01/2025 The Path to AI Leadership: Insights for Modern Organizations (2024)
This article is part of a series of columns on artificial intelligence originally published throughout 2025 in Mreža magazine. Written from the intersection of psychology, business, and emerging technologies, these reflections aim to provide both practical insights and critical perspectives on the rapid evolution of AI and its broader social, economic, and ethical implications.
Each column captures a moment in time, while contributing to an ongoing conversation about how we understand, implement, and adapt to artificial intelligence — individually, organizationally, and collectively.
Artificial Intelligence (AI) has evolved from a popular buzzword into a key pillar of modern business strategy. Organizations across industries are leveraging AI to boost productivity, drive innovation, and create new revenue streams. As AI systems become more sophisticated, it’s essential to understand best practices and strategies for successful adoption and scaling. This article connects recent research and analysis to explore how businesses can harness the potential of AI while navigating the challenges it brings.
Different Levels of AI Maturity
AI maturity levels vary widely, with companies falling into three categories: leaders, optimizers, and beginners. Leaders — representing about 15–26% of organizations — are ahead in AI adoption. They report significant revenue growth, often exceeding 25%, and successfully integrate AI to improve productivity, customer experience, marketing effectiveness, and security. Optimizers focus on improving internal processes, while beginners are still exploring AI’s potential. These figures confirm some of my own predictions from earlier this year.
For leaders, the path to AI success begins with a clearly defined strategy that aligns business goals with AI capabilities. These organizations invest in robust data infrastructure and use hybrid clouds to scale efficiently. On the other hand, beginners often prioritize cost-cutting measures — such as automating routine tasks — before progressing to more complex AI-driven innovations.
AI’s Sector-Specific Impact
AI’s impact spans a range of industries, each focusing on unique applications:
- Finance: AI-powered virtual assistants and advanced search tools improve efficiency and customer service.
- Retail: AI enhances user experience through personalized recommendations and seamless purchasing journeys.
- Manufacturing: AI automates IT operations and optimizes production processes.
- Telecom: AI is used for IT automation and internal tools.
That’s why I’ve written throughout this year about various use cases and the importance of developing original AI-driven ideas.
High-Value Use Cases & Leadership Alignment
Success depends on identifying high-value applications. AI leaders prioritize implementations that deliver tangible value — like improved CX or enhanced security — over quick, low-impact fixes.
Adopting AI isn’t just a technical challenge — it requires visionary leadership and change management. Strong leadership ensures organizational alignment, with decision-makers across departments sharing a unified vision of AI’s role. Leaders excel at storytelling and change management, fostering employee enthusiasm and support. They avoid the “perfection trap” — where companies get stuck endlessly iterating AI projects — and instead choose iterative progress and continuous improvement.
Governance, Risk, and Trust
Effective governance ensures ethical and trustworthy AI use. Standards like ISO/IEC 42001 offer frameworks for responsible AI system management. Regular risk assessments and transparent communication — about cybersecurity risks or algorithmic bias — build stakeholder trust. Leading companies emphasize transparency, explainability, and resilience in their AI systems to address reliability and fairness concerns.
The Role of Data Infrastructure
Data is the lifeblood of AI, and managing it effectively is key to meaningful results. Leading companies use various tools to ensure easy access to high-quality data — including labeling tools, synthetic data generation, and governance frameworks. These efforts help tailor AI applications for maximum value, especially in areas like marketing and customer experience that rely on personalization.
For beginners, data readiness remains a major hurdle. Only 19% of organizations report being fully prepared to leverage their data for AI initiatives. By adopting multimodal approaches and fine-tuning models with proprietary data, companies can improve accuracy, efficiency, and competitive positioning.
Fairness, Explainability, and External Deployment
Transparency and fairness become especially important as AI is scaled for external use. Explainable AI models that let users understand decision-making processes are crucial for building trust. Companies must also address concerns over bias and workforce displacement — two major risks frequently cited by enterprises.
People & Competencies: The Human Side of AI
Successful AI adoption hinges on a skilled and engaged workforce. Leading organizations invest heavily in training, on-the-job experience, and education to equip employees with the necessary expertise. This proactive approach ensures that employees not only understand AI but also embrace its transformative potential. In leading companies, 74% of employees report high levels of AI competency, compared to just 10% in beginner organizations. These statistics reflect my experience working with companies and sectors in Croatia and the broader region where I’ve conducted numerous AI trainings.
The Discipline of Iteration
As AI continues to evolve, organizations must balance ambition with pragmatism. The most successful companies approach AI as a discipline requiring continuous learning and adaptation. By focusing on iterative progress, strong governance, and a clear vision, businesses can unlock AI’s full potential while managing its risks.
The path to AI maturity is not without its challenges — from data readiness to ethical concerns, organizations must navigate a complex landscape. But the rewards — increased efficiency, innovation, and new revenue opportunities — make the effort worthwhile.
As AI becomes a core part of business strategy, companies that lead will set the standard for innovation and success in the intelligence era. The topic of specific outcomes achieved by top AI performers may be addressed in one of my upcoming columns.
