Enterterprises should create networks of specific AI solutions and intelligent agents. These agents adapt to their environment and are autonomous. In other words, they continually learn from situations and each other about how to best perform their tasks. Soon, intelligent agents augment the whole enterprise and support employees. As they grow and evolve, these systems become ubiquitous, from internal systems to monitor and control processes to client facing applications and services.
Smart monitoring devices digitise legacy equipment
The internet of things is continuously growing. The last couple of years we saw many groundbreaking developments in the field, especially around the machine to machine communication, data storage and transfer, and analytics. Nevertheless, adoption of these technologies is still low. A possible cause for this is that the majority of the plants have a fair share of legacy equipment and systems. However, smart monitoring systems can transform the majority of the legacy equipment into digital assets without replacing them unlocking new opportunities for improving operational efficiency.
Smart monitoring systems coupled with artificial intelligence turn your legacy equipment into digital assets
Artificial intelligence transforms analogue equipment into digital assets via smart monitoring systems. In recent years, image recognition algorithms and embedded designs have been developed to read analogue display devices. Taking a cue from how humans interact with analogue display devices, these algorithms take a snapshot of the display at fixed time intervals which are then processed to extract what is displayed . This type of algorithms crossed the boundary between research and application as prototypes, startups, and established companies them into production. One notable example is Cypress Envirosystems who developed a non-invasive, wireless device capable of remote monitoring analogue pressure or temperature. There are some other devices and algorithms existent on the market that enable people to retrofit their legacy assets and machine tools such as wireless pneumatic thermostats, steam trap monitors, or freezer monitors. These examples go to show that legacy systems can be a part of the Industrial IoT. Data collection is one of the primary blockers in deploying IoT analytics and machine learning solutions. However, these smart monitoring systems solve that problem and make most of the digital equipment digital.
“Analog data” can be digitised by combining AI with human supervision. Industrial sectors are renowned for the massive data sets they generate. Every moment, your business produces data that is recorded by machines, humans, or both. Unfortunately, a small collection of these data are digitised leaving the vast majority of the data lying in archives spread across different countries or even continents. This issue is similar to what happens in most of the health or legal sectors. Luckily, smart people have recognised this problem and developed intelligent systems capable of converting paper-based information records into digital ones, for example, IBM’s Datacap module. Therefore, transforming information to digital formats is a now manageable, with many products and solutions out there ready to bring paper-based data into a digital form.
Newly digitatised assets can send their data to a central repository in the cloud
The cloud is a reliable and flexible medium to centralise data. So far, you saw how analogue display devices and documents could be made digital. Once the systems to turn them into digital assets are in place, you can push all the data and associated applications to the cloud. The available cloud providers vary regarding performance, costs, and security. However, the multitude of vendors ensures that there is a solution to any data and security requirement.
Data and application security in the cloud is an evolving area of research. Although there is no such thing as a 100% secure digital product or service (you can find out more about the top threats here), most of the big cloud providers invest massively in improving the security of their services. When you digitise your legacy equipment, you will primarily have three options of getting cloud resources: infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). If you choose either IaaS or PaaS, then there is a shared responsibility regarding the security of your application. If you decide to use a SaaS approach, then the cloud provider is responsible for securing the backend of your application and the data leaving you to worry only about the client side security. All options have pros and cons (here is a survey on security in cloud computing that is worth reading). Irrespective of how you deploy your apps, you need to keep in mind that there is no such thing as a 100% vulnerability free system (be it in the cloud or on-premises) and that all primary cloud providers are always upgrading their systems to ensure that they are up to date with the latest security developments. Furthermore, the market for cloud computing resources is sufficiently mature to provide solutions for applications and data of any size, shape, or form. Thus, deploying applications and data to the cloud is a viable solution for storing, organising, and making use of all the data generated by your assets and other business functions.
Use centralised data to gain more comprehensive insights
Existent analytics software can be customised to extract insights from data. Converting so much data to digital formats means that you instantly have access to raw information about every part of your organisation from any place in real time. At this stage, you are probably asking how are you going to analyse and interpret all that data because it is one thing to have data, and it is an entirely different matter to gain actionable insights from those data.
Probably you already have analysts specialised in processing data coming from specific data sources, such as sales, financial information, or marketing data. It might seem that their expertise is all lost in the scenario where all the data is centralised. Nothing could be further from the truth. If your information is all in one place, then your analysts and data scientists have access to the same data in real time. They share the data, and therefore it will be easier for them to create more comprehensive models. Furthermore, the tool they built over the years does not need to change. If anything, they will evolve to accommodate new data sources that are now readily available, thus gaining more insights about your business.
Irrespective of the state of digitalisation, any company can take advantage of analytics in a few steps. First, you transform your analogue assets into digital ones. Next, you build or extend your data collection and organisation capacity to store all the relevant raw data into the cloud. Once you are there, there is no limit to what your team can achieve. Now that everyone in your organisation has access to the same data in real time, you can capitalise on the insights you get. For example, you can build predictive algorithms that use all the data as opposed to being limited to information coming from one or two sources. Furthermore, you can start ingesting external data, such as market trends, weather information, or consumer reports and incorporate that into the models you build giving the algorithms even more information about the world they are trying to model and anticipate.
In conclusion, any plant can benefit from analytics, irrespective of the age of the equipment and systems in operation. We now have a plethora of methods to choose from to transform analogue assets into digital ones and to collect, organise, understand, and explain the data they produce.
 Shen, X. et al. (2016). Research on automatic indication values of pointer gauges based on computer vision. In: 2016 China International Conference on Electricity Distribution (CICED). [online] City: Xi’An, China. IEEE. Available at: http://ieeexplore.ieee.org/document/7576034/ [Accessed 28 Jan 2018]
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voice interfaces and video content will soon become the primary way in which customers interact with businesses. Customers will interact with a brand in real time about their products and services. Voice interfaces like Alexa, Siri, or Cortana will enable customers to talk to a brand. Video and speech processing algorithms will further support that interaction either via intelligent chatbots or other interfaces that understand consumers’ pains, wants, and needs. As far-fetched as it sounds, this is not a sci-fi scenario. It will happen in the next 3-5 (maybe ten years). Thus, for brands to maintain and establish new long-lasting relationships, they need to support audio and video conversations with their clients as soon as possible.
AI is a collection of methods that initiated a new paradigm in business, one where we solve problems without knowing the steps to take in advance. Using AI, we develop systems that mimic natural intelligence and in some cases even surpass it. Thus, AI creates systems capable of reasoning, solve problems, acquire and use knowledge, make decisions, and communicate in natural language.