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Less than half of AI projects make it into production

Difficulties in estimating the value of AI projects is the primary obstacle to using it. 49% of companies say difficulty in estimating and demonstrating the value of AI projects is the reason they don’t use it, a survey from market research firm Gartner shows. GenAI is the No. 1 type of AI solution deployed. 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.

“Business value continues to be a challenge for organisations when it comes to AI,” says Gartner analyst Leinar Ramos. 

“As organisations 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.” 

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29% of the 644 respondents in the US, Germany and the UK said that they are using GenAI, making GenAI the most frequently deployed AI solution. GenAI is more common than other solutions like graph techniques, optimization algorithms, rule-based systems, natural language processing and other types of machine learning.

“The survey also found that utilising GenAI embedded in existing applications is the top way to fulfill GenAI use cases, with 34% of respondents saying this is their primary method of using GenAI”, the survey says. 

“This was found to be more common than other options such as customising 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,” Ramos says.

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“This creates a window of opportunity for AI leaders, but also a test on whether they will be able to capitalise on this moment and deliver value at scale.”
The survey found 9% of organisations are currently AI-mature and what makes these organisations different is that they focus on four foundational capabilities:

  • A scalable AI operating model, balancing centralised 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 organisation.
  • 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 organisations 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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