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AI Strategy: From Efficiency to Exponential Growth

By Dr. Dwi Suryanto
Date: February 23, 2026

Introduction

In the current global landscape, Artificial Intelligence (AI) has transitioned from a speculative luxury to a non-negotiable strategic imperative. As we navigate 2024-2026, the divide between “AI-native” organizations and legacy laggards is widening. This is no longer just about automating tasks; it is about redefining the very logic of value creation.

Consider a leading regional retail bank facing a 20% churn rate. While traditional methods struggled to identify at-risk customers, the implementation of a predictive AI model integrated directly into their CRM—allowed them to intervene 48 hours before a customer decided to leave. This isn’t just a technical upgrade; it is a fundamental shift in business resilience and customer intimacy.

In an era defined by geopolitical volatility and rapid technological shifts, the primary challenge for senior executives is not the technology itself, but the strategic alignment of that technology with the human capital capable of steering it.

Concepts and Theoretical Foundations

To master AI, boards must move beyond the “black box” mentality and embrace two core pillars:

  1. Strategic Alignment (SA): The degree to which an organization’s AI systems support its business goals. Research by Taşkın (2022) and Tarawneh (2019) emphasizes that without a clear bridge between software objectives and business clarity, technology becomes a cost center rather than a profit driver.

  2. Innovative Leadership Theory: Drawing from Zelienková (2022), this framework suggests that AI adoption fails in “command-and-control” environments. It requires leaders who view AI as an augmentative partner, fostering a culture where experimentation is decoupled from the fear of failure.

Evidence and Synthesis: The AI Value Chain

Recent evidence suggests that the impact of AI is most profound when applied across three distinct themes:

  • Operational Agility & Resilience: In high-stakes environments, AI serves as an adaptive shield. Korneyev (2022) demonstrates how businesses in volatile regions (e.g., Ukraine) utilized AI-driven marketing and operations to maintain viability during crises. This proves that AI is a critical tool for business continuity.

  • Precision Marketing & ROI: The “scattergun” approach to marketing is dead. Fareniuk (2023) found that AI-optimized marketing mix modeling can boost retail campaign effectiveness by up to 15%. Furthermore, Awad (2025) notes that in the banking sector, data-driven AI significantly enhances Return on Investment (ROI) by personalizing customer journeys at scale.

  • The “Green” Competitive Advantage: Sustainability is no longer a peripheral ESG concern. Shwawreh (2025) and Pranata (2025) argue that integrating AI into “Green Business Strategies” creates a dual benefit: reducing environmental footprint while simultaneously strengthening brand loyalty among Gen Z and Millennial consumers.

Current Data, Trends, and Policies (2024–2026)

  • Macro Impact: According to McKinsey & Company (2024), generative AI is estimated to add between $2.6 trillion and $4.4 trillion annually to the global economy.

  • Upskilling Urgency: The OECD (2024) reports that approximately 27% of jobs in member countries rely on skills that could be easily automated, highlighting a massive demand for executive-level AI literacy.

  • Adoption Rates: World Bank (2025) data indicates that while 70% of large enterprises have “piloted” AI, fewer than 20% have successfully scaled it across their global supply chains due to a lack of leadership readiness.

Cause–Effect Patterns

The logic of successful AI integration follows a clear mechanism:

Strategic Alignment (Taşkın, 2022) → Innovative Leadership (Zelienková, 2022) → AI-Driven Data Precision (Awad, 2025) → Operational & Sustainable Resilience (Shwawreh, 2025) → Sustained Competitive Advantage.

In short: Strategic clarity determines technical utility.

Cross-Domain Insights

To truly understand AI, we must look at Complexity Theory. Just as biological ecosystems rely on rapid feedback loops to survive environmental changes, a modern business uses AI as its “sensory nervous system.” If your AI (the sensors) is not aligned with your Strategy (the brain), the organization suffers from “strategic dissonance,” leading to wasted capital and missed market windows.

Practical Recommendations

For CEOs and Founders:

  • Move Beyond the Pilot: Stop treating AI as a series of experiments. Move toward a “Platform-First” strategy where AI is integrated into the core enterprise system (Taşkın, 2022).

  • Audit Your Leadership: Ensure your top tier possesses the “Transformational Leadership” qualities needed to navigate VUCA (Volatility, Uncertainty, Complexity, and Ambiguity) environments (Noviyanti, 2025).

For Middle Managers:

  • Master the Marketing Mix: Utilize AI-driven modeling to optimize resource allocation across digital channels. Small gains in efficiency (10-15%) compound into massive annual growth (Fareniuk, 2023).

  • Foster Knowledge Management: Ensure that AI tools are used to capture and institutionalize “tacit knowledge” so that the organization learns as the machine learns (Nkurunziza, 2018).

For Policymakers:

  • Incentivize “Green” AI: Develop frameworks that reward companies using AI for sustainability and green marketing, as these firms show higher long-term viability (Pranata, 2025).

Conclusion

AI is not a destination; it is a discipline. The organizations that will dominate the late 2020s are those that recognize AI as a leadership challenge, not just a technical one. Success requires a synthesis of strategic alignment, innovative leadership, and a commitment to data-driven sustainability.

To bridge this gap, Borobudur Training & Consulting offers exclusive AI Leadership & Strategy Training designed for senior executives. For organizations ready to transition from theory to execution, we also provide specialized Business Consulting Services to tailor AI implementation to your specific corporate DNA.

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. Available at: https://doi.org/10.32479/irmm.19738

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

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

  • McKinsey & Company (2024). The Economic Potential of Generative AI: The Next Productivity Frontier. [External Data Enrichment].

  • Noviyanti, A. (2025). The Role of Transformational Leadership in Adaptive Business Strategy Implementation in the VUCA Era. SIMBA. Available at: 10.63985/simba.v1i1.9

  • OECD (2024). Employment Outlook 2024: Artificial Intelligence and the Labour Market. [External Data Enrichment].

  • Pranata, S. (2025). Peningkatan Kesadaran dan Implementasi Green Marketing bagi UMKM. Aspirasi Masyarakat. Available at: 10.71154/f1ntkf73

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

  • Taşkın, N. (2022). An Empirical Study on Strategic Alignment of Enterprise Systems. Acta Infologica. Available at: 10.26650/acin.1079619

  • Zelienková, A. (2022). What Theories Explain Entrepreneurship as Compared to Innovative Leadership? Acta Academica Karviniensia. Available at: 10.25142/aak.2022.019 

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