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Generative AI emerges as top choice for organizational solutions

Generative artificial intelligence is the number one AI solution deployed in organisations, according to a survey by research and consulting firm Gartner, Inc.

As per the survey conducted in the fourth quarter of 2023, 29% of respondents from organisations said they have deployed and are using gen AI, making gen AI the most frequently deployed AI solution. Gen AI was found to be more common than other solutions like graph techniques, optimisation algorithms, rule-based systems, natural language processing and other types of machine learning.

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

“Gen AI is acting as a catalyst for the expansion of AI in the enterprise,” said Leinar Ramos, senior director analyst at Gartner. “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 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 (42%), technical difficulties (40%), data-related problems (39%), lack of business alignment (39%) and trust in AI (39%).

The business value, with regard to AI, continues to be a challenge for organisations. As organisations scale AI, they need to consider the total cost of ownership of their projects, as well as benefits beyond productivity improvement.

The survey found 9% of organisations are currently AI-mature and 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.

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

“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,” Ramos said.

Focusing on these foundational capabilities can help organisations 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 eight months to go from AI prototype to production. NDTV Profit

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