Artificial intelligence has become an increasingly common tool in modern organizations. Businesses now rely on AI to analyze large datasets, identify trends, forecast future scenarios, and support strategic planning across finance, marketing, operations, and human resources. While AI significantly improves the speed and scale of data analysis, many organizations continue to face an important challenge: ensuring that algorithmic recommendations are interpreted within the broader organizational and human context before decisions are made.
According to Lunamasa, organizational decision-making should not depend solely on technological capability. Instead, successful AI adoption requires managers who can critically evaluate algorithmic outputs, understand organizational realities, and balance technological insights with ethical responsibility. This perspective positions AI as a decision-support system rather than a replacement for human leadership.
To explore how managers interact with AI during strategic decision-making, the research employed a descriptive qualitative design. The study involved 18 managers from organizations that had already integrated AI into managerial functions such as operations, finance, marketing, customer service, strategic planning, and human resource management. Participants had at least two years of managerial experience and worked in organizations that had used AI for a minimum of six months.
Researchers conducted semi-structured interviews lasting approximately 45 to 60 minutes and analyzed the responses using thematic analysis. Rather than measuring numerical relationships, the study focused on identifying recurring patterns in how managers interpret AI recommendations, validate information, collaborate with colleagues, and make final strategic decisions.
The analysis identified five major factors that shape strategic sensemaking in AI-assisted organizational decision-making.
1. Data literacy is the foundation of effective AI use.
Sixteen of the eighteen managers emphasized that understanding data quality, interpreting dashboards, and evaluating information sources are essential before accepting AI-generated recommendations. Managers reported that they rarely rely on AI outputs without first examining where the underlying data originated and whether it accurately reflects business conditions.
2. Managerial experience remains indispensable.
Fifteen participants stated that professional experience allows them to recognize organizational realities that AI cannot fully capture. Factors such as organizational culture, employee readiness, customer behavior, and local business conditions often require contextual judgment beyond algorithmic analysis.
3. Human validation is the most critical step.
Seventeen of the eighteen managers said AI recommendations are never implemented automatically. Instead, they compare AI outputs with historical data, consult relevant departments, and discuss recommendations internally before making strategic decisions. This process ensures that decisions remain accurate, relevant, and accountable.
4. Cross-functional collaboration strengthens decision quality.
Managers reported that AI-assisted decisions frequently involve collaboration among business leaders, IT specialists, data analysts, operational teams, and senior executives. Each group contributes different expertise, allowing organizations to better evaluate AI recommendations from both technical and business perspectives.
5. Ethics and accountability cannot be delegated to AI.
Thirteen participants expressed concerns regarding algorithmic bias, privacy protection, transparency, and organizational responsibility. Managers consistently emphasized that while AI can recommend actions, humans remain accountable for every strategic decision affecting employees, customers, and stakeholders.
Based on these findings, the study outlines a four-stage model of managerial strategic sensemaking. Managers first examine AI-generated information, then interpret its relevance within the organizational context. Next, they validate recommendations through experience, comparative evidence, and collaborative discussion before making decisions that incorporate business objectives, ethical considerations, organizational risks, and accountability. This framework demonstrates that AI accelerates analysis but does not replace strategic human judgment.
The research also found that AI improves organizational decision quality in three important ways. First, it accelerates access to information, allowing organizations to respond more quickly to emerging issues. Second, it strengthens analytical capability by identifying patterns and generating predictions from historical data. Third, AI reduces reliance on subjective assumptions by providing evidence-based recommendations. However, these benefits materialize only when managers possess sufficient data literacy and critically evaluate AI-generated outputs before implementation.
An important insight emerging from the study is that organizations should view AI as an augmentation technology rather than a replacement for human expertise. Junus Johanis Lunamasa of Universitas Sepuluh Nopember Papua concludes that AI expands managerial decision-making capacity by combining technological capability with strategic reasoning, organizational experience, collaboration, and ethical responsibility. In other words, the final quality of organizational decisions depends on how effectively managers interpret and apply AI recommendations not on AI alone.
The findings carry significant implications for businesses, policymakers, and educational institutions. Companies investing in AI should simultaneously strengthen managers' data literacy, develop transparent AI governance frameworks, establish procedures for validating algorithmic recommendations, and encourage collaboration between technical and managerial teams. Such investments will help organizations maximize AI's benefits while maintaining accountability and public trust.
The author also acknowledges several limitations. Because the research involved only eighteen managers using a qualitative approach, the findings should not be generalized to every industry or organization. Future studies are recommended to include larger samples, multiple industries, and mixed-method research designs to better understand how AI maturity, organizational context, and managerial capabilities influence strategic decision-making.
Author Profile
Junus Johanis Lunamasa is a researcher from Universitas Sepuluh Nopember Papua whose academic work focuses on strategic management, organizational behavior, digital transformation, and artificial intelligence in organizational decision-making. Through this study, he emphasizes that the future of AI-driven organizations depends not on replacing managers, but on strengthening human judgment through responsible and ethical technology adoption.
Source
Article: Understanding Strategic Sensemaking of Managers in Artificial Intelligence Driven Organizational Decision Making
Journal: Formosa Journal of Science and Technology (FJST)
Publication: Vol. 5, No. 7, 2026
Author: Junus Johanis Lunamasa, Universitas Sepuluh Nopember Papua
0 Komentar