Gartner: Difficulty in assessing and demonstrating the value of AI projects is the biggest barrier to AI adoption

Generative artificial intelligence (GenAI) is the number one AI solution deployed in organizations, according to a new Gartner survey.

According to a survey conducted in the fourth quarter of 2023, 29% of 644 respondents from the United States, Germany, and the United Kingdom said they have deployed and are using GenAI, making GenAI the most frequently deployed AI solution. GenAI was found to be more commonly deployed than other solutions, such as graph technology, optimization algorithms, rule-based systems, natural language processing, and other types of machine learning.

The survey also found that embedding GenAI into existing applications is the preferred way to implement GenAI use cases, with 34% of respondents saying this is their primary method of using GenAI. The study found that this is more common than other options, such as using just-in-time engineering custom GenAI models (25%), training or fine-tuning custom GenAI models (21%), or using standalone GenAI tools such as ChatGPT or Gemini (19%).

Demonstrating AI value is the biggest barrier to adoption

49% of respondents said the main barrier to AI adoption is the difficulty in assessing and demonstrating the value of AI projects. This issue surpassed other barriers such as talent shortages, technical difficulties, data-related issues, lack of business alignment and trust in AI.

Learning from AI-mature organizations

The survey found that 9% of organizations are currently mature in AI technology and found that these organizations differ in that they focus on four basic capabilities:

• A scalable AI operating model that balances centralized and distributed capabilities.
• Focus on AI engineering and design a systematic way to build and deploy AI projects.
• Invest in upskilling and change management across the wider organization.
• Focus on Trust, Risk, and Security Management (TRiSM) capabilities to mitigate risks from AI implementation and drive better business outcomes.

Focusing on these foundational capabilities can help organizations mature and mitigate the current challenges of getting AI projects into production. The survey found that on average only 48% of AI projects make it to production, and it takes eight months to go from AI prototype to production.