-since 2018

The charity jobs that could soon be enhanced by AI

Sheffield Hospitals Charity will receive funding through an online discount scheme run by parking firm Q-Park.

Over the last few years, there’s been a fair amount of scare-mongering around Artificial Intelligence (AI) and the future of employment: we’ve had experts saying that AI could take over 40% of jobs by 2035, and there’s been a general panic that these technology innovations will leave people in various sectors without jobs to earn a living, making many job roles obsolete.

But a lot of this is a misconception at best, or simply overblown. The reality is that technological progress has been changing and improving business models for a long time. AI has the potential to replace many of the mundane and repetitive, data-driven tasks that are just easier for a robot to do, and this is no bad thing.

For charities, it means there is a huge opportunity to augment their operations with efficiency-driving tools that will leave humans to focus on the things they’re best at: coming up with new ideas and carrying out the core work that will drive their missions and impact more lives. As CAF’s Rhodri Davies explains over on the Giving Thought blog, there are plenty of new jobs that will be actually created in the wake of the AI revolution.

We explore a few of the charity jobs that artificial intelligence and machine learning can enhance.


While there’s likely to always be a place for a friendly face or voice in the area of fundraising, we’re likely to see a lot more use of chatbots in the charity sector to help support fundraising.

While not artificial intelligence, charities like Greenpeace have had success in bringing their fundraising operations onto platforms such as Facebook Messenger and WhatsApp.

The Children’s Society have recently trialled a chatbot application on their Facebook page to support fundraising questions.

And chatbots are being used to bring the stories of service users to life for potential donors.

In philanthropy, data and AI-driven approaches could help make charity advice a commodity available to everyone, connecting charities with their ideal donors.

Support services assistant

As well as informing potential donors about charities’ work and guiding them towards a donation decision, chatbots can be an incredibly useful tool when it comes to support services.

Charity chatbots are already doing a great job in directing people towards the information they need, such as the Ally Housing chatbot, which helps the users of housing associations and housing charities access information through their phones.

These systems are a clever way of guiding people down set pathways to get to the right information that may already exist on a charity website, making the process more efficient for everyone and leaving support services staff to answer more complex or sensitive queries.


The Children’s Society have recently shown how AI-driven language translation is breaking down barriers between young asylum seekers and the charity workers who are helping them their services, allowing them to have a fluent conversation through a smartphone.

Conservation scientist

We’ve recently covered some of the ways in which data science and machine learning is being used by researchers, companies and non-profit organisations on the front lines of sustainability.

AI tools are becoming a powerful way for wildlife and conservation charities to understand patterns such as habitat loss, climate change, water use, poaching and more. These tools can help us build a better picture of the impact of human life on the natural world, and better prepare for the future.

Medical researcher

AI and robotics are transforming the healthcare landscape in many ways, helping researchers and healthcare workers diagnose health conditions and care fot patients better.

The Cystic Fibrosis Trust has partnered with the Alan Turing Institute (the national institute for data science and artificial intelligence) to use advanced machine learning techniques on UK Cystic Fibrosis Registry data taken from 99% of people diagnosed. From AI analysis of this data, they are able to better spot patterns in behaviour, symptoms and treatment effects. By doing this, they can treat more effectively.

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