Corporate AI: 5 Implementation Barriers
- Interest in artificial intelligence (AI) is surging, but companies face critically importent hurdles in successful AI adoption.
- Poor data quality is a primary reason AI projects fall short, according to a Lenovo and IDC survey of 2,920 IT and business decision-makers.
- Satishy Mutu Krishnan, chief facts, data and digital officer at Ally Financial, advocates breaking down silos and emphasizing data governance.
Companies face crucial hurdles in successful AI adoption. Overcoming these obstacles is key to unlocking the full value of AI-based systems. This vital piece examines five major implementation barriers: poor data quality, internal expertise gaps, lack of clear business cases, outdated legacy systems, and employee resistance. Analyze the importance of data governance, the need for AI-related education, and how to build compelling business cases. Uncover why legacy systems can hinder progress and ways to address employee concerns. Such as, generative AI can explore and improve data accuracy. Overcome these corporate AI barriers,and streamline your workflow. learn from experts, and see how they address the main problems. News Directory 3 can provide deep dives of details on this topic.
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AI Adoption: Overcoming Data, Expertise and legacy System Hurdles
Table of Contents
Interest in artificial intelligence (AI) is surging, but companies face critically importent hurdles in successful AI adoption. IT leaders are working to identify and overcome these obstacles to unlock the full value of AI-based systems.
Low Data Quality
Poor data quality is a primary reason AI projects fall short, according to a Lenovo and IDC survey of 2,920 IT and business decision-makers. One-third of respondents plan to bolster data management capabilities to address this.
Satishy Mutu Krishnan, chief facts, data and digital officer at Ally Financial, advocates breaking down silos and emphasizing data governance. He said AI relies heavily on data processing, necessitating careful management of related challenges and risks. Ally Financial has integrated 98% of its data into a centralized, cloud-native database.
John Thompson, senior vice president at the Hackett Group, said data quality problems are realistic obstacles to AI implementation. He suggests using generative AI to actively explore data and identify inaccuracies, rather than attempting to purify data beforehand. “Generative AI is a tool that identifies the parts that need to be improved,” Thompson said.
Lack of Internal Expertise
A March 2025 American Management Association survey revealed that 57% of 1,100 North american experts feel they are not keeping pace with AI.Only 49% had AI-related education.
Mutu Krishnan advises organizations to establish education and training infrastructure so employees understand AI’s capabilities and limitations. He called AI one of the biggest technological innovations, but integrating it into daily work poses a significant change management challenge.
Thompson recommends starting with current teams, practicing test cases, and applying AI to personal projects. SAP America’s Chief AI Officer Jared Coil said organizations should deploy internal experts who can identify appropriate AI use cases, as well as external talent with experience using AI in other organizations.
Lack of Clear Business cases
IT leaders need convincing business cases to secure resources for AI projects.Chandra Benkataramani, chief information officer of TaskUs, said one of the biggest tasks is finding a business case that creates measurable value without adding unneeded complexity.
Benkataramani said it’s easy to get swept up in the trend of generative AI, but real success comes from focusing on areas that improve productivity, strengthen decision-making, and simplify core workflows. TaskUs operates AI projects as an enterprise-wide duty, helping business leaders understand the return on investment.
Thompson advises against spending too long on use cases, saying it’s crucial to start now to make a difference.
Old Legacy Systems
Outdated legacy systems can hinder AI success. Applications designed to store restricted data may not integrate with the latest AI tools.
SAP America’s Coil warns that simply adding the latest LLM or Lake House technology is not a guaranteed fix.Fred Cook, co-founder and CTO of Veho, said a full reorganization was needed to maximize AI tools.Veho rebuilt its core systems to integrate AI applications more easily, leading to faster AI experimentation.
High costs associated with IT modernization, such as system integration and customized software advancement, also pose a barrier.
Employee Resistance
A 2025 Workplace Intelligence survey found that 31% of knowledge workers intentionally interfered with their institution’s AI projects.
Orla Daily, chief information officer at Skillsoft, said this interference can neutralize AI initiatives. She said it frequently enough stems from anxiety about job loss, misunderstandings of AI benefits, and resistance to change. Organizations should address these concerns, create a continuous learning culture, and actively involve employees in AI implementation.
Daily said effective leadership is crucial, requiring not only technical capabilities but also dialog skills. When employees see clear uses and achievements of AI, fear turns into interest and curiosity.