Dr.Dwi Suryanto, MM., Ph.D.
February 11, 2025
Introduction
In the current global landscape, Artificial Intelligence (AI) is no longer a peripheral technological experiment; it is the central nervous system of modern enterprise strategy. However, as many boards are discovering, the distance between “buying AI” and “capturing value from AI” is vast. We are witnessing a shift from the era of digital experimentation to an era of strategic execution.
Consider a mid-sized retail conglomerate I recently observed. They invested millions in generative AI tools for their marketing team, yet saw zero impact on top-line growth. Why? Because the technology was siloed. It lacked “Strategic Alignment” the harmony between technological capability and organizational purpose. Conversely, firms that treat AI as a core strategic pillar, rather than a plug-and-play tool, are seeing productivity gains of up to 40% (McKinsey, 2024).
This article explores the evidence-based frameworks required to transform AI from a buzzword into a sustainable business engine.
Concepts and Theoretical Foundations
To lead an AI-driven organization, executives must move beyond technical jargon and master two foundational concepts:
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Strategic Alignment Maturity: Based on the work of Taşkın (2022) and Erdağ (2019), this is the degree to which an organization’s AI capabilities are synchronized with its enterprise systems and long-term goals. Without this, AI becomes an expensive distraction.
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The Green-Digital Synergy: In 2025, a business cannot be “digital” without being “green.” Shwawreh (2025) introduces the framework of integrating Green Business Strategies with AI-driven marketing to build “Sustainable Business Intelligence.” This ensures that AI efficiency does not come at the cost of ESG (Environmental, Social, and Governance) commitments.
Evidence and Synthesis: The Pillars of AI Performance
The current body of research emphasizes three critical dimensions of AI integration:
1. Marketing Precision and Economic Viability
Research by Fareniuk (2023) demonstrates that Marketing Mix Modeling (MMM) supported by AI can increase retail marketing effectiveness by 15%. This is echoed by Awad (2025), who notes that in the banking sector, data-driven AI significantly enhances marketing efficiency. For SMEs, Mvunabandi (2024) proves that AI-supported frameworks are vital for the long-term viability of female-led enterprises in emerging markets.
2. Leadership in the VUCA Era
Implementation fails not because of bad code, but because of poor leadership. Noviyanti (2025) highlights that Transformational Leadership is the only way to navigate the Volatility, Uncertainty, Complexity, and Ambiguity (VUCA) of the AI era. Furthermore, Murcio (2021) argues for “Person-Centered Leadership,” ensuring that AI augments human potential rather than replacing it.
3. Resilience and ESG Integration
During times of crisis, AI serves as a resilience tool. Korneyev (2022) observed how Ukrainian businesses utilized digital marketing and AI to maintain operations during wartime. Furthermore, Oprescu (2024) asserts that AI is now essential for managing the complexities of ESG and CSR, providing the transparency that modern stakeholders demand.
Current Data and Global Trends (2023–2025)
The macro-economic data supports this urgency:
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OECD (2024): Business investment in AI is projected to reach $200 billion globally by 2025, yet only 25% of firms have a clear roadmap for ethical AI use.
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IMF (2024): AI is expected to impact 40% of global employment; however, countries and companies that prioritize “knowledge management and adaptability” (Nkurunziza, 2018) will see a net positive gain in GDP growth.
Cause–Effect Patterns: The Logic of AI Success
The mechanism of success can be distilled into the following flow:
High Strategic Alignment + Ethical Leadership → Operational Efficiency → Scalable Innovation.
Conversely:
Siloed AI Tools + Low Adaptability → Increased Complexity → Leadership Burnout (Palovski, 2020).
Cross-Domain Insights
We can draw a parallel from Complexity Theory: an organization is a living system. Just as a biological organism requires its brain (Strategy) and nervous system (AI) to be perfectly synced to survive a changing environment, a corporation requires Situational Leadership (Stręk, 2019) to adjust AI implementation based on the team’s psychological readiness.
Furthermore, the concept of Humility in Coaching (Scherf, 2021) suggests that the most successful AI transitions occur when leaders admit they don’t have all the answers and foster a culture of “Co-Intelligence” between humans and machines (Cheong, 2025).
Practical Recommendations
For CEOs and Founders:
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Audit your Alignment: Use a maturity scale to evaluate if your AI tools actually serve your 3-year strategic goals. Don’t automate a broken process.
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Prioritize ESG: Ensure your AI initiatives contribute to your sustainability targets (Pranata, 2025).
For Middle Managers:
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Foster Psychological Safety: Manage the “Emotional Burnout” (Palovski, 2020) that comes with rapid tech shifts.
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Adopt Marketing Mix Modeling: Use AI to move from “gut feeling” to data-driven decision-making (Fareniuk, 2023).
For Policymakers:
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Incentivize Adaptability: Focus on programs that support SMEs in reengineering their business processes through AI (Nkurunziza, 2018).
Conclusion: Taking the Next Step
The AI revolution is not a race of speed; it is a race of strategic depth. Those who treat AI as a mere IT upgrade will falter. Those who treat it as a fundamental shift in leadership and strategy will lead the next decade.
At Borobudur Training & Consulting, we specialize in bridging the gap between high-level AI theory and boardroom reality. We invite you to join our Executive AI Leadership Training, designed specifically for practitioners who seek to master the integration of AI with strategic purpose.
Beyond training, we offer Strategic Business Consulting for corporations ready to implement AI across their value chain ensuring your transition is ethical, sustainable, and highly profitable.
Let us help you turn AI potential into corporate performance.
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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Cheong, P. H. (2025). Generative Artificial Intelligence and Collaboration: Exploring Religious Human-Machine Communication and Tensions in Leadership Practices. Human-Machine Communication. 10.30658/hmc.11.9
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Erdağ, O. V. (2019). Stratejik Uyumlaşma Olgunluk Ölçeğinin Türkçeye Uyarlanması. Ömer Halisdemir Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi. 10.25287/ohuiibf.542171
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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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Korneyev, M. (2022). Business marketing activities in Ukraine during wartime. Innovative Marketing. http://dx.doi.org/10.21511/im.18(3).2022.05
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Murcio, R. (2021). Person-Centered Leadership: The Practical Idea as a Dynamic Principle for Ethical Leadership. Frontiers in Psychology. https://doi.org/10.3389/fpsyg.2021.708849
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Noviyanti, A. (2025). The Role of Transformational Leadership in Adaptive Business Strategy Implementation in the VUCA Era. 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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Palovski, J. (2020). Clinical and psychological characteristics of emotional burnout in business leaders. Science and Education a New Dimension. 10.31174/send-pp2020-239viii95-19
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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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