Gen AI Rules, But Companies See Problems

Generative AI is easily the most widely used AI tool, but companies still struggle to assimilate the technology into workplace cultures.

There’s no doubt about it: Generative AI is the most commonly used AI solution, and it’s not even close.

A new survey from the business analytics firm Gartner backs that point up.

In talking to 644 executives in the U.S., U.K., and Germany, Gartner reported 29% of respondents said they have “deployed and are using Gen AI,” which makes the technology far and away the most widely used artificial intelligence tool in worldwide businesses. Gen AI bests a group of artificial intelligence, including graph techniques, optimization algorithms, rule-based systems, natural language processing, and other types of machine learning in usage quantities, Gartner stated.

While Gen AI dominates the business landscape in 2024, companies are using the technology for multiple tasks.

Gartner reported utilizing GenAI embedded in existing applications (such as Microsoft’s Copilot for 365 or Adobe Firefly as the most widely deployed use case. Companies are also regularly customizing GenAI models with prompt engineering, training, or fine-tuning bespoke GenAI models, or they are using standalone GenAI tools, like ChatGPT or Gemini, the survey noted.

“GenAI is acting as a catalyst for the expansion of AI in the enterprise,” said Leiner Ramos, a senior 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.”

Obstacles Persist

On the downside, Gartner found that company executives are still vexed over user limitations that cut into Gen AI’s productivity and cost savings.

Approximately 50% of senior managers whose companies are using AI say they’re having trouble “estimating and demonstrating the value of AI projects,” Gartner noted. Key areas of concern include talent shortages, technical difficulties, data-related problems, lack of business alignment and overall trust in AI among employees, business partners and customers. All of the above are concerns to at least 40% of corporate users.

That’s a big issue, as Gartner points out; only 48% of Gen AI projects make it into production. Additionally, it takes eight months to go from AI prototype to production.

“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.”

Companies struggling to derive business value from AI can learn from mature AI organizations,” Ramos said. “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,” he stated.

Yet only 9% of organizations are “AI mature,” yet that’s enough to give other enterprises some Cliff Notes on maximizing the use of Gen AI technologies. Gartner said these four “foundational capabilities” should be part of any Gen AI rollout strategy.

• 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 in upskilling and change management across the wider organization.
• A focus on trust, risk, and security management (TRiSM) capabilities to mitigate the risks 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.

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