The 5 biggest AI adoption challenges for 2025

5 years ago, IBM’s Rob Thomas and Paul Zikopoulos built a framework for successful artificial intelligence (AI) adoption called the AI Ladder, a “unified, prescriptive approach to help [leaders] understand and accelerate the AI journey.” The framework became a book, which was teased with a hook that now seems rather quaint:

“Everybody’s talking about AI. Why? Well, we believe AI presents a tremendous opportunity for businesses of every size across any industry.”

Considering the AI landscape today, it’s funny to imagine a world where Rob and Paul felt the need to persuade readers that AI was going to be a big deal. Also notable are the “rungs” of the ladder: modernize, collect, organize, analyze and infuse.

Back in 2020, there were numerous organizations that hadn’t even begun to lift their foot onto that first rung. Just 5 short years later, you don’t need a McKinsey report to tell you that AI is the future.

Virtually every organization can embrace AI to one degree or another. New technology advancements have made it easier to accomplish AI integrations that produce immediate return on investment (ROI).

A lack of enthusiasm for AI is never a question of uncertainty about AI’s potential but about how to do it right.

The IBM Institute of Business Value released a report that revealed some interesting data around AI adoption, namely, the obstacles that are still preventing organizations from making headway with generative artificial intelligence (gen AI). 

Nearly half of respondents indicated concern about data accuracy or bias. Business leaders can overcome such concerns by prioritizing governance, transparency and AI ethics.

AI governance is essential for reaching a state of compliance, trust and efficiency in developing and applying AI technologies. Effective AI governance includes oversight mechanisms that address risks such as bias, privacy infringement and misuse while fostering innovation and building trust. 

Strong governance structures, such as ethical AI committees and compliance with regulatory frameworks, help maintain accountability and responsible AI deployment.

AI ethics is a multidisciplinary field that studies how to optimize AI's beneficial impact while reducing risks and adverse outcomes. AI ethics encompasses data responsibility and privacy, fairness, explainability, robustness, transparency and other ethical considerations.

Fairness checks and other corrective measures fall under AI ethics and help to ensure that AI outputs are reliable and equitable.

AI transparency helps people access information to better understand how an AI solution was created and how it makes decisions. Researchers sometimes describe AI as a “black box,” as it can still be difficult to explain, manage and regulate AI outcomes due to the technology’s increasing complexity. AI transparency helps open this black box to better understand AI outcomes.

About 42% of respondents said that they felt their organizations lacked access to sufficient proprietary data. Enterprises can overcome the significant challenge of insufficient high-quality data for customizing gen AI models by using a combination of data augmentation, synthetic data generation and strategic data partnerships.

One effective approach is to enhance existing datasets through augmentation techniques such as paraphrasing, translation or adding noise to increase diversity without collecting entirely new data.

Synthetic data created artificially through computer simulation or generated by AI algorithms can take the place of real-world data. The data can be used as an alternative or supplement to real-world data when it is not readily available.

Another key strategy is forming strategic partnerships and participating in industry-wide data-sharing initiatives. Collaborating with noncompeting companies, research institutions or consortia enables businesses to access larger and more diverse datasets while avoiding ethical concerns and adhering to legal standards.

Federated learning, where models are trained across decentralized data sources without sharing raw data, is another way to benefit from external data while maintaining security and compliance.

Gen AI is still new, but enterprises can address inadequate gen AI expertise by investing in talent development, strategic partnerships and accessible AI tools.

One of the most effective approaches is to upskill existing employees through specialized training programs, workshops and certifications in AI and machine learning (ML). Providing hands-on experience with AI tools and fostering a culture of continuous learning helps bridge the skills gap internally.

In addition to developing in-house expertise, companies can collaborate with AI vendors, research institutions and consulting firms to gain access to specialized knowledge.

Partnering with AI startups or technology providers enables businesses to use external expertise without needing to build everything from scratch. Participating in the open source ecosystem can also provide valuable insights and prebuilt models that reduce the complexity of implementing an AI strategy.

Another solution is to adopt low-code or no-code AI platforms that allow employees with limited technical backgrounds to work with gen AI. These tools simplify AI deployment and customization, making it easier for enterprises to integrate AI into their workflows without requiring deep expertise.

Companies should approach making a financial justification for exploring gen AI initiatives by focusing on cost savings, revenue growth, competitive advantage and risk mitigation.

They need to identify specific use cases where gen AI capabilities can drive efficiency, such as automating business processes, generating marketing content or accelerating digital transformation.

By quantifying the benefits of AI—such as reduced labor costs from supply chain automation, faster time-to-market or improved customer engagement—companies can estimate the ROI.

Businesses should also consider the full potential for new revenue streams, such as AI-powered product offerings, personalized customer experiences or real-time decision-making. Starting with small, low-risk pilot projects can provide tangible results that justify further investment.

Risk assessment plays a role in financial justification as well. Organizations must weigh the cost of inaction, including losing market share to AI-driven competitors or inefficiencies that AI projects could resolve.

Privacy concerns remain a major barrier to gen AI implementation. Again, data governance and responsible AI principles play a role. A crucial first step is to limit the exposure of sensitive data by using data management techniques such as anonymization, differential privacy and encryption before feeding information into AI models.

This reduces the risk of exposing personally identifiable information (PII) or proprietary business data. Enterprises should also help ensure that AI systems follow strict access controls and auditing mechanisms to track who interacts with the data and how it is used.

Federated learning can be an effective approach, allowing AI models to be trained across multiple decentralized datasets without moving the data itself, thus preserving privacy.

Regulatory compliance is another key factor. Businesses must align their AI usage with global data privacy laws such as GDPR, CCPA and industry-specific regulations. Conducting regular privacy impact assessments and maintaining clear documentation on how AI applications handle data can help enterprises stay compliant and build customers' trust. 

On the bright side, many organizations are well on their way to handling these challenges:

• 80% of respondents have a separate part of their risk function dedicated to risks associated with AI or gen AI.

• 81% conduct regular risk assessments to identify potential security threats introduced by gen AI.

• 78% maintain robust documentation to enhance the explainability of how gen AI models work and were trained.

• 76% establish clear organizational structures, policies and processes for gen AI governance.

• 72% develop policies and procedures for managing data and addressing potential risks.

Overcoming common challenges to AI adoption requires a holistic approach that includes not just AI development teams but stakeholders from across technology, finance, security and legal departments. However, considering how quickly the technology is moving, the best time for stragglers to get started is today.