These are the top 'soft skills' your business needs to succeed with AI

‘Soft skills’ are crucial in driving successful AI adoption projects, according to new research from Multiverse, but finding staff with these traits is easier said than done.

In a study of ‘AI power-users’, the digital skills company found analytical reasoning, communication skills, and ethical understanding ranked among the top ‘human skillsets’ required for AI adoption.

These, Multiverse noted, are equally important as technical skills such as prompt engineering, AI model evaluation, and AI process modelling.

Gary Eimerman, chief learning officer at Multiverse, said the research underlines the importance of a combined approach to AI adoption which combines both technical proficiency alongside broader skills development.

The focus on soft skills is by no means a new trend. Indeed, cultural change has traditionally been a key hurdle in digital transformation projects and an area tech leaders have struggled with over the last decade.

“Leaders are spending millions on AI tools, but their investment focus isn't going to succeed. They think it's a technology problem when it's really a human and technology problem,” he said.

“Without a deliberate focus on capabilities like analytical reasoning and creativity, as well as culture and behaviors, AI projects will never deliver up to their potential.

The study comes in the wake of similar research highlighting the impact of soft skills on AI-ready workforces, according to Multiverse. Analysis from Accenture, for example, found that companies that prioritize soft skills are nearly twice as likely to successfully adopt and integrate AI.

So, with soft skills now make or break for enterprises across a range of industries, what should tech leaders focus on?

The soft skills needed for AI success

Multiverse based its top soft skills on observational research with frequent AI users alongside its own learnings in upskilling workers in the use of the technology.

Based on these findings, the company has pulled together an array of human skills that are key to supporting technical AI adoption.

These skills, Multiverse said, form part of its broader skills taxonomy - “a hierarchical system mapping the skills required for success in the digital era”.

‘Cognitive skills’ were among the most important identified by the firm, with staff capable of reasoning and problem solving among the best-suited to support AI adoption projects.

These cognitive skills span three main areas:

• Analytical reasoning
• Creativity
• Systems thinking

Analytical reasoning skills, by Multiverse’s definition, allow staff to break down “complex information for AI to more effectively deliver its instructions” and help them recognize tasks that AI isn’t suitable for.

Systems thinking ties in closely with analytical reasoning, with staff possessing these specific skills capable of identifying particular patterns in AI performance to predict how the technology or tools will respond to certain tasks.

Creativity was also identified as a key soft skill. This is particularly important during the early stages of adoption, as creative-minded staff are more likely to be “pushing the boundaries of AI use” and experimenting with ways the technology can be used to deliver benefits for themselves and coworkers.

Responsible AI skills are vital

Elsewhere, staff with ‘responsible AI skills’ are vital for enterprises, Multiverse noted. Those with an understanding of the potential harms of the technology are more likely to apply “ethical principles to ensure the responsible use of AI” and consider its impact on individuals.

This includes “spotting bias and recognizing how it affects AI outcomes” as well as the ability to identify when AI outputs lack sufficient geographic or cultural awareness.

A recent study from Trustmarque warned about the risks of AI bias, particularly during development processes.

The study noted that enterprises are building and deploying AI models designed for traditional software processes and fail to account for risks such as model bias.

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