Is social housing on the cusp of a wave with artificial intelligence? Ask the computer.

It was inevitable – with computers came the generation of data, lots of it – and increasing exponentially all the time. In turn, this created the desire to take that data and find ways of analysing it to deliver business answers to business questions. Closing the loop, that would provide the evidence required to amend, update and refine processes that would make us more efficient and cost effective. We’ve been doing it for years – but with the advent of artificial intelligence, do we need to be in the driving seat or can we leave it to the computer to learn from the data and act accordingly

The housing sector has stored endless amounts of data for many years, whether that’s been paper based or electronically, so it has generated enough data to mine to its advantage, to spot trends and to deliver the business benefits. Pattern analysis will become increasingly prevalent over the next five years and no more so than in social housing where housing management systems have provided the key to unlocking cost and operational efficiencies, often on tight budgets. Data continues to drive business processes and outcomes and predictive analytics will be wholly possible thanks to the big data that housing associations have stored thus far, and continue to store.

So that’s the data taken care of. Now what about the AI part of the puzzle?

AI – Artificial intelligence is a step further on, in that the computer is able to ‘learn’ based on user behaviour. It becomes conditioned if you will. For example, when a tenant makes a call to the housing association, the CRM system looks at that caller’s records and knows that they have a housing repair logged for that day. It is able to pre-empt that perhaps the caller may wish to cancel the appointment, or that the repairs operative is running late. The system will then present the caller with options to arrange a new appointment or provide details of timings. Alternatively if the caller has no repairs logged but they are perhaps in rent arrears, the system can present an agreements screen to take a payment or to setup an agreement.

The uses for AI in housing are endless. It can be used to identify predictive patterns for repairs – if a particular brand of boiler breaks every 9 years, it can be flagged and plans for replacements put into motion seamlessly. The housing management system can act on information and trend it highlights. Likewise, if a certain type of property or area has issues with flooding or anti social behaviour at certain times of the year this could be presented to the relevant team for contingency planning before it becomes a problem.

The argument against AI is often based around manpower and ‘robots taking over’. But used correctly, AI will deliver the right person to the right job making the workforce more efficient. If a certain type of job has required more than one person to fix it on several occasions, the system is able to provision the right manpower for the next job of that type. Existing employees can be redeployed in higher value roles where they can make a difference or deliver increased value to the business.

It’s clear that AI is becoming an ambition and in some cases reality for many housing associations and councils in the way they communicate and deliver services. Chatbots are already mainstream in many other commercial sectors and housing will follow suit.

Often it’s the case that consumer technology drives the implementation of business technology – and with the growth in devices such as the Amazon Echo and Dot – there will come a point where tenants expect to “ask Alexa” or “ask Siri” to “ask my housing association to fix a repair.”  It’s just around the corner…