Gartner: Few organisations have mature AI processes

Survey results show that estimating and delivering value from AI projects are among the biggest challenges organisations face

The use of artificial intelligence (AI) is spreading rapidly in organisations, the latest survey from analyst Gartner has reported. However, the majority of organisations polled admit that their AI processes are not mature.

The survey, based on a poll of 644 organisations, found that the percentage of respondents whose organisations are applying AI in multiple business processes has increased from 13% in 2021 to 28% this year.

Leinar Ramos, senior director analyst at Gartner, said: “This means that AI is evolving from something just performed in certain islands in the organisation to a much more widespread activity.” 

Almost half (49%) of the organisations polled said that their biggest challenge was estimating and delivering value with AI-based initiatives. Gartner identifies five business outcomes that AI initiatives can support, namely: revenue growth, cost optimisation, risk reduction, customer experience and employee productivity improvement.

According to Ramos, the survey results show that organisations need to develop foundational AI capabilities to tackle the challenges that come from deploying the technology at scale. This involves balancing AI projects and initiatives across the broader business.

However, Gartner’s survey found that less than a tenth (9%) of the organisations polled said they had mature processes in place for AI. A focus on AI is among the attributes of those organisations that identify as having mature AI processes, said Ramos, which means they have a systematic way of building and deploying AI projects into production, including monitoring AI models and a change management programme.

“We found that organisations that performed changed management activities more frequently tend to have AI initiatives that have a greater impact on business outcomes,” he said.

Ramos said that there is a clear difference between those organisations that claim their data is ready for AI versus the ones that said their data is not AI-ready, adding: “You need to prioritise data when you’re working on AI.”

He noted that there is a misconception, especially with generative AI (GenAI), that organisations do not need to worry about clean data since they start with pre-trained models that have already been trained using a lot of data.

“For the most valuable use cases, you need some sort of data source going into these models. Having your data AI-ready enables organisations to generate good business outcomes with AI,” added Ramos.

The survey found that utilising GenAI embedded in existing applications (such as Microsoft’s Copilot for 365 or Adobe Firefly) is the top way to fulfil 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 customising GenAI models with prompt engineering (25%), training or fine-tuning bespoke GenAI models (21%), or using standalone GenAI tools such as ChatGPT or Gemini (19%).

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

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