Artificial Intelligence is without doubt one of the technologies that are at the peak in the hype cycle. It already surfaces in a lot of real-life applications - sometimes very prominently, sometimes just humming under the hood. In this article we will look at some the most relevant AI practical applications for property management operations.
One of the most overlooked artificial intelligence applications is speech to text. This technology allows technicians in the field to dictate their notes and comments and avoid typing lengthy text on their mobile devices. This technology is now mature and all major mobile device ecosystems incorporate state of the art voice recognition technology in their devices. The same powerful tech that drives Siri, Alexa and Google Home devices can accurately translate your technicians’ SuiteSpot notes into text in all major spoken languages.
Another area where technology has made leaps over the past few years is Automatic Translation. We can now leverage powerful artificial intelligence engines to translate notes and comments from one language to another and allow employees and residents with different linguistic backgrounds to communicate effectively. At SuiteSpot, we are now employing this technology to allow teams of English, Spanish and French speakers to work together across large scale geographies and to serve residents with different cultural and linguistic backgrounds.
At its core, Artificial Intelligence is a disruptive technological advancement in data analysis. In its most raw and powerful form it can be used to discover correlations between seemingly unrelated variables in large data sets. For example, we could apply it to your history of renovation data and provide an AI powered prediction for renovation cost using basic prospective resident demographic information. We can look at weather, demographics, amenity and other building data and provide a predictive assessment of future property maintenance needs and costs. These analysis techniques are available now and are accessible if the source data is available. This is why adoption of a digital operations solution that tracks and collects work order, inspection and turnover data is essential in enabling predictive artificial intelligence analysis.
When looking at properly executed data analysis strategies that employ AI, it is very easy to mistake analytical capabilities with true intelligence. In the end, at least in its current form and technological state, AI is not and should not be believed to be an end-all human replacement. This has already been acknowledged by the thought leaders in the field. In his 2015 book, Rise of the Robots, Martin Ford actually identifies several blue collar jobs as being relatively immune to disruption by artificial intelligence. The skills that we typically associate with our field staff and technicians are leading his assessment.
In Superintelligence, another leading work, Nick Bostrom explores in depth the dangers of relying completely on artificial intelligence for decision making. In the end we have to see AI for what it actually is: a super competent helper that allows organizations to be managed better with data based decisions and helps people connect and work together by eliminating mundane friction points such as thought transcribing and translation.
Cannot take decisions - it is only a data pattern recognition technology at the moment and any repeated reliance on its decisions carries with it the risks of feedback loops.
The biggest opportunity on the analytics is bringing light to the actionable data points instead of a massive data dump.
Property operators do not have time to go digging through mountains of data or properly interpret that data. The systems that will win today need to be smart enough to pull out the data they need to know in real-time. The disruptive piece we built is coming from insights we can derive from that data, in order to find the pain points where focus and attention is required or where there are opportunities to reduce costs or increase revenues, in a quick and simple way.
At a high level, managers need to understand their costs, what work their staff and 3rd party vendors are actually doing (and highlighting if/where there are significant variances from baseline or standard costs), where they are spending more relative to other properties, what the costs variances between in-house teams and contractors are, what the biggest drivers in their costs are and how quickly they can spot anomalies.
At an executive level, it’s primarily knowing where properties are operating relative to budget levels, (and more importantly capital allocation), what the strongest drivers on their returns are and what is driving under/over performing properties. All of these points come from the data that is collected in SuiteSpot.
Thanks for reading. My name's Sorin Michnea, I'm the CTO at SuiteSpot and I'd be happy to speak with you about your AI related projects, questions, or ideas. You can reach me by emailing Hello@SuiteSpotTechnology.com