Data is the real revolution; AI is just the tool
The social housing sector is grappling with rising operating costs, ageing housing portfolios, strained contractor relationships and longer repair times for residents, all under a spotlight of greater regulatory and political scrutiny. In this climate, the promise of a quick technology fix is compelling, with AI being the most frequently-cited solution.
However, before we’re drawn in by the allure of intelligent technology, we must address a more fundamental truth. The underlying problem connecting these pressures isn’t a technology deficit, it’s a data deficit. While AI is a powerful tool, its effectiveness in certain AI toolkits is entirely dependent on the quality of the information we provide it.
Understanding the toolkit
To leverage any technology, we must first understand its nature. AI is a vast domain concerning the creation of systems which can perform tasks requiring human-like intellect.
This field is broadly classified by its capabilities, from the ‘narrow AI’ we use today for specific tasks, to the theoretical, human-level ‘general AI’ of the future, and by its functionality, which defines how it perceives and interacts with its environment.
Within this field, the most practical application for our sector is machine learning (ML), a subset of AI where systems learn from data to identify patterns and make predictions. Consider the potential for proactive asset management or defect identification. An ML system could analyse years of maintenance records to accurately predict when a component within a property is likely to fail. This capability allows us to shift from an expensive, reactive repair cycle to a planned, preventative strategy.
Rubbish in, rubbish out…
Using ML as a use-case, we arrive at the heart of the matter; sophisticated ML models are rendered entirely useless if the data they learn from is flawed. The potential for predictive maintenance evaporates if historical repair information is captured erratically, stored in disparate silos or is missing entirely. When the machine has no reliable pattern to recognise, it can’t make a reliable prediction.
Too many organisations are captivated by the potential of AI without appreciating this prerequisite. An investment in intelligent systems without a parallel investment in a robust data pipeline is fundamentally unsound. The unglamorous, behind-the-scenes work of establishing strong data governance, ensuring consistent capture methods and cleansing existing information is the essential groundwork that must be done first.
The ‘why’’ before the ‘how’
The second major pitfall is adopting a new technology without a clear purpose. The objective should never be to simply ‘implement AI’. The objective must be to solve a specific problem. Do we want early insights into properties at risk of damp and mould? Is the goal to optimise our repairs scheduling to improve tenant satisfaction? Or are we trying to better understand tenants’ vulnerability? We must identify the ‘why’ to tackle the problem we are looking to solve.
Only by defining the problem can we determine the data required and the technology best suited to solve it. This needs-led approach ensures that technology serves the organisation and its residents, not the other way around. It’s the only way to transform the abstract hype of AI into concrete, measurable value for our operations and our residents.
The power of the collective
No housing provider is an island. We are all navigating the same operational problems. We are also all sitting on invaluable, yet siloed, data.
Imagine the exponential power we could unleash by creating a secure, anonymised data-sharing community. By pooling our collective data on asset performance, repairs and tenant needs, we could build datasets of unparalleled scale and depth.
This shared intelligence would dramatically accelerate innovation. It would allow the development of far more accurate and insightful predictive models, benefiting every organisation involved. We could move forward together, preventing countless hours of duplicated effort and establishing best practices that elevate the entire sector and improve lives of many.
The road ahead for social housing will undoubtedly be paved with technological advances. But the organisations that will truly lead the way are those that understand that the journey doesn’t begin with an AI procurement process. It begins with a deep, strategic commitment to the quality and accessibility of their data.
In order to build a sustainable future for everyone in the sector, we must ensure we build this foundation with diligence, focus on solving the right problems and collaborate to forge a smarter and more resilient future for social housing.
Akhlaq Choudhury is the founder of GGM360.
