DIGITAL TECHNOLOGY
Above: Figure 6. The wastage of data.
Above right: Figure 7. The outcomes of DT.
surveyed considered their IT applications, such as BIM, PPM, and BMS, to be fully integrated, although a majority indicated that their systems were partially integrated. Three out of 10 said there was no integration. When it comes to the use of data, all of the respondents said that between 41 and 80% of their data collected is wasted, i.e. not analysed. This level of wastage would not be tolerated in any other aspect of the services provided by Estates Departments. What about the future? I’ve said that DT is inevitable,
but the secret is to get the most out of it, with the least pain. A couple of years ago I was in an airport looking at the books, not expecting to buy anything. Then Scary Smart7 by Mo Gawdat caught my eye. It was subtitled ‘The Future of Artificial Intelligence and How You Can Save Our World’. Mo is former Chief Business Officer at Google, so probably well qualified to comment. Bearing in mind that I was ‘off duty’, this was a bit of a heavyweight book choice, so for contrast I chose Expected Goals8
by Rory
Smith, which is subtitled ‘The story of how data conquered football and changed the game forever’. I recommend them to anyone who wants to understand more about data and the future. The Scary Smart book spends the first half scaring the wits out of the reader, with three premises – AI is inevitable, AI will outsmart humans, and bad things will happen. The author observes that self-driving cars are already safer than cars driven by humans.
Self-driving cars On a frosty day years ago, driving on the A40 dual carriageway towards London, I crossed a bridge, and the road curved to the right to become the slip road onto the M40. That’s when I spotted six cars all lined up alongside each other in the ditch, where they had lost control on the colder road surface of the bridge, and failed to make the bend. If those cars and my own had been self-driving, how many would be in the ditch? I think the answer would be one or none. As soon as the first car lost control, the data would be shared with the other cars (much as our satnavs share data) in order to allow them to avoid a similar fate. Alternatively, if each of the cars had data on the air temperature, surface temperature, accident history, and location of the bridge etc, the conditions would have been known or at least predictable, so as to avoid any of the cars ending up in the ditch. It’s easy to believe the data that demonstrate that self-drive cars are safer. One of the most interesting statements was that
36 Health Estate Journal March 2025
comparing AI to human intelligence in 2049 will be like comparing Einstein to a moth. This raises some difficult questions, not least, ‘Would Einstein want to be subservient to a moth?’ The second half of the book explains how the potential nightmare scenarios described in the first half can be avoided. A further enlightening observation of Mo Gawdat is that the amount of data being generated is increasing rapidly, but that the volume of data analysis has stayed the same. This indicates a lack of systematic management. Correcting this anomaly should form part of your data transformation journey. Either use the data being generated, or don’t bother to collect it.
Trailblazers for digital technology The Expected Goals book would certainly be relevant to anyone interested in data and/or football. One of the most surprising things about it is that the early adopters of data in football were ‘Big’ Sam Allardyce and Steve McClaren, unlikely candidates to many of us. The moral of that story is that even if you perceive
yourself as Sam or Steve (or even Samantha or Stephanie), it does not mean you could not be a trailblazer for DT. There have been plenty of examples of AI potentially going wrong. The old adage, ‘Anyone can make a mistake, but to make a huge mistake, you need a computer’, should be updated to ‘Anyone can make a mistake, but to destroy mankind, you need AI’. There have been experiments involving computers
working together, that have produced unexpected outcomes. In one case, the computers – while solving a problem – started to communicate with each other in a language they had invented themselves, and which the human developers could not understand, at which point the ‘off’ button was hit by the developers. In another example, the computers developed a new religion, at which point the experiment was stopped. It would be complacent to think that the ability to ‘switch off’ AI will persist. Would AI not be capable of anticipating the power being cut, would AI not be capable of avoiding that situation, or – if it occurred – taking measures to restore power? We live in an interconnected world, which AI would clearly recognise. Can AI not control robots and power plants? It is much easier to imagine that AI could self-perpetuate than the opposite. Perhaps a more amusing AI-fail I read recently concerns a children’s TV programme called Bluey. Bluey and Bingo don’t want to tidy their playroom, so they get Daddy Robot to do it for
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