AI and Machine Learning's Role in Enhancing the PropTech Landscape

AI and Machine Learning's Role in Enhancing the PropTech Landscape

The words digital, innovation, technology, and now AI is starting to appear within the language of many engineering and construction companies. Certainly, there's an aspiration to be more forward-looking, as well as future-gazing—this is exactly how we should be behaving like an industry. However, we are still very much at the embryonic stage, and this new path is fraught with dead ends, troughs of disillusionment, and dare I say it, failures. we have a tendency to still have not learned to celebrate failure, and as such overlook the learnings and opportunities they bring.

As an industry that likes the comfort of tried and tested, we forget that we have innovated over the years, still as having adopted and adapted new technologies over the decades. One might call it evolution, but we do ourselves a disservice—we have developed new materials, applied new engineering processes, improved efficiencies while reducing waste and rising safety—themes that are still very relevant today. though our industry rarely makes original mistakes, we do learn, and that we are able to build incredible structures, safely dig huge tunnels under cities among a myriad of obstacles with millimeter accuracy. do we innovate? of course, we do—we simply don't quote it or understand we do it!

However, as an industry, our maturity is still either very siloed or behind once introducing these style of technologies to the trade. to say that we are tech-shy is a name. As an industry, we've got modified considerably over the years, and continue to do so. Most applied science corporations are currently well entrenched in digital technology.

To name some examples:

• optical maser scanning as well as measuring system
• using mobile computing for red-lining, snags, and reporting have become more and more traditional
• The introduction and adoption of BIM and its standards
• Digital authoring tools, now with the added opportunities that UAVs and satellite data can bring (photogrammetry and purpose clouds
• AI and machinery are permitting exaggerated efficiency or lowering personnel risk.

Collecting and sorting data is all very well and good, but data is only useful if it can be used

However, this new information revolution being an intangible part for many raises a lot of queries fuelled by apprehension. often I hear the following: ‘Artificial intelligence and data analytics thus what?’ ‘What is in it for me?’ ‘It isn't applicable to us.’

Collecting and sorting information is all okay and smart, but data is simply useful if it can be used. The challenge for my industry is unlocking but additionally understanding the potential and also the profit that information can provide us. many AI developers promise a plethora of information and insights. we tend to have already got a plethora of information, insights are solely useful if they add worth.

I think the question we should be asking ourselves is: we know we have data, however what will we tend to do with the data to create it a lot of valuable than it presently is? what's the info telling us that we do not already know or make sense of? moreover, there is a major amount of supervised learning that has to be done to ensure the insights actually provide worth, how will we tend to learn to trust the outputs?

Lastly, the economics of going ‘all in’ with AI and machine learning area unit significant: investment in software, sensors or data acquisition devices, data warehousing, costs of integrating or structuring all the info along then processing it, requires careful consideration in an industry with tiny profit margins (scraping past fractions below one percent). Ultimately, to do this requires a significant outlay while not data concerning AI and machine learning capability, which is a risk.

Our demands are comparatively simple. build our data give more value than it currently will. we deliver advanced multi-disciplinary and million pound projects with success (and usually on time and budget), our safety records are perpetually rising (and market leading). What will the info we've got provide to us that we do not already know? With the decades of experience we have within our businesses, this can be where the challenge lies. That said, we still have some way to go to fully establish lessons learned and spotting trends and repeated mistakes.

What will our businesses need to invest in to be able to gather new data to provide insights we did not know we tend to needed? is that the reward well worth the risk of investment?

Merely exploring this can be a very daring first step for our trade. we might need to provide associate external party access to any or all our project information (be it smart or bad). they would need to give our project or programme planning a shakedown with many terabytes of information. The outputs might be positive, or uncover some uncomfortable truths, or give insights that we tend to thought to be smart that are literally the polar opposite.

If this revolution succeeds, the information that AI, sensors, or big data might give some spectacular business intelligence and insights that were never celebrated, creating a paradigm shift in behaviours in however we tend to deliver projects as well as enhance our learning and knowledge, indeed, in however we tend to run our businesses. If this revolution fails, just like the dreadful incident of Elaine Herzberg’s death owing to the trial of an autonomous automobile, it'll set our industry back many years entrenching any scepticism.

Weekly Brief

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