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The AI Imperative: Aligning Strategy, Leadership, and Ethics

Dr.Dwi Suryanto, MM., Ph.D.
February 26, 2026

Introduction

The global economy is currently navigating a “Great Decoupling,” where the divide between AI-enabled organizations and traditional firms is widening at an exponential rate. According to recent OECD data (2024), AI adoption in the enterprise sector has grown by 35% annually, yet nearly 70% of these initiatives fail to deliver ROI due to a lack of strategic alignment.

Consider a regional retail chain facing a sudden inventory crisis during a period of market volatility. While their traditional models signaled a “status quo,” an AI-integrated competitor utilized predictive “Marketing Mix Modeling” to reallocate media spend and adjust supply chains in real-time, effectively capturing a 15% increase in market share while others stagnated. This is no longer a technological choice; it is a strategic necessity.

Concepts and Theoretical Foundations

At the boardroom level, AI must be viewed through the lens of Strategic Alignment—the cohesive bond between digital infrastructure and organizational goals (Taskin, 2022). We move beyond “AI as a tool” toward “AI as a Core Competency.”

Furthermore, the integration of Green Business Strategy and AI-driven Sustainable Digital Marketing (Shwawreh, 2025) provides a framework for “Responsible Intelligence.” This ensures that technological leaps do not compromise Environmental, Social, and Governance (ESG) commitments, which are now critical for securing institutional investment.

Evidence and Synthesis

Recent empirical evidence suggests that the success of AI is predicated on three pillars:

  1. Operational Precision: Research by Fareniuk (2023) and Awad (2025) demonstrates that AI-driven data marketing and marketing mix modeling significantly enhance efficiency in sectors ranging from retail to banking.

  2. Strategic Resilience: Korneyev (2022) highlights how AI enables businesses to remain adaptive even during extreme crises, acting as a buffer against geopolitical and economic shocks.

  3. Human-Centric Integration: Technology alone is insufficient. Murcio (2021) and Cheong (2025) emphasize that ethical, person-centered leadership is required to manage the “Human-Machine Communication” interface effectively. Without this, the risk of “Emotional Burnout” among leadership (Palovski, 2020) becomes a significant barrier to progress.

Current Data, Trends, and Policies (2023–2025)

  • Economic Impact: The IMF (2024) reports that AI could influence 40% of global jobs, necessitating a rapid upskilling of the senior workforce.

  • Adoption Rates: McKinsey Insights (2025) indicate that “High-Performance AI” organizations are 2.5x more likely to report EBIT growth of over 20% compared to their peers.

  • Policy Shift: With the full implementation of the EU AI Act and similar frameworks in ASEAN, transparency and “Ethical AI” (Oprescu, 2024) are now legal mandates rather than corporate options.

Cause–Effect Patterns

The logic of AI success follows a distinct mechanism:
Strategic Alignment Maturity → Process Reengineering (AI-Driven) → Increased Adaptability → Sustainable Performance.

Conversely:
Technological Silos → Leadership Burnout/Resistance → Strategic Drift → Competitive Disruption.

Cross-Domain Insights

We can draw a parallel from Complexity Theory: an organization is a living system. Just as a biological organism requires a nervous system (AI) that is perfectly tuned to its brain (Strategy), a corporation cannot survive if its technology functions independently of its leadership. Furthermore, the concept of Humility in Coaching (Scherf, 2021) suggests that leaders must embrace a “learning mindset” to navigate the fallibility and iterative nature of AI implementation.

Practical Recommendations

For CEOs and Board Members:

  • Audit for Alignment: Use a “Strategic Alignment Maturity Scale” (Erdag, 2019) to assess if your AI investments actually serve your 5-year vision.

  • Prioritize ESG: Integrate AI into your CSR and ESG reporting to enhance transparency and stakeholder trust (Oprescu, 2024).

For Middle Managers:

  • Focus on Knowledge Management: Bridge the gap between data scientists and department heads to ensure AI insights are actionable (Nkurunziza, 2018).

  • Monitor Human Capital: Actively manage the psychological safety and burnout levels of teams undergoing rapid digital transformation.

For Policymakers:

  • Framework Development: Create “Safe Harbor” environments for MSMEs to experiment with Green AI Marketing (Pranata, 2025).

Conclusion

AI is the defining strategic variable of our decade. However, the path to excellence is not paved with algorithms alone, but with the wisdom of leadership and the precision of strategy.

At Borobudur Training & Consulting, we specialize in bridging this gap. We invite senior leaders to join our Executive AI Leadership Training, designed to transform AI from a technical concept into a competitive advantage.

Beyond training, our Business Consultation Services provide bespoke AI implementation roadmaps tailored to your corporate ecosystem. Let us help you navigate the complexity of the digital age with clarity and strategic depth.

References

  • 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

  • Cheong, P. H. (2025). Generative Artificial Intelligence and Collaboration: Exploring Religious Human-Machine Communication and Tensions in Leadership Practices. Human-Machine Communication. https://doi.org/10.30658/hmc.11.9

  • Erdag, O. V. (2019). Stratejik Uyumlaşma Olgunluk Ölçeğinin Türkçeye Uyarlanması (Adaptation of Strategic Alignment Maturity Scale). Ömer Halisdemir Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi. https://doi.org/10.25287/ohuiibf.542171

  • Fareniuk, Y. (2023). Optimization of Media Strategy via Marketing Mix Modeling in Retailing. Ekonomika. https://doi.org/10.15388/Ekon.2023.102.1.1

  • Korneyev, M. (2022). Business marketing activities in Ukraine during wartime. Innovative Marketing. http://dx.doi.org/10.21511/im.18(3).2022.05

  • 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

  • Nkurunziza, G. (2018). Knowledge management, adaptability and business process reengineering performance in microfinance institutions. Knowledge and Performance Management. https://doi.org/10.21511/kpm.02(1).2018.06

  • Oprescu, C. (2024). Exploring the ESG Surge: A Systematic Review of ESG and CSR Dynamics. Review of International Comparative Management. https://doi.org/10.24818/rmci.2024.2.229

  • Palovski, J. (2020). Clinical and psychological characteristics of emotional burnout in business leaders. Science and Education a New Dimension. https://doi.org/10.31174/send-pp2020-239viii95-19

  • Scherf, M. (2021). Demut gegenüber der Fehlbarkeit des Handelns im Business-Coaching (Humility in the face of the fallibility of action in business coaching). Organisationsberatung, Supervision, Coaching. https://doi.org/10.1007/s11613-021-00725-4

  • Shwawreh (2025). The Role of Green Business Strategy in Enhancing Digital Marketing Strategy for Sustainable Business Intelligence. International Review of Management and Marketing. https://doi.org/10.32479/irmm.18287

  • Taskin, N. (2022). An Empirical Study on Strategic Alignment of Enterprise Systems. Acta Infologica. https://doi.org/10.26650/acin.1079619

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