Ditulis oleh : Dr.Dwi Suryanto, MM., Ph.D.
Date: February 23, 2026
Introduction
In the current global economic landscape, the conversation surrounding Artificial Intelligence has shifted from speculative curiosity to a mandate for survival. As the world navigates a post-pandemic recovery characterized by volatile inflation and geopolitical shifts, AI has emerged as the primary lever for productivity. According to the IMF (2024), nearly 40% of global employment is exposed to AI, a figure that rises to 60% in advanced economies. This represents a fundamental restructuring of how value is created.
Consider the case of a mid-sized regional bank. While their competitors were mired in legacy processes, this institution integrated a data-driven AI framework into their marketing and risk assessment. Within twelve months, they didn’t just automate tasks; they re-engineered their entire customer journey. This is not merely a technical upgrade it is a strategic acceleration. For the modern executive, the challenge is no longer if to implement AI, but how to align it with the core organizational DNA to ensure sustainable growth.
Concepts and Theoretical Foundations
At the heart of a successful AI-driven transformation lies the principle of Strategic Alignment. As articulated by Taşkın (2022), the synergy between enterprise systems and overarching business objectives is the single greatest predictor of operational efficiency. Without this alignment, AI remains an expensive silo.
Furthermore, we must move beyond the “black box” view of technology and embrace Transformational Leadership. In a VUCA (Volatility, Uncertainty, Complexity, and Ambiguity) environment, the leader’s role is to bridge the gap between academic theory and boardroom reality (Noviyanti, 2025). This involves a transition from traditional command-and-control structures to an adaptive, sustainable marketing and operational framework that prizes long-term ESG (Environmental, Social, and Governance) goals as much as quarterly profits.
Evidence and Synthesis
Recent research underscores that AI is most effective when integrated into a holistic business ecosystem.
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Operational Synergy: Taşkın (2022) demonstrates that organizations achieving high levels of strategic alignment see an average increase of 30% in operational efficiency. This confirms that AI must be viewed as a strategic partner, not just a tool.
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Data-Driven Marketing: In the financial sector, Awad (2025) found that AI-driven marketing significantly enhances performance by personalizing the “Marketing Mix.” Similarly, Fareniuk (2023) highlights that data-driven modeling can improve campaign effectiveness by up to 25% in the retail sector.
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Sustainability and Digital Ethics: The surge in ESG awareness is no longer optional. Oprescu (2024) and Gregurec (2025) argue that digital marketing must be sustainable to maintain brand equity. Shwawreh (2025) found that a “Green Business Strategy” actually boosts digital marketing success by 18%, suggesting that ethical AI is also more profitable AI.
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The Human Element: Innovation is driven by people, not just algorithms. Zelienková (2022) posits that visionary leadership is the true engine of AI-based acceleration. However, this comes with risks; Palovski (2020) warns of “emotional burnout” among leaders navigating these rapid transitions, necessitating a focus on psychological resilience and coaching humility (Scherf, 2021).
Current Data, Trends, and Policies (2023–2025)
The macro-level data from 2024 and early 2025 paints a clear picture:
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Tech Adoption: McKinsey (2024) reports that 65% of organizations are now regularly using generative AI, a nearly two-fold increase from the previous year.
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Economic Impact: The World Bank (2024) notes that AI adoption is a key differentiator in GDP growth disparities between emerging and developed markets.
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Policy Shifts: The EU AI Act (2024) and subsequent global frameworks have moved toward “Human-Centric AI,” mandating transparency and accountability in how business algorithms operate.
Cause–Effect Patterns
To visualize the logic of AI acceleration, we can trace the following mechanism:
Strategic Alignment (Taşkın, 2022) → Enhanced Operational Capacity → Data-Driven Precision (Awad, 2025) → Customer Loyalty & Revenue Growth (Unknown, 2023).
Parallelly:
Transformational Leadership (Noviyanti, 2025) → Green/Sustainable Innovation (Shwawreh, 2025) → Stronger ESG Reputation (Oprescu, 2024) → Long-term Business Viability.
Cross-Domain Insights
The integration of AI in business mirrors Biological Complexity Theory. Just as an ecosystem thrives on the balance between different species, a business thrives when AI, human leadership, and sustainable practices are in equilibrium. Furthermore, we see a parallel in Supply Chain Management: just as “Just-in-Time” delivery requires perfect synchronization across nodes, AI acceleration requires perfect synchronization across departments from HR (managing generational shifts, Kati, 2021) to Finance.
Practical Recommendations
For CEOs and Founders:
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Prioritize “Strategic Alignment” audits. Do not invest in AI until your software objectives are perfectly synchronized with your business clarity (Tarawneh, 2019).
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Champion a culture of “Humility in Coaching” to encourage continuous learning at the executive level.
For Middle Managers:
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Implement Marketing Mix Modeling to optimize media spend based on real-time AI insights (Fareniuk, 2023).
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Focus on knowledge management and process re-engineering to ensure your team can adapt to AI disruptions (Nkurunziza, 2018).
For Policymakers:
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Develop frameworks that incentivize “Green Marketing” and sustainable AI practices among MSMEs to drive local economic resilience (Pranata, 2025).
Conclusion
AI is the most potent business accelerator of our century, but it is not a “plug-and-play” solution. It requires a sophisticated blend of strategic alignment, transformational leadership, and an unwavering commitment to sustainability. To truly lead in this era, executives must move beyond the technical jargon and master the strategic orchestration of intelligence.
At Borobudur Training & Consulting, we specialize in bridging this gap. We invite you to join our AI Leadership Training, designed specifically for senior executives who seek to master the integration of AI into their strategic roadmap. Furthermore, for organizations ready to transition from theory to practice, our Business Consulting Services provide bespoke AI implementation strategies tailored to your unique corporate architecture.
Let us help you turn the complexity of AI into your greatest competitive advantage.
References
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Awad, A. (2025). Data-Driven Marketing in Banks: The Role of Artificial Intelligence in Enhancing Marketing Efficiency and Business Performance. International Review of Management and Marketing. https://doi.org/10.32479/irmm.19738
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Fareniuk, Y. (2023). Optimization of Media Strategy via Marketing Mix Modeling in Retailing. Ekonomika. https://doi.org/10.15388/Ekon.2023.102.1.1
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Gregurec, I. (2025). Sustainable Digital Marketing: A Systematic Review and Content Analysis of Current Research. DIEM: Dubrovnik International Economic Meeting. 10.17818/DIEM/2025/1.5
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IMF (2024). Gen-AI: Artificial Intelligence and the Future of Work. IMF Research.
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McKinsey & Company (2024). The State of AI in early 2024: Gen AI adoption spikes and starts to generate value.
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Noviyanti, A. (2025). The Role of Transformational Leadership in Adaptive Business Strategy Implementation in the VUCA Era. Sistem, Informasi, Manajemen, dan Bisnis Adaptif (SIMBA). 10.63985/simba.v1i1.9
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Oprescu, C. (2024). Exploring the ESG Surge: A Systematic Review of ESG and CSR Dynamics. Review of International Comparative Management. 10.24818/rmci.2024.2.229
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Shwawreh (2025). The Role of Green Business Strategy in Enhancing Digital Marketing Strategy for Sustainable Business Intelligence. International Review of Management and Marketing. 10.32479/irmm.18287
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Taşkın, N. (2022). An Empirical Study on Strategic Alignment of Enterprise Systems. Acta Infologica. 10.26650/acin.1079619
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Tarawneh, M. M. (2019). The Alignment Between Business Objectives Clarity and Software Objectives. Computer Engineering and Intelligent Systems. 10.7176/ceis/10-2-04
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