AI Strategy: From Tactical Tool to Competitive Edge

By : Dr.Dwi Suryanto, MM., Ph.D.
February 6, 2026

Introduction

In the current global economic landscape, the “wait-and-see” approach to Artificial Intelligence (AI) has officially expired. We are witnessing a transition from AI as a novel experiment to AI as a fundamental pillar of corporate strategy. As the IMF (2024) recently noted, nearly 40% of global employment is exposed to AI, presenting a paradox: it is a significant risk to the unprepared and a massive multiplier for the visionary.

Consider a Tier-1 retail executive I recently advised. Despite having mountains of customer data, their decision-making remained reactive. They were “data-rich but insight-poor.” By integrating an AI Business Analyst framework, they shifted from analyzing what happened last quarter to predicting what customers would demand next month. This is not just a technological shift; it is a strategic evolution.

Concepts and Theoretical Foundations

The integration of AI into business analysis is anchored in the theory of Strategic Alignment. As Taşkın (2022) posits, the efficacy of enterprise systems is not found in the software itself, but in the harmony between technological capabilities and organizational objectives.

In the boardroom, this means moving beyond “efficiency” and toward Transformational Leadership in a VUCA (Volatile, Uncertain, Complex, Ambiguous) environment. The modern AI Business Analyst serves as the bridge between academic data science and strategic execution, ensuring that AI initiatives drive measurable business value rather than becoming expensive “vanity projects.”

Evidence and Synthesis

Recent research highlights three critical dimensions where AI-driven analysis reshapes the enterprise:

Operational Excellence and Efficiency: Research by Abdelrehim Awad (2025) demonstrates that AI-integrated marketing in the banking sector can increase campaign effectiveness by up to 30%. This efficiency is achieved by shifting from broad-brush strategies to precision-targeted, data-driven interventions.
Sustainability and ESG Integration: AI is no longer just for profit; it is for purpose. Shwawreh (2025) and Iva Gregurec (2025) emphasize that digital marketing strategies integrated with AI enhance “Green Business Intelligence,” allowing firms to measure and optimize their ESG (Environmental, Social, and Governance) impact with unprecedented accuracy.
Resilience in Crisis: Evidence from Maxim Korneyev (2022) regarding business activities during the Ukrainian conflict shows that firms using adaptive, data-informed models maintain higher resilience. AI allows for real-time pivots when traditional five-year plans become obsolete overnight.

Current Data and Global Trends (2023–2025)

Productivity Gains: McKinsey (2023) estimates that Generative AI could add the equivalent of $2.6 trillion to $4.4 trillion annually across various industries.
Adoption Rates: According to the OECD (2024), AI investment in G7 nations has grown by 25% year-on-year, with a specific focus on “Decision Support Systems” rather than just automation.
Labor Shift: The World Bank (2024) reports a surging demand for “Human-AI Collaboration” roles, where the ability to interpret AI output is more valued than the ability to code the AI itself.

Cause–Effect Patterns

To understand the impact of AI on your organization, observe these strategic mechanisms:

Strategic Alignment → Enhanced Data Accuracy → Superior Decision Quality
AI-Driven Sustainability → Higher ESG Ratings → Increased Institutional Investment
Transformational Leadership → Reduced Employee Burnout (via AI automation of drudgery) → Higher Innovation Output

Cross-Domain Insights

The logic of AI integration mirrors Supply Chain Resilience. Just as a “Just-in-Time” supply chain requires perfect synchronization between nodes, a “Just-in-Time Intelligence” system requires the AI Business Analyst to synchronize data flows with executive decision cycles. Furthermore, from the lens of Psychological Safety, leaders who use AI to augment not replace human talent foster a culture of “Co-opetition” where employees leverage AI to reach their peak performance (Scherf, 2021).

Practical Recommendations

For CEOs and Board Members:

Stop viewing AI as an IT expense. Reclassify it as a “Strategic Capability.”
Audit your organization’s Strategic Alignment Maturity (Erdag, 2019) to ensure your AI tools actually serve your 2026 growth targets.

For Middle Managers:

Focus on “Translation Skills.” Your value lies in translating boardroom problems into data queries and AI outputs into actionable tactics.
Implement AI-driven “Marketing Mix Modeling” (Fareniuk, 2023) to optimize budget allocation in real-time.

For Policymakers:

Promote frameworks that encourage “Green Marketing” and sustainable AI use to ensure long-term national competitiveness (Pranata, 2025).

Conclusion

The AI Business Analyst is the new architect of the modern enterprise. However, the technology is only as effective as the strategy guiding it. To lead in this era, executives must blend the precision of machine learning with the wisdom of experienced leadership.

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References

Abdelrehim Awad (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
Anis Noviyanti (2025). The Role of Transformational Leadership in Adaptive Business Strategy Implementation in the VUCA Era. SIMBA. 10.63985/simba.v1i1.9
IMF (2024). Gen-AI: Artificial Intelligence and the Future of Work. Washington, DC.
Iva Gregurec (2025). Sustainable Digital Marketing: A Systematic Review and Content Analysis. DIEM. 10.17818/DIEM/2025/1.5
McKinsey & Company (2023). The economic potential of generative AI: The next productivity frontier.
Nazım Taşkın (2022). An Empirical Study on Strategic Alignment of Enterprise Systems. Acta Infologica. 10.26650/acin.1079619
OECD (2024). AI and the Labour Market: 2024 Outlook. OECD Publishing, Paris.
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
Yana Fareniuk (2023). Optimization of Media Strategy via Marketing Mix Modeling in Retailing. Ekonomika. https://doi.org/10.15388/Ekon.2023.102.1.1