Many companies' AI implementation projects lack intelligence.

• Whether companies possess the necessary skills and infrastructure to effectively invest in artificial intelligence remains a question as they continue to allocate substantial funds towards it.
• One of the largest obstacles to implementing AI projects is constructing a robust data infrastructure.
• Organizations often adopt a Whac-A-Mole approach to data infrastructure, addressing data issues on a project-by-project basis.

Whether businesses have the necessary infrastructure and skills to maximize their returns on AI investments as they continue to spend heavily on this technology remains a question.

AI has become the driving force behind innovation across various industries, according to Paul Pallath, vice president of applied AI at technology consulting firm Searce. AI can improve operational efficiency, provide insights, and discover new opportunities. However, many organizations face challenges in adopting AI, which can hinder their success.

To achieve real value from AI, businesses must overcome obstacles while establishing a strong foundation for sustainable integration, according to Pallath.

Building a robust data infrastructure is crucial for accelerating AI initiatives, according to a survey of 500 U.S. senior business leaders conducted by EY last year. The survey found that 83% of respondents said their organization's AI adoption would be faster if they had stronger data infrastructure in place. Additionally, two-thirds of the respondents admitted that a lack of infrastructure is hindering AI adoption at their companies.

Dan Diasio, global AI consulting leader at EY, emphasized the importance of strong data hygiene practices to maintain the accuracy, consistency, and reliability of data used in training and operating generative AI models.

Diasio stated that many organizations are adopting a Whac-A-Mole approach to data infrastructure, where they tackle data on a project-by-project basis and address issues as they arise.

To have a strong foundation for AI, companies must develop a unified enterprise strategy. Although it can be challenging to connect everything, it can lead to significant advantages. Once the data and knowledge base is established, it becomes a self-sustaining cycle. Greater context enhances all initiatives, and each new initiative becomes simpler to implement.

AI requires clean, correct, and accessible data to thrive, but most organizations struggle with messy, siloed, and unreliable information, leading to poor data quality.

To establish a strong data foundation, companies must implement robust data governance, including developing policies and standards to ensure data accuracy, consistency, and security across the organization. Additionally, they should break down silos by integrating disparate data sources into unified platforms, such as data lakehouse implementations leveraging data fabric architecture.

Investing in automation

Pallath advised investing in automation by using tools to clean, deduplicate, and validate data continuously. He emphasized that AI is only as smart as the data it's trained on, and clean data is crucial in building trust in an AI-driven world.

AI-related skills and cultural barriers to AI adoption remain challenges for IT leaders.

Pallath emphasized that while AI is advanced, it still requires human intervention for building, managing, and guiding it ethically. Additionally, a culture that fosters innovation is crucial, but many organizations face challenges in finding skilled professionals in areas such as machine learning, data engineering, and AI ethics. Moreover, cultural resistance to AI, fueled by fears of automation and job displacement, can hinder progress.

AI fatigue is a growing concern among senior business leaders, with half of them reporting a decline in company-wide enthusiasm for AI integration and adoption, according to EY research.

Diasio stated that although employees generally trust AI technologies, anxiety remains, particularly concerning job displacement and the fast pace of adoption.

Business leaders have intensified their focus on responsible AI in the past six months, with the aim of enhancing trust and maximizing the positive impact of AI across their organizations.

Pallath advised companies to invest in training programs to build AI literacy across teams, including technical skills, ethical implications, and responsible use of AI.

In addition, they should establish open communication channels.

"Openly discuss AI to address fears, misconceptions, and resistance, as Pallath suggests. Encourage transparency through channels that promote information sharing, feedback, and ongoing collaboration. Talent isn't always just a skill gap, it's a mindset gap. Foster a culture that embraces AI, not fears it."

Diasio emphasized the importance of leaders taking a holistic approach to AI adoption, considering its impact on people, processes, data, and technology, rather than just viewing it as a technology installation.