First things first. This isn’t about robots. The day may come when robots take over caring for the vulnerable and cleaning the streets – but before then there’ll be a fundamental change in the business model for local public services.
There’s a direct parallel with the aerospace industry. Over the last decade the analytics of information generated by connected sensors has transformed the business models of companies like Rolls Royce and Boeing. That artificial intelligence has allowed a shift from a manufacturing-based business model to a service model based on preventative maintenance.
As the cost of connected devices continues to fall there’s huge potential for local public services to follow a similar path. Local authorities and government agencies can use artificial intelligence to analyse data generated by sensors. These could be air quality sensors, motion sensors to detect potholes or even fall detectors for the elderly in their homes, for example.
Using effective data analytics will help authorities and agencies move resources from assessing need to solving problems.
Let AI take the strain
Nearly all local public services follow a similar approach when delivering services. First they assess the need. Next they apply a set of assessment criteria to evaluate the relative priority of the need and allocate the appropriate resources. Then they authorise an ‘intervention’, assess its impact, and then re-assess the need.
Artificial intelligence will cut the time spent identifying and assessing issues. This is true across the whole, diverse, remit of public services. Let’s take three examples.
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Local authorities are responsible for air quality. They could use data analytics from connected devices, like the Cleanspace™ we developed for Drayson Technologies, to monitor air quality in real time. They could then dynamically divert traffic away from areas where air quality is below the required standard. By doing that they’ll be able to improve traffic flow, promote economic activity – and keep within the law.
Local authorities could use cameras and motion sensors underneath buses and taxis to dynamically monitor potholes. That makes it possible to not only assess the current state of the road but assess how it deteriorates over a period of time – allowing that preventive maintenance. A council can then prioritise repairs to fill the potholes that are likely to cause a significant hazard.
Care for the elderly
Typically an older person will have their needs assessed after an incident such as a fall, or deterioration in health. Local authorities use a set of criteria to assess their need and entitlement to support and then authorise a care package. A significant amount of resource is involved in assessing and managing the care package. But a simple skin patch can detect hydration levels and could trigger a prompt from a device like Amazon Echo for the person to have a drink of water. Or a GPS-enabled alarm could alert a family member or neighbour if someone with dementia left home at an unexpected time. This type of artificial intelligence will allow people to live independently in their own homes for longer. What’s more the reduction in the resources required to assess need, allocate resources and directly monitor vulnerable individuals means a higher proportion of the social care budget could be spent on face-to-face care.
We’re already proving the power of this approach with our award-winning Argenti Partnership which provides telecare services for vulnerable people in Hampshire. Through innovative use of technology supported by robust business analytics Hampshire County Council has been able to demonstrate savings of £7.1 million over years – and bring improved outcomes. Some 94 per cent of the service users say the service has increased their feelings of safety and security.
So it’s not technology that saves money and improves services. The technology provides the data that when effectively collated and analysed using artificial intelligence helps councils make better decisions. And it facilitates changes to the business model that mean councils could spend a greater proportion of funding addressing problems rather than assessing need.
To take advantage of this opportunity local authorities need to focus on three questions:
What are the existing AI options we can apply in our services to predict need and reduce demand for services in the long term?
How do we build a consistent approach across the organisation to collecting and analysing the data from connected devices so we can improve the way we allocate resources within and between services?
How do we change the design of our business to respond to this opportunity?
There’s a significant opportunity for local authorities to save money and improve services. The challenge is how to move from the existing business model to one which releases more resources for front-line service delivery, in short, how to move to a model of preventative maintenance. Our advice is to think big – by envisaging the potential to transform lives of citizens – and start small – with projects that use proven technology with a demonstrable return on investment. (Trying it out needn’t cost £ millions. An investment of £20-£30,000 could get an air quality service up and running, for example.) And, once you know it works, you should scale fast.