Using AI to Achieve Outstanding Care: What Care Leaders Told Us at the Care Show
AI is the biggest conversation in social care right now, but the gap between hype and practical adoption is still wide. At the Care Show on 8 October 2026, OneTouch Group Chief Technology Officer Seán Morris chaired a panel of care leaders to close that gap: Mason Barendrecht (Director of IT, AI & Systems, Hales Group), Steve Sinnott (Group Head of Quality and Compliance, Alcedo Care), Louise Cobban (Franchise Owner, Guardian Angel Carers) and Liz Easthope (Operations Manager, Surecare Community Services Ltd).
They didn’t agree on everything, which made it worth hearing.
Key takeaways
- AI’s clearest value today is in data analysis and admin: a day’s analysis can be done in minutes.
- AI informs decisions; it doesn’t make them. Accountability stays with people.
- Start with a problem, not a tool, and fix your processes and data first.
- Put a written AI policy in place now, because staff are already using AI.
- Bring carers with you by positioning AI as support, not replacement.
Will AI replace carers?
The panel’s answer was no, though not without debate. Louise Cobban described AI as “a new workforce” that elevates current teams rather than replacing them, taking on “the donkey work”.
Liz Easthope brought a valuable counterbalance. She sees a real place for AI in data analysis and spotting trends, and is focused on making sure it protects the person-centred contact and intuition carers rely on: “that feeling when somebody’s not right”. Her example of a complainant that had used ChatGPT to write a complaint, the team used AI to draft the reply, and it became “AI against AI”, with neither side backing down and no resolution. It was a useful reminder that resolving concerns still needs a human touch.
The exchange that followed captured a question every provider will face: does taking admin away free carers up, or take away part of how they learn?
Mason Barendrecht made the case that AI gives people time back with clients, to “do what humans do best”. Liz pointed out that admin is part of learning: working through a safeguarding report step by step helps to keep her own judgement sharp. Seán noted that finding that balance is something the whole sector will need to navigate.
His way of looking at it: the introduction of power tools didn’t turn everyone into a carpenter, but they made skilled carpenters faster. AI can do the same for carers, amplifying skills that can’t be replaced.
Where AI helps most in care right now
Steve Sinnott sees the biggest opportunity in prediction: getting ahead of risks, not just reviewing them after the fact. Audits and inspections review what has already happened; AI could help predict risks from patterns hidden in small incidents or nuances in care notes.
He shared a real-life example. He analysed nine months of quality reviews, with personal data redacted, a job that would normally take him half a day. AI produced a report in 15 seconds, and an hour of human checking showed it was largely accurate on themes and trends. With that time saved, quality teams can review incidents, complaints and care notes for patterns far more often, and AI can surface details the human eye might miss at the end of a long day.
Mason called this “a golden age in tech”, comparable to the move from paper to digital “times 10”, and stressed the value is in freeing time from admin and reporting, not flashy demos.
Louise pointed to a less obvious benefit: career progression. As a business owner, she is committed to internal promotion and likes to see her care delivery team move up. But a carer who has spent ten years in the field may never have had to write a professional email or a report. AI can help close that gap.
A newly promoted team member can set out their thoughts and let AI help shape them into something polished, learning along the way until they can do it themselves. “We’re not expecting everyone to be at that level straight away,” she said, but AI can help guide people “to reach new potentials”.
Where should the saved time go? With a major care worker shortage, Mason argued for fewer people on admin, more delivering care, and better pay. Steve added that lower back-office overheads could free up budget for complex nursing and frontline delivery.
Who is accountable when AI is involved?
“AI doesn’t replace the accountable person’s role,” said Steve. “It can inform them to make a decision, but it shouldn’t be making that decision for them.”
If AI flags a client with repeated falls or medication incidents, it should present that as a potential risk. It shouldn’t make the safeguarding referral itself. That decision belongs to the accountable person, who understands the nuances of that client and the local thresholds in a way AI doesn’t. AI supports the accountable person; it doesn’t replace them.
Steve also warned that AI makes mistakes. Review the notes yourself, compare your findings with the AI’s, and build confidence before relying on it.
How to start: a practical framework
- Start with the problem. “What AI is right for you and your company, maybe isn’t right for me and mine,” said Louise. Ask your teams what isn’t working, then whether AI can help. If it needs a person, bring in a person.
- Work backwards from the outcome. Mason advised defining the outcome you want the technology to achieve, then working backwards from there.
Treat it as a scoping exercise first. Rather than adding AI on top of how things work today, be ready to reimagine the process or workflow entirely, or “rip it up and start again” if that’s what the outcome needs. - Get governance and data right. For Mason, AI governance builds on the data governance you should already have: clean data, restricted access, and clear processes, policies and storage. Skip that step and AI is just “going to make it bad faster”. Seán summed up the order: process first, then systems, then AI.
- Write an AI policy now. Your staff are probably already using AI. “People do use it. I see it all the time,” said Steve. Without a written policy, that’s hard to manage.
Set one out, explain why data security matters, and tell staff never to put names, addresses, phone numbers or anything that identifies a person into AI tools outside your own systems, as that could be a data breach. - Feed it properly. AI “is not your quick fix,” said Louise. It needs your policies, procedures and information to produce good results.
- Prioritise payback. Steve recommended starting where AI frees up the most resources, such as reviewing care notes for concerns and trends.
Bring your teams with you
Louise’s message for frontline staff: AI is “your shadow, not your manager”. Carers are at their best with clients, not writing notes in the car or finishing an audit at 11pm.
Louise also showed what that extra capacity could look like: a care coordinator who schedules 500 hours of care could manage 1,000 hours if rotas went out automatically from templates. Mason took the idea further, picturing coordinators asking their care management system, in plain, human language, whether next week’s rota has any gaps. For Liz, the key is education. Carers need to understand both the benefits and the drawbacks, so they embrace AI rather than simply accept it.
Drawing on 30 years of experience leading transformation, Seán added, you can choose evolution or revolution. Revolution is tempting because it’s fast, but it’s hard to bring people with you, and in care, people matter most to the outcome.
