexity, uniqueness, and long-term, often irreversible, consequences. With the advent of big data and AI, decision-making has transitioned from intuition-based practices to data-driven processes. New tools now allow organizations to reduce biases, enhance accuracy, and manage uncertain environments more effectively. However, many organizations and governments are yet to harness the full potential of these technologies, partly because of the absence of comprehensive theoretical frameworks.Based on this, the study aims to present an integrated framework for data-driven strategic decision-making by exploring how AI and big data influence this process. By synthesizing previous research findings, it addresses existing gaps in knowledge and provides practical guidance for managers and policymakers. This research emphasizes that data-driven decision-making is not merely a technical tool—it represents a shift in power structures and decision-making mindsets, enabling improved governance and organizational performance.MethodologyThis study uses the meta-synthesis approach, a qualitative method for integrating and interpreting findings from prior research. The method facilitates the identification of patterns, differences, and overlaps, helping to establish cohesive theoretical frameworks. The framework follows the seven-step model of Margarete Sandelowski and Juliet Barroso, which includes formulating research questions, reviewing the literature, identifying and selecting studies, extracting information, analyzing and synthesizing findings, conducting quality control, and presenting results.Research questions focused on identifying the framework and key elements of data-driven strategic decision-making. Relevant studies were sourced from leading databases, such as Scopus, Web of Science, and Emerald, using keywords including "strategic decision-making," "artificial intelligence," "big data," and "data-driven." The study focused on English-language research from 2010 to 2024. Out of 88 initially identified studies, 36 were selected after removing duplicates and reviewing titles, abstracts, and methodologies. Data extracted from these studies underwent open coding, yielding 102 initial codes. These were categorized into 25 subcategories and four main themes: conditions, features, dimensions, and outcomes. To ensure quality, the CASP tool was used for validity checks, and reliability was measured using the Kappa index, scoring 0.69. This rigorous approach enabled the development of a robust and comprehensive framework.Discussion and ResultsThe study's findings are categorized into four main themes:Conditions: These include prerequisites for effective data-driven decision-making, such as access to high-quality data (characterized by volume, accuracy, timeliness, and variety) and advanced infrastructure (hardware and software). Organizational restructuring is essential to integrate data analytics processes, and cross-functional collaboration is necessary for data collection and interpretation. Building technical expertise to develop AI models and fostering a data-driven culture through employee training are also critical. Regulatory frameworks, including periodic evaluations and risk management, play a vital role in ensuring process quality.Features: The core characteristics of data-driven decision-making include bias reduction through objective data, the ability to predict trends and behaviors, and the discovery of hidden patterns using AI. High-speed data processing, accuracy, and transparency contribute to reliable decision-making. Additionally, increased resolution—offering a more precise understanding of issues—is a defining feature of this approach.Dimensions: This theme addresses the structural and contextual elements of the decision-making process. Balancing intuition with data analysis is particularly important in complex, turbulent environments. Structured data significantly enhance the quality of decisions, whereas unstructured data limit the effectiveness of technical tools. Collective intelligence, inspired by natural behaviors, enables the integration of group knowledge. Striking a balance between human creativity and AI computational power, alongside building stakeholder trust through interpretability and user-focused design, are other critical dimensions.Outcomes: Data-driven decision-making reduces uncertainty, identifies new opportunities and threats, and offers solutions to complex challenges. It improves processes through automation, increases speed and accuracy, enhances organizational performance, and creates sustainable competitive advantages. At a national level, it has the potential to transform governance structures and improve outcomes.ConclusionThrough the meta-synthesis approach, this research provides a comprehensive framework for data-driven strategic decision-making, organized into four key themes: conditions, features, dimensions, and outcomes. The findings highlight that implementing this approach requires robust infrastructure, high-quality data, a strong data-driven culture, and cross-departmental collaboration. Features such as bias reduction, predictability, speed, and precision differentiate this method from traditional approaches. Structural elements like the balance between human and AI involvement and the role of collective intelligence emphasize the importance of combining human judgment with computational power. The outcomes include reduced uncertainty, enhanced performance, and sustained competitive advantages.However, the study acknowledges limitations, including its exclusive focus on strategic decision-making, the emerging nature of the topic, and potential biases in study selection. Overall, data-driven strategic decision-making is not optional—it is a necessity for governments and organizations in the digital era. Future research should explore other aspects of strategic management and consider cultural and regional influences to deepen our understanding of this phenomenon. This framework offers managers and policymakers practical tools to harness AI and big data to improve governance and decision-making.
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