As artificial intelligence continues to transform business operations across industries, organizations are increasingly looking to harness its power. According to Gartner, 59% of CEOs believe AI will be the most impactful technology for their industries over the next three years. However, the path to successful AI implementation is fraught with obstacles. This report examines the five most significant challenges organizations encounter when adopting AI technologies, providing insights into how these barriers manifest and potential strategies to overcome them.

1. Data Quality and Management Issues

The foundation of any effective AI system lies in its data, making data quality and management perhaps the most fundamental challenge organizations face in their AI journey. AI models require accurate, relevant, and comprehensive data to intelligently automate tasks, make predictions, and drive business decisions. Without quality data, AI systems produce misleading results that can misdirect an organization’s efforts.

Many companies struggle with data that is either insufficient or overwhelming in volume. In numerous cases, this data contains inherent biases that can significantly skew AI outcomes and compromise decision-making processes. Moreover, ensuring data relevance across different departments and functions adds another layer of complexity to the implementation process.

A related critical challenge involves integrating data from various sources and formats. Businesses typically operate with data silos where information is stored in incompatible systems, increasing the difficulty of data consolidation and analysis. The diverse formats and structures of data across an organization further complicate this process, requiring sophisticated tools and strategies to harmonize the information for AI consumption.

2. Lack of Knowledge and Skills

Despite AI being a hot topic of conversation, it remains new territory for many businesses. A significant barrier to adoption is simply a lack of AI expertise. Many organizations don’t know how to get started, which tools to use, or how to integrate AI into existing decision-making processes. This knowledge gap was reported as the second-highest barrier to adoption, cited by 44% of respondents in a recent survey.

The knowledge deficit becomes even more pronounced as businesses move further into the AI adoption process. For example, when rolling out more complex applications like AI agents, 54% of businesses cited lack of knowledge as their top obstacle. As one respondent explained, “The scope is so wide, it’s overwhelming to businesses”.

The impact of this challenge varies significantly across different organization types. Smaller companies feel these knowledge barriers particularly acutely. For example, 30% of small businesses want to use AI but haven’t figured out which tools or strategies to use, while only 12% of large enterprises report the same struggle. Similarly, there are substantial differences across industries, with tech-forward sectors progressing much faster than traditional industries like transportation, logistics, and manufacturing, where 17-19% of organizations report having plans to implement AI but haven’t yet identified which tools to use.

Internal Skills Gap

Within organizations, the distribution of AI literacy varies widely across departments. IT teams typically use AI at significantly higher rates than other departments, likely due to better AI literacy and natural curiosity about technologies like machine learning. This creates an internal imbalance that can hamper organization-wide adoption efforts. That notwithstanding, there are still many IT teams that are also struggling to understand the varied AI tools and help their organizations formulate a strategy. 

3. AI Trustworthiness and Output Quality

As organizations deploy AI systems, ensuring the reliability and accuracy of AI outputs emerges as a critical concern. AI’s susceptibility to hallucinations remains a significant issue. Hallucinations occur when AI produces convincing but factually incorrect outputs. As one industry observation notes, “All AI answers are hallucinations, but sometimes they happen to be right”.

While minor inaccuracies might be tolerable in customer service recommendations or non-critical applications, errors in mission-critical operations, such as manufacturing, finance, or healthcare, can lead to costly disruptions, safety hazards, or even loss of life. This concern is reflected in organizational practices. In one survey, 27% of respondents whose organizations use generative AI say employees review all content created by AI before it is used, while a similar percentage check 20% or less of AI-produced content before deployment.

To mitigate these risks, enterprises are increasingly moving beyond black-box AI models toward implementing AI orchestration layers. These layers define explicit rules, guardrails, and governance mechanisms that ensure AI decisions are explainable, trackable, and aligned with business objectives. Unlike traditional AI reliability measures focused on predictability, an orchestration-driven approach provides real-time visibility into AI decision-making and allows for immediate course correction when needed.

4. Data Privacy and Security Concerns

As AI systems assume greater responsibilities within enterprises, they require access to vast amounts of sensitive data, creating significant privacy and security challenges. Traditional cloud-based AI architectures, where data is transmitted to off-premise AI services for processing, pose substantial privacy risks, particularly in industries like healthcare, finance, and government.

Organizations handling sensitive information cannot afford to expose critical data to external systems, regardless of claimed security measures. This challenge is compounded by the fact that only 35% of AI capabilities will be built by internal IT teams according to Gartner research, forcing CIOs to develop new approaches to managing and protecting data access while governing AI inputs and outputs.

Many organizations are ramping up efforts to mitigate AI-related risks. Compared to early 2024, more organizations are actively managing risks related to inaccuracy, cybersecurity, and intellectual property infringement. These are three of the AI-related risks most commonly reported to have caused negative consequences. Larger organizations tend to be more proactive in this area, with respondents from these companies much more likely to report managing potential cybersecurity and privacy risks.

5. Cost Management

While AI promises significant long-term benefits, managing the costs associated with implementation and operation represents a major challenge. Over 90% of CIOs report that managing costs limits their ability to extract value from AI for their enterprise. This issue is exacerbated by the difficulty in accurately forecasting AI-related expenses.

Gartner estimates that organizations may experience a staggering 500% to 1,000% error in their cost calculations if CIOs fail to understand how their generative AI costs scale. This dramatic miscalculation potential creates budget uncertainty that can derail AI initiatives or lead to unexpected operational expenses that undermine the technology’s ROI.

The challenge of cost management extends beyond simple implementation expenses to include:

1. Ongoing operational costs for AI systems
2. Expenses related to acquiring and preparing quality data
3. Costs associated with hiring or training specialized talent
4. Expenses for monitoring and governance systems
5. Integration costs with existing infrastructure

As organizations move from pilot projects to enterprise-wide implementation, managing these costs becomes increasingly complex and critical to success.

Overcoming These Challenges

As AI adoption accelerates across industries, organizations must navigate these five key challenges (data quality and management, knowledge and skills gaps, AI trustworthiness, data privacy and security, and cost management) to realize the technology’s full potential. While the complexity of these challenges varies based on organization size, industry, and existing technical capabilities, incorporating the following actions into your organizational strategy will go a long way.

1. Invest in Data Strategy and Infrastructure

• Develop a comprehensive data governance framework to ensure data quality, consistency, and accessibility across departments.
• Implement tools for data integration and cleaning to break down silos and prepare high-quality datasets for AI models.
• Continuously monitor and audit data for biases to improve AI fairness and accuracy.

2. Build AI Expertise and Foster a Learning Culture

• Provide targeted training programs and workshops to upskill employees in AI concepts, tools, and ethical considerations.
• Hire or collaborate with AI specialists to guide strategy and implementation, especially for complex projects.
• Encourage cross-functional teams to share knowledge and align AI initiatives with business goals.

3. Establish Robust Governance and Cost Management Practices

• Create clear policies for AI ethics, privacy, and security to build trust and ensure regulatory compliance.
• Implement AI orchestration layers and monitoring systems to maintain transparency and control over AI outputs.
• Develop detailed cost models and ROI frameworks to forecast expenses accurately and justify investments in AI projects.

Wrapping Up

Successfully implementing AI requires a strategic approach that addresses each of these areas concurrently. By addressing these challenges systematically, organizations can move beyond the hype cycle to achieve meaningful business value from their AI investments.