Artificial Intelligence is touted to have a truly significant role to play in the future of our lives. Just take the words of Google CEO, Sundar Pichai:
“AI is one of the most important things humanity is working on. It is more profound than… electricity or fire…AI holds the potential for some of the biggest advances we are going to see.”
The impact it could make is astronomical. Everything and anything has the potential to be positively transformed by AI. Medicine, world poverty, education; all hugely important issues which can be improved to the betterment of the world under the right AI-led innovation.
If transformation is the heart of AI, then the lifeblood of AI must lie in the realm of data. Data is not just that invisible entity which dictates how long you can watch Netflix for or see what your friends are up to on Facebook, before inexplicably running out (and you need to buy more). Instead, it is the raw stats and figures behind practically everything that, when pulled together, offer insight into how things work and the patterns and trends that guide them.
So, if the data collected is not good or incomplete, AI innovation is stunted in its capability. It is largely down to everyone to put in the groundwork now so that when AI is ready to make a transformational difference it has the power to do so.
Monica Rogati, a data scientist for LinkedIn and independent AI advisor, highlights the need for data perfectly using Maslow’s Hierarchy of Needs as a visual tool. The Hierarchy of Needs resembles a layered pyramid where each level details certain human needs. The most basic, most important needs are at the bottom and the most complex needs at the top.
Rogati’s rethinking of this model looks at an AI Hierarchy of Needs.
As seen, the bottom layer is focussed on data as the essential need for AI and deep learning to exist. In fact, each layer above it focuses around data too: how it is stored, explored, labelled and analysed, and ultimately learnt from before it can be effective AI.
In housing, this is no different. AI certainly has the potential to take the sector into a new dimension but it can only do so if data is there to start with. Housing organisations have a duty to fulfil if they truly want to see the transformation AI can deliver. So, what can housing teams do to make this happen?
While many housing providers have access to large amounts of data, the quality is often way behind where it should be. Incomplete, inaccurate or duplicated data found across multiple systems (which are often in isolation from one another) impacts the potential AI offers.
For example, take a quick responsive repair. Perhaps all the details have not been filled on the system to save time. What might be a quick fix now will mean there is not sufficient enough detail for AI to be applied effectively further down the line.
If there is no complete detail of the repair then AI will not know if it was a leaky pipe or a faulty boiler. This restricts the machine learning capability and so the opportunity to more accurately predict whether a repair or replacement is needed in future situations. This may translate into increased costs and unnecessary repair visits to a boiler that would have been better being replaced entirely.
If we cannot find a way to ensure data quality is consistent and good today, we can not quite reach the capabilities of tomorrow. Or rather, the benefits artificial intelligence will bring to your business are not so valuable after all.
Building a culture around healthy technology usage and advocation is essential for AI to work. Staff need to understand why they should be working hard, to ensure data quality, and how this work will pay dividends in their professional futures.
For staff, AI can be the enabler to free teams from mundane tasks; offering more variety and satisfaction to their roles. AI will not directly replace housing staff, as many may fear. Instead, it will:
For all its power, however, it is worth noting AI cannot meet the same standards and abilities of our own thinking and interaction skills. AI remains purely a tool that will make jobs easier. Housing teams remain very much on the front-line of your organisation. They also remain key to correctly capturing data – so AI can have the intended impact – an ideal that must be adopted organisation-wide.
Directly leading on from the last point, adoption at this level must be seen across all teams and all organisation stakeholders. This is so AI can use as much data as possible to paint a complete picture of tenants and properties. This directly influences actionable predictions and, ultimately, saves time and money.
Take tenants who are late with their rental payment as an example. AI-led analysis of tenant records may indicate that they are late during certain months but are otherwise reliable, good payers. This scenario may reveal that late payment is more a question of cash flow rather than a tenant’s persistent inability or refusal to pay.
Provided good data available in all systems, AI learns these trends from the payment history and cross-references against other weighting factors, such as the type of household or income band, before applying to the whole database.
Going one step further, a housing provider could then look to identify families within the same demographics and weighting factors. These families could then be targetted for early interventions during these regular financial pressure-points and proactively help prevent falling behind with payments.
Without complete data sets, collated by the whole housing organisation – not just ‘front-line’ staff – you run the risk of diminishing the opportunities AI can highlight.
AI is not as simple as a plug-and-play device. Its integration into your organisation will take expert understanding to ensure a robust digital framework and sensible data processes are all in place. Its very success or failure can rest entirely on these essential preliminary steps.
To this end, housing providers need to invest in the right IT knowledge and allow them to spearhead digital transformation projects of this magnitude at the top level. Times, in the digital sense, are changing at a rapid pace; do not risk falling behind the curve by neglecting to bring in the right people.
The housing sector is incredibly rich in data, hence why AI has the potential to make such a massive impact, but so much relies on the changes you look to make now:
If all of these points can be satisfied you are certainly on the right track. It will be fascinating to see where AI and digital technology takes the housing sector in the (very) near future.