The researchers have found 13 essential human skillsets – including creativity, analytical reasoning, and systems thinking – are needed for the workforce to effectively embrace AI. These sit alongside technical abilities such as prompt engineering, AI model evaluation, and AI process modelling, forming a foundation for uniting human potential with technological capability to drive value.

These conclusions are drawn from qualitative and observational research involving AI power-users, as well as experience gained from upskilling thousands of professionals in AI tools. The resulting skills framework is designed to support workers and organisations aiming to improve their AI maturity – the ability to generate meaningful outcomes using AI.

Accenture predicts that AI could contribute £736 billion to UK GDP by 2038, but also notes that leading organisations are nearly twice as likely to emphasise ‘soft skills’. A mismatch between the potential of AI and the human expertise needed to wield it effectively could pose a significant risk to the UK’s future productivity and growth.

“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. Without a deliberate focus on capabilities like analytical reasoning and creativity, as well as culture and behaviours, AI projects will never deliver up to their potential," said Gary Eimerman, Chief Learning Officer, Multiverse. "This framework provides a new model for talent development in the age of AI, which must include human skills as well as technical skills in order to drive tangible business results.”

Focusing on the requirements for effective collaboration between humans and AI, 13 human skills have been identified as critical to support technical AI adoption. These form part of Multiverse’s broader skills taxonomy, a hierarchical system mapping the skills required for success in the digital era.

The most essential human skills identified for meaningful AI adoption are:

Cognitive skills: Mental abilities used for learning, reasoning, problem-solving, and decision-making.

1. Analytical reasoning: breaking down complex information for AI to more effectively deliver its instructions; recognising tasks that AI is not suitable for

2. Creativity: pushing the boundaries of AI use and experimenting with new approaches to drive innovation

3. Systems thinking: identifying patterns in AI performance to predict how AI will respond to a task

Responsible AI skills: applying ethical principles to ensure the responsible use of AI, considering its impact on individuals and society.

4. AI ethics: spotting bias and recognising how it affects AI outcomes; using AI outputs in an ethically sound way to inform business recommendations

5. Cultural sensitivity: identifying when AI outputs lack sufficient geographic or cultural awareness

Self-management skills: recognising thoughts, values, feelings, and behaviours, and how they impact the ability to achieve objectives when using AI.

6. Curiosity: examining the broader context and requirements of a task to augment AI outputs

7. Self-regulated learning: reflecting on the success of a chosen AI approach; partnering with AI to self-assess its outputs

8. Detail orientation: fact checking AI for hallucinations and errors; using one’s own domain expertise to ensure accuracy

9. Adaptability: iterating and refining one’s approach to interacting with AI based on the quality of outputs

10. Determination: patience and willingness to continue trialling new approaches with AI, even during unsuccessful AI interactions

Communication skills: Strong interpersonal skills which support the optimisation of AI outputs

11. Empathy: treating AI as an extension of one’s own mind and thoughts; anthropomorphising AI to create more thoughtful, receptive, and intentional dialogue

12. Tailoring communication: discerning whether AI output has the desired tone for a particular audience or situation, and refining prompts if it is not

13. Exchanging feedback: using AI to proactively seek feedback on work

“We need to start looking beyond technical skills and think about the human skills that the workforce must hone to get the best out of AI,” said Imogen Stanley, Senior Learning Scientist at Multiverse, who led the development of the skills taxonomy. “What we found during our first principles research phase was that skills like ethical oversight, output verification, and creative experimentation are the real differentiators of power AI users. By developing these specific skills, employees can move from being passive users of AI to active drivers of innovation and value.”

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