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 beginning to appear within the language of the many engineering and construction companies. Certainly, there's an aspiration to be more forward-looking, also as future-gazing—this is strictly how we should always be behaving like an industry. However, we are still considered 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 bent to still have learned to celebrate failure, and intrinsically overlook the learnings and opportunities they carry.

As an industry that likes the comfort of tried and tested, we forget that we've 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, which we are ready to build incredible structures, safely dig huge tunnels under cities among a myriad of obstacles with millimeter accuracy. Can we innovate? In fact, we do—we simply don't quote it or understand we do it!

However, as an industry, our maturity remains either very siloed or behind once introducing these sorts of technologies to the trade. to mention that we are tech-shy may be a name. As an industry, we have got modified considerably over the years, and still do so. Most engineering corporations are currently well entrenched in digital technology.

To name some examples:

• laser scanning also as a measuring instrument
• using mobile computing for red-lining, snags, and reporting has become more and more traditional
• The adoption and introduction of BIM and its standards
• Digital authoring tools, now with the combined opportunities that UAVs and satellite data can bring
• 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 several raises tons 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 just useful if it is often used. The challenge for my industry is deciphering but additionally understanding the potential and also the profit that information can provide us. many AI developers promise a plethora of data and insights. We tend to possess already got a plethora of data, insights are solely useful if they add worth.

I think the question we should always be asking ourselves is: we all know we've data, however, what is going to we tend to try to to with the info to make it tons of valuable than it presently is? what is the info telling us that we don't already know or add up of? moreover, there's a serious amount of supervised learning that has got to be done to make sure the insights actually provide worth, how will we tend to find out to trust the outputs?

Lastly, the economics of going ‘all in’ with AI and machine learning square measure significant: investment in software, sensors or data acquisition devices, data warehousing, costs of integrating or structuring all the data along then processing it, requires careful consideration in an industry with tiny profit margins (scraping past fractions below one percent). Ultimately, to try to do this need a big outlay while not data concerning AI and machine learning capability, which may be 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 successfully (and usually on time and budget), our safety records are perpetually rising (and market-leading). What is going to the data we've got provided to us that we don't already know? With the decades of experience we have within our businesses, this will be where the challenge lies. That said, we still have how to travel to completely establish lessons learned and spotting trends and repeated mistakes.

What will our businesses get to invest in to be ready to gather new data to supply insights we didn't know we tend to need? is that the reward well well worth the risk of investment?

Merely exploring this will be a really daring initiative for our trade. We’d got to provide associate external party access to any or all our project information (be it smart or bad). they might get to give our project or program planning a shakedown with many terabytes of data. The outputs could be positive, or uncover some uncomfortable truths, or give insights that we tend to thought to be smart that's literally the polar opposite.

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

 

 

Weekly Brief

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