5 AI trends for 2022: Our predictions

Tech trends are converging to transform our lives and artificial intelligence and machine learning will play a central role in this change. Camilla Braithwaite, Rezatec Product Manager and Geospatial AI expert, explores five of the key ways in which data and AI will lead you and your organisation into a more efficient, safer and smarter future.  

1.AI delivering better informed risk

When we consider risk there is a threshold of quality in the data that can be used to assess risk. You have to make sure that your data reaches a threshold of quality. However, the more data the better. If we can, we should add in many, many sources of data. With AI you can pull in as many data sets as possible to create the best output. If some of your initial data is only just over the quality threshold, pulling in all these other data sets can take your understanding up a level. AI helps to improve confidence in the data overall. If you have 50 data sets and one is only marginally over the quality threshold, but correlates closely with the other 49 data sets, that can give additional confidence in the overall data quality.

In the asset management world assessments are based on factors such as the age of the asset, data that might indicate where problems arise and is judged by experience. With AI, that human-based assessment can be taken up a level. For example, our pipeline risk assessment which considers multiple parameters is about 10 times more accurate at assessing risk than just considering the age of a pipe. Managing risk used to be a qualitative approach based on experience, AI is making that much smarter. In the coming years, AI will continue to give us steadily improving information and a better approach risk management. Using machine learning we are able to better validate the data and the output that the user gets is a better understanding of what parameters are affecting their assets.

Ultimately, with better data, asset owners are able to make systemic improvements to the system giving overall resilience benefits and this is a trend we expect to see far more of.

2.AI enabling business transformation  

Every company obviously has a different technology need but while these systems may evolve, at some point you need to reinvent the wheel and start from scratch. For example, the covid pandemic has prompted a lot of businesses to look into transforming the way they think about their business. That’s especially true of the large infrastructure companies which have been moving slowly but now have been spurred to put things in place. You have to be much more remote now and remote data collection is a good example. Rather than going on to a dam site to read a piezometer, which was previously normal and expected, now the expectation is that businesses will support remote working and remote data collection. This has given us a different frame of reference and this transformation is really a data transformation. It brings with it a major organisational and cultural transformation because it brings a new way of thinking about things. It’s allowing people to work more productively and more safely and have a better work-life balance as well other wider benefits such as improving air quality by reducing the need for transport.

Working remotely, it is important to make sure that the parameters recorded offer the same kind of context where that could affect the interpretation of the result. For example, a piezometer reading may also need weather or other data to provide really meaningful information.

In that case many streams of data are emerging simultaneously, but that could become unwieldy and result in lower efficiency. AI is able to bring all those data streams together to deliver context and indicate which readings are significant. It makes the abundance of data manageable again. With a site visit, a piezometer reading immediately makes sense, lots of data streams do not necessarily make sense, but the AI gives us those qualities back again. In the coming years we expect AI to feature far more in deriving context and quality from data and transforming that data to support new business models. 

3.AI and data-driven cultural change

While many conservative high-risk industries have been playing at the edges of AI and machine learning we have now reached a tipping point where organisations and industries are embracing change and these new approaches to data and data use. There’s a clear acceleration of that process and much broader acceptance of the advantages these kinds of tools have to offer. That wasn’t the case even a few years ago. Sometimes some external influence, like the pandemic, is necessary to begin the process of change and to make that step change to the AI way of working.

Nonetheless, the transformation of data is also changing how industries think about resource allocation. Remote working implies more data, especially in the large infrastructure industries. It means gathering data remotely but also being smarter with data by being more proactive and offering quicker responses to risks.

For example, rather than two roving engineers who conduct site visits and base their assessments on their experience coupled with local conditions and measurements instead we may see a single engineer and a single data scientist. However, in this case the engineering experts aren’t collecting data, instead they are applying their expertise more effectively to provide better risk assessments. The data scientist is managing the measurement, recording and collating of the necessary data. It’s about changing thinking to deploy resources more efficiently. 

Changing how businesses think about using AI to facilitate better resource deployment can help to quickly build a business case and one the delivers a return on investment in just a few years. Proactive, data and information-driven processes enable a dynamic approach to business and an effective response to real-time events. Perhaps more fundamentally though, people are smart and we should value and maximise that resource rather using humans to execute what are relatively mundane tasks when their key attributes could be far more productively employed. It’s a waste and using smart data can achieve extraordinarily far-reaching transformation of the workplace. Creativity, experience, and understanding can all be enhanced and supported by AI and we can expect to see far more of that kind of change in the years ahead.

4.AI and embedded smart technology  

Everything is becoming more connected and it is clear that AI will increasingly help manage the administration of life. As soon as you have the internet of things you have enormous amounts of data. But often this data is too removed to be of real value, AI allows us to use the information that smart embedded technology delivers in a productive and beneficial way. You have to have AI to manage that. It’s the only way to interpret that data. It’s not just the individual, if every kettle in the world is connected to the internet the person managing that system is faced with an overwhelming volume of information. They couldn’t possibly sift through that data and so the only way to use that data in a meaningful way is by relying on AI.

With a connected world manufacturers and suppliers can use that data to develop better and more appropriate products, individual consumers can then benefit from that data. At its heart the internet of things is using sensors to gather data, AI allows us to make better decisions based on that data and make them quickly.

It potentially offers a route to dramatically change how we manage resources like water for example. Better understanding of how we use water through our IoT kettle will allow us to better manage water usage and supply infrastructure. We will come to depend on the internet of things and that in turn will be built on a foundation of AI to extract meaningful data we can act upon.

With an increasing population and growing urbanisation basic maths demonstrates that we will need more and more water unless we change how we manage things. The coherent interpretation of that data enables change, basic change like how much water an individual uses but also at the macro scale where cities make decisions about water supply infrastructure. At both levels AI is really important. Data can reveal how we can change our consumption and IoT coupled with AI is the only route to that change by showing us something new that we didn’t know before. We can fully expect to see that kind of connectivity embedded in our world in the years to come and AI is an essential adjunct to that change.

5.AI supporting intuitive technology 

In order to make decisions quickly you need the information to be available in a usable way. Usable really infers that relevant information can be identified quickly from a tablet or smart phone at a glance. There are still apps and software, especially in the world of business, that are really hard to use. There shouldn’t be any requirement to dig for the right information through multiple menus for instance. An intuitive platform is really key here. Modern smart apps are designed to be instantly usable and understandable with easy, light touch learning. B2C platforms like Google and Facebook are already starting to influence the design of the business side of things too, so that a much more thought through and intuitive set of technologies are emerging. That will make a difference.

Today businesses are thinking about how they can achieve as few barriers as possible between the people who are going to use their tools and the information they need, making it as easy and usable as possible. Partly that is because it will save people time but it is also making information accessible not just by the experts who use it on a daily basis but also for the big decision makers and leaders of companies. If we can get our data into the hands of those people and make it useful for them then it is obviously of benefit.

The key to making data usable and informative is to add more layers of analysis. Going up the value chain means not just giving information about a particular pipe or pump or an area but giving an overview of a whole asset’s risk or allowing easy comparison between similar assets for example. Usability means making it as easy as possible and it is AI that allows relevant conclusions to be extracted from bulk data and presented in a usable, actionable format. We anticipate that AI will increasingly influence the design and functionality of data-led platforms to make them more intuitive and ultimately make data more usable and valuable.

For more information on Rezatec’s geospatial AI solutions, click here.