Businesses trust AI for strategic decisions, but adaptation struggles persist

Although AI’s technical capabilities are advancing quickly, the research shows that the biggest obstacles to implementation come not from technology but from organizational structures, human behavior and internal culture. A majority of respondents agreed that companies face more organizational barriers than technological ones, indicating that the main challenges originate from within the workforce rather than from AI tools themselves.

Artificial intelligence is rapidly changing the way modern enterprises make strategic and operational decisions, replacing manual judgment with streamlined, data-driven processes while exposing deep human and organizational barriers that continue to slow adoption. The findings of a new study reveal a growing divide between the technological capabilities of AI systems and the readiness of organizations to adapt their structures, leadership practices and employee skillsets.

The study, “The Impact of Artificial Intelligence on Enterprise Decision-Making Processes,” published in the European Research Studies Journal, evaluates AI adoption across 92 companies representing trade, services, manufacturing, IT and finance sectors.

AI rapidly expands across industries as companies shift toward data-driven decisions

The study shows that AI is no longer a fringe innovation but a core operational component for most businesses. According to survey responses, 93 percent of companies have already implemented AI, confirming widespread integration across both large and small enterprises. Organizations primarily use AI in four areas: customer service, data analysis and forecasting, management decision support and process automation. Customer-facing functions accounted for the highest level of adoption, demonstrating how interaction-focused tasks increasingly rely on automated systems for efficiency and responsiveness.

The dominance of AI in customer service reflects its ability to manage high-volume communication while supporting quicker resolutions, consistent decision patterns and personalization. Meanwhile, the growing share of AI use in predictive analytics and managerial decision support underscores the push toward evidence-based decision-making in competitive markets. Companies also use AI to automate routine tasks, where algorithmic efficiency enables managers to reallocate human labor to more strategic activities.

The study identifies AI as a catalyst for improved decision speed and accuracy. Eighty-five percent of respondents stated that AI noticeably improves the decision-making process, signaling widespread recognition of its benefits in processing information, identifying trends and reducing the risk of errors that stem from human judgment. Enterprises reported that AI facilitates decision tasks by accelerating data interpretation, simplifying complex analyses and offering targeted recommendations that support more confident managerial choices. These improvements have contributed to faster operational workflows and greater strategic responsiveness.

Beyond efficiency, many companies expressed growing confidence in AI's capability to take over strategic decisions. Approximately 85 percent believed AI could serve as an alternative to human decision-makers in certain contexts. This belief reflects a shifting mindset in which algorithmic intelligence is perceived as reliable for tasks involving high-volume calculations, pattern detection and consistency across decisions. Even so, respondents still emphasized the importance of human oversight for interpreting AI outputs and ensuring alignment with broader organizational goals.

The study confirms the rise of hybrid decision-making models that merge the analytical strengths of AI with the experiential insight of managers. This approach reduces cognitive bias and supports more balanced decisions that combine machine-derived logic with human understanding of context.

Organizational barriers emerge as the primary obstacle to wider AI implementation

Although AI’s technical capabilities are advancing quickly, the research shows that the biggest obstacles to implementation come not from technology but from organizational structures, human behavior and internal culture. A majority of respondents agreed that companies face more organizational barriers than technological ones, indicating that the main challenges originate from within the workforce rather than from AI tools themselves.

Employee resistance to new technology was cited as the most common barrier, reported by 49 percent of companies. Many employees struggled to adapt to AI-based workflows, with over 90 percent experiencing some degree of difficulty during adoption. These challenges often stem from unfamiliarity with algorithmic tools, concerns about increased performance expectations and discomfort with system-driven decision pathways. The study highlights that adapting employees to AI tools is one of the most significant hurdles businesses encounter during digital transformation.

High implementation costs posed additional constraints for 25 percent of organizations, reflecting ongoing financial challenges in acquiring AI infrastructure, securing specialist support and maintaining system upgrades. Regulatory ambiguity was noted by 24 percent, underscoring uncertainty regarding data privacy, algorithm transparency and operational compliance.

The study also identifies the most significant skill gaps hindering AI adoption. Nearly 49 percent of respondents highlighted understanding how AI and algorithms work as the most essential competence, suggesting a need for stronger technological literacy among staff. Change management skills ranked closely behind at 44.6 percent, indicating the necessity of leadership that can guide organizations through structural shifts, communicate effectively and foster a culture receptive to technological change. Surprisingly, only 6.5 percent identified programming or data-analysis skills as mandatory, revealing that the greater challenge lies not in coding capabilities but in conceptual understanding and managerial adaptation.

The most notable employee difficulty involved the creation of appropriate prompts, experienced by 62 percent of respondents. As companies increasingly adopt generative AI models, the ability to formulate precise instructions becomes crucial for achieving effective results. Issues related to AI acceptance and tool operation each affected 16.3 percent of users. These findings reinforce that AI effectiveness depends just as much on employee proficiency and mindset as on the systems themselves.

Notably, none of the respondents identified job loss as a major risk, despite global public concerns regarding automation. Instead, concerns centered on interpreting AI decisions, operational costs and potential data misuse. These results indicate a shift in perception, where AI is seen more as an enabler than a competitor to human roles.

AI adoption strongly correlates with better decision efficiency and employee acceptance

The study analyses correlations between AI implementation and broader organizational outcomes. The report shows a strong positive correlation between the level of AI implementation and both decision-making efficiency and employee acceptance of AI. Companies with deeper AI integration experienced better decision processes and higher levels of employee openness toward automation.

Conversely, negative correlations emerged between AI implementation and organizational barriers as well as employee adaptation difficulties. This trend suggests that as companies mature in their AI usage, the structural and behavioral challenges associated with integration begin to diminish. Experienced organizations are more effective at designing communication strategies, managing transitions and aligning AI tools with business needs.

These correlations reveal that successful AI adoption is part of a cycle: early challenges are significant, but as organizations become more AI-capable, the benefits intensify while the obstacles decline. This gradual improvement reflects an internal learning curve in which continuous exposure to AI technologies builds familiarity, confidence and competence among both leaders and employees.

AI’s impact extends far beyond decision processing. It also enhances communication flows, strengthens leadership adaptability, streamlines internal coordination and contributes to stronger organizational agility. AI tools support feedback mechanisms, clarify information channels and offer predictive insights that help leaders anticipate employee needs and operational risks.

The authors argue that effective AI adoption requires combining algorithmic intelligence with human-centered leadership. Managers must be capable of interpreting AI insights, guiding teams through change and fostering a culture of trust and collaboration. AI thus becomes a complementary force that amplifies human strengths rather than replacing them.