Generative AI now the most frequently deployed AI solution in organisations

The survey also found that utilizing GenAI embedded in existing applications (such as Microsoft’s Copilot for 365 or Adobe Firefly) is the top way to fulfill GenAI use cases, with 34% of respondents saying this is their primary method of using GenAI. This was found to be more common than other options such as customizing GenAI models with prompt engineering (25%), training or fine-tuning bespoke GenAI models (21%), or using standalone GenAI tools, like ChatGPT or Gemini (19%).

“GenAI is acting as a catalyst for the expansion of AI in the enterprise,” said Leinar Ramos, Sr Director Analyst at Gartner. “This creates a window of opportunity for AI leaders, but also a test on whether they will be able to capitalize on this moment and deliver value at scale.”

The primary obstacle to AI adoption, as reported by 49% of survey participants, is the difficulty in estimating and demonstrating the value of AI projects. This issue surpasses other barriers such as talent shortages, technical difficulties, data-related problems, lack of business alignment and trust in AI (see Figure 1).

“Business value continues to be a challenge for organizations when it comes to AI,” said Ramos. “As organizations scale AI, they need to consider the total cost of ownership of their projects, as well as the wide spectrum of benefits beyond productivity improvement.”

“GenAI has increased the degree of AI adoption throughout the business and made topics like AI upskilling and AI governance much more important,” said Ramos. “GenAI is forcing organizations to mature their AI capabilities.”

“Organizations who are struggling to derive business value from AI can learn from mature AI organizations,” said Ramos. “These are organizations that are applying AI more widely across different business units and processes, deploying many more use cases that stay longer in production.”

The survey found 9% of organizations are currently AI-mature and found that what makes these organizations different is that they focus on four foundational capabilities:

A scalable AI operating model, balancing centralized and distributed capabilities.
A focus on AI engineering, designing a systematic way of building and deploying AI projects into production.
An investment on upskilling and change management across the wider organization.
A focus on trust, risk and security management (TRiSM) capabilities to mitigate the risks that come from AI implementations and drive better business outcomes.

“AI-mature organizations invest in foundational capabilities that will remain relevant regardless of what happens tomorrow in the world of AI, and that allows them to scale their AI deployments efficiently and safely,” said Ramos.

Focusing on these foundational capabilities can help organizations mature and alleviate the current challenge of bringing AI projects to production. The survey found that, on average, only 48% of AI projects make it into production, and it takes 8 months to go from AI prototype to production.

Photo by Solen Feyissa on Unsplash