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Freedom251 › Blog › adult social care

AI Won’t Fix Social Care, But Could It Help Us Finally Make It Work?

By Freedom251 Team · · 6 min read

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AI cannot fix adult social care’s funding, workforce or access problems. It can help with particular tasks—such as spotting a possible fall or drafting documentation—but only when the tool is suitable, people can act on its output, and accountable staff remain involved. The practical test is whether it improves outcomes that matter to the person receiving care, not simply whether it produces an alert or saves time.

How is AI being used in adult social care?

“AI in social care” covers different tools with different purposes and risks. England’s Department of Health and Social Care (DHSC) describes uses including sensors, acoustic monitoring, chatbots, facial recognition, data analytics and generative AI for tasks such as drafting assessments and care plans. A system that drafts text is not equivalent to a sensor that alerts staff to a possible safety issue. DHSC’s guidance on AI in adult social care gives examples and implementation considerations.

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Monitoring and alerts

Sensors may detect movement or sound, turn on lighting when someone gets up at night, track vital signs, or flag gait changes that could indicate increased frailty risk. Acoustic monitoring may alert staff to a possible fall or disturbance and, in some circumstances, reduce the need for intrusive routine checks. The technology does not itself provide care: a person still has to assess an alert, respond appropriately and decide whether the care plan should change.

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Drafting and administration

Generative AI can help staff draft or organise records and assist with workload-heavy activities such as auditing, monitoring and logging. That may make some administrative work faster, but fluent writing is not evidence that a care plan reflects someone’s wishes, circumstances or needs. DHSC says, “Evidence that generative AI (artificial intelligence) can create truly personalised care plans is currently limited,” and its guidance calls for staff to review AI-assisted plans for accuracy.

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Decision support

Facial-analysis tools may contribute to pain assessment for someone who has difficulty communicating. DHSC describes a tool that calculates a score from facial movements as part of a broader caregiver observation process; it does not replace the caregiver’s judgement or response. More generally, an AI output should inform a decision only within a clearly defined process for checking and acting on it.

Can AI reduce falls in care settings?

A UK Parliament written answer on 5 June 2026 reported that emerging evidence from government-funded independent evaluations indicated AI-enabled technologies, including the Nobi Smart Lamp, could prevent falls and “long lies” in care settings by 37% to 49%. The answer also referred to people living at home longer, reduced hospital admissions and staff time. This is an emerging evaluation finding reported by government, not a guaranteed result for every lamp, provider or person. The settings, methods and implementation behind an evaluation matter when judging whether its result applies elsewhere. The answer said reports would be published on a rolling basis from May 2026; it does not establish that a particular technology will deliver the reported reduction in a different care setting. Read the written answer to parliamentary question HL150.

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For context, that same answer cited NICE figures of about 210,000 emergency hospital admissions in England related to falls among people aged 65 and over in 2022/23. That figure describes the scale of the falls issue; it does not show how many admissions an AI system would prevent. The answer also said the government intended to set new standards for care technologies and had commissioned NICE to develop an adult social care evidence standards framework. Those were policy intentions reported in June 2026, not confirmation here that the work has been completed.

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Why won’t AI fix the underlying problems?

Adult social care in England faces pressures involving funding, workforce and unmet need. An AI tool may help with a particular task or support an individual, but it cannot by itself provide funded care capacity, recruit staff or settle how care is commissioned and delivered. In a 2023 report, the National Institute for Health and Care Research (NIHR) cited more than 1.9 million requests for adult social care in England in 2019/20 and an estimate of 1.2 million older people in England with unmet care needs. These are historical figures, not 2026 counts. The report also cited a 2021 projection of an additional 627,000 social care staff—or 55% growth—over the following decade. That was a dated projection, not a measure of the current workforce gap. The NIHR report on home sensors with AI capabilities examines both potential benefits and implementation and evidence gaps.

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Nor does time saved automatically become more face-to-face care. DHSC presents that as a possible benefit, dependent on staffing, workflow and provider decisions. A provider would need to measure what happens to the time released rather than assume it has improved care.

Adoption can also be uneven. Research conducted in the first quarter of 2025 by Social Work England and Research in Practice found generative AI was the most commonly reported AI use among participating social workers and students, with potential efficiency gains in case recording and use of case data. The research also described uneven employer policies, concerns about uses involving people, limited AI understanding, data-quality and interoperability problems, and a lack of procurement or implementation expertise. Customised tools could be prohibitively expensive for some providers. This research concerns social work practice and education in England; it is not a representative audit of every adult social care provider. Read the Social Work England research.

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What should a provider check before adopting AI?

Compare a proposed tool with the care need and the way work is actually done. A procurement decision should cover more than features or a supplier’s headline claim.

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  1. Define the care problem. State the need the tool is meant to address and consider whether AI is necessary. Involve people drawing on care, carers and frontline staff in shaping that decision.
  2. Check relevant evidence. Ask whether independent evaluation measured outcomes that matter to people in a comparable population and setting. A plausible mechanism or promising pilot is not proof of system-wide effectiveness.
  3. Test the data and technical fit. Check whether records are accurate, representative and usable with existing systems. Poor-quality or incompatible data can undermine outputs and make a tool difficult to sustain.
  4. Set privacy and dignity protections. Explain what a sensor or other system collects, why it is collected, who can see it and how long it is kept. Consider consent, accessibility and how a person can raise concerns. Under DHSC guidance, do not enter personally identifiable information into a free or online tool unless the organisation has appropriate contractual protections and assurance about data use.
  5. Check fairness. Bias can enter through training data, algorithm design, or the way data is selected and labelled. Ask whose circumstances are represented, examine whether performance differs across relevant groups, and provide a way to challenge errors. Social Work England’s research found limited awareness among participants of bias in commercial models.
  6. Keep a human decision-maker in the process. Decide who checks outputs, what standard they use, how errors are corrected and what staff should do when the system misses an event or produces a questionable alert. For care plans drafted with AI, DHSC guidance calls for staff review.
  7. Assign responsibility and prepare staff. Set out what staff may rely on the tool for, who is accountable and how people can report problems or harm. Train staff before launch and establish a written policy, with support such as digital champions where appropriate. A February 2026 workshop report from the Professional Standards Authority noted, “The risk will differ for different AI.” Its discussion highlighted safety, bias, transparency and accountability; responsibility should not be left ambiguous between a professional, employer, developer and regulator. Read the Professional Standards Authority workshop report.
  8. Calculate whole-life cost and measure results. Include equipment, software, training, integration, maintenance, support and workflow changes—not just the licence. Set a baseline and success measures before deployment, then monitor intended and unintended effects and make changes when results fall short.

These considerations reflect DHSC’s advice on human review, consultation, data protection, organisational policy, cost and continuous improvement, alongside NIHR’s findings on involvement and sustainable implementation. DHSC’s guidance and the other implementation examples here concern England; arrangements elsewhere in the UK may differ.

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