19th August 2026
Defining a clear mission for artificial intelligence in social care
Stef Lunn, social work practice lead, Civica
Just like millions of people in the UK, I am an informal carer. My Dad needs help with some day-to-day tasks and I am glad that I can be his main supporter. I am also a registered social worker, so I have a good understanding of the care system. For the last eight years, I’ve been working in care technology, exploring how digital case management can make social workers’ lives easier. Of course, AI in social care is a current hot topic.
These three worlds of caring, social work and technology came together recently when I was offered the chance to try out a new AI-enabled carers assessment tool designed to support carers earlier in their journey. I was intrigued to see how it would work for me. The AI assistant called at the time I had requested and asked me several questions. I didn't really know what to expect, but it was an unexpectedly positive experience. In conversation with a person, I would tend to filter information for the avoidance of any judgement. But as the assessor wasn’t a person, it just felt a little bit more freeing, enabling me to be more direct about our situation and my feelings.
With my social worker’s hat on, I was satisfied with the follow-up guidance and support that has been offered. And from a technology user’s point of view, I was pleased with the overall user experience.
This is a very specific product, targeted at offering earlier support to carers who would otherwise be just waiting for an allocated worker. What marks this out from much of the debate is that clear mission.
What should AI achieve in social care?
As a sector, we don’t yet know where we should be heading with AI. Within local authorities, AI policies are often in need of development and stronger AI governance. But even before refining operational policy, organisations need a clear mission.
We often hear the vague stated aim to the effect of “let's do it quicker, let's do it better”. From a social worker productivity standpoint “let’s do it quicker” is measurable. For example, the length of time spent writing assessments and completing social work administration can be reduced by X-amount. As a sector we are less clear about what ‘better’ looks like.
Part of the problem is that we’re lacking robust research into the views, needs and preferences of citizens who will use and hopefully benefit from these AI tools.
Keeping relationship-based social work at the centre
We know that people value having a relationship with their social worker. They value the opportunity to build that understanding with a consistent worker. That seems a sensible, ethical guiding principle for AI in social work. With the current focus on ‘test and learn’ approaches in AI, this might be a benchmark for ‘better’ to sit alongside those measurements of speed.
There is a fear among social workers that if efficiency savings demonstrate someone can generate their reports and recordings twice as fast, then they’re just going to end up with double the workload, rather than more time with people. That is what we should be trying to avoid.
Using AI to create more time for people
This approach is encapsulated by the British Association of Social Workers’ 80:20 campaign. People are drawn into social work because they want to make a difference to people's lives, but then they spend 80 percent of their time on social work administration. Their work on AI has focused on using efficiencies to reduce administrative burden and support relationship-based practice.
If AI in social care is the answer, then it should be deployed with the purpose of unlocking more time for relationship-based social work. It should be informed by citizens and supported by a framework for responsible AI in social care.
Without setting a clear, unified mission for AI deployment in local authorities that works for both citizens and service providers, it’s going to be much more challenging to ensure that the sector can keep pace with the technology in an appropriate way.
A registered social worker, Stef is an expert in digital transformation for social care. She sits on the 80:20 steering group and special interest AI group for the British Association of Social Workers and has also served as a specialist advisor to the Care Quality Commission for Adult Social Care.
