Partnering with technology: Why human-AI teaming matters for social care

The conversation surrounding artificial intelligence (AI) in the workplace has had a fundamental shift. Rather than viewing technology as a tool designed to replace human workers, research is increasingly focusing on pro-worker AI and Human-AI teaming (Acemoglu et al. 2026). Instead of using technology to automate human expertise into obsolescence, Human-AI teaming aims to act as a 'force multiplier'. It expands what social care professionals can achieve by complementing human skills with AI capabilities.

What is human-AI complementarity?

One study shows that as AI is adopted, the core goal must shift from simple automation to complementarity (Gonzalez et al. 2026). The goal of human-AI teaming is to recognise the different strengths of humans and AI and combine them to mitigate weaknesses.

In social care, where empathy, values, intuition, and contextual understanding are essential, AI cannot serve as an autonomous replacement. However, AI excels at tasks that humans often struggle with. For example, processing large amounts of unstructured data, recognising patterns, and managing information rapidly.

Complementarity happens when a human-AI team achieves better outcomes together than either the human or the AI could achieve on their own.

The Complementarity Framework in social care

Grounded in three foundational cognitive processes—reasoning, memory, and attention—along with the cross-cutting theme of meta-coordination and governance. Some examples include:

• Reasoning: AI excels at detecting patterns in very large datasets. Humans excel at looking at deriving meaning from what they see. A teaming strategy might mean AI acts as the pattern hunter and spots correlations. The human makes sense of the patterns to determine if they are meaningful.

• Memory: AI has can access and recall massive datasets. Human memory is 'lossy' and biased, but it is highly associative. A teaming strategy might mean, AI serves up the information, while the human provides the context. The human will decide which of those facts are relevant to the current problem.

• Attention: AI can maintain ‘attention’ across thousands of variables simultaneously without getting tired or bored. Human attention is narrow and easily exhausted, but it is deep. A teaming strategy might mean the AI monitors the noise and surfaces only the 1% of data that requires human judgment. This allows the human to move from searching for problems to solving problems.

• Meta-coordination and governance: AI is good at executing structured processes and procedures reliably. It can help monitor workflows and coordinate activity. Humans can make decisions with norms, ethics, and other important human traits. Humans can be inconsistent with applying governance. A teaming strategy might include deciding which tasks humans and AI do better and clearly set this out in policy, guidance, training and support.

Keys to success

Simply combining humans and AI does not guarantee better results; poorly designed interactions can lead to worse performance than working alone. Research identifies three critical factors for effective teaming:

• Balanced trust: Practitioners should avoid too much or too little trust in AI. They should avoid rejecting useful AI advice and blindly accepting AI outputs. Interfaces that display AI confidence levels and underlying reasoning help workers gauge when to trust or scrutinise recommendations.

• Critical thinking: Teams perform best when practitioners actively question, test, and interrogate AI. Avoid passively accepting them. Critical thinking remains the best safeguard against system errors or hallucinations.

• Matching tasks to strengths: Be clear on who performs the task best. Use the Framework to understand where humans and AI excel. Provide clear guidelines on who should perform each task and how work will be checked. Use Human-AI teams to offset each other's limitations. While AI excels at structured, highly predictable tasks, humans retain a decisive advantage in open-ended, emotional, and contextual decision-making.

Empowering the workforce

Human-AI teaming offers a practical blueprint for collective intelligence in social care. By designing AI systems that respect human expertise, handle administrative burdens, and support complex decision-making, care providers can build hybrid teams where technology directly elevates human capabilities and improves outcomes.