The technology industry is abuzz with talk of digitally transformative technologies including the Internet of Things (IoT), Artificial Intelligence (AI), data sharing, distributed ledger, 5G connectivity and more. But, as ever with new technologies, it is only when end-users deploy solutions that leverage these technologies that digitally transformed industries will materialise. So, what will be the real-world impact of new emerging technologies?
This article is drawn from work that Transforma Insights has undertaken to profile the major domains of change that are enabled by digitally transformative technologies across a range of sectors. The sectors and identified dmains of digitally transformative change are illustrated in figure 1, below. The underlying reports currently run to a combined length of 420 pages, of which this article is just an initial toke.
The Internet of Things (IoT) is key
Of all emerging technologies IoT will probably be the most impactful, extending the reach of technology-enabled change into the real world. A quick glance through the domains of change listed in figure 1, above, reveals very few that do not in some way leverage IoT. Changes enabled by IoT range from Automatic Irrigation Systems in agriculture to Parametric Insurance and from Warehouse Monitoring to Digitised Concrete Management in construction.
The deployment of many of these IoT solutions is enabled by, or at least accelerated by, the emergence of new wireless connectivity technologies such as Low Power Wide Area (LPWA) and 5G. LPWA technologies in particular have the potential to extend technical solutions further into harder-to-reach locations by virtue of their ability to combine low costs with wide area coverage and low power consumption (and so correspondingly long battery life). These technologies are a perfect fit for many applications such as Crop Monitoring and Environment Monitoring in smart cities. In the context of industrial digital transformation, the role of 5G is generally more niche in nature. The potential to support low latency, highly reliable connectivity (Ultra Reliable Low Latency Communication – URLLC) will support certain critical solutions such as Worker Assistance in the context of industrial private networks, whilst higher bandwidth capabilities (Enhanced Mobile Broadband – eMBB) will similarly find a role to support solutions such as video-based Quality Assurance in factories, but also applications like AR-enabled Augmented Field Force.
It remains to be seen what the impact of some of the latest emerging connectivity technologies will be. For instance, NR+ (previously known as DECT-2020 NR and also briefly DECT-5G) is a non-cellular 5G wireless technology. It operates as a self-healing mesh typically in licence-exempt spectrum and can support similar services as cellular 5G, including low-bandwidth Massive Machine Type Communication (mMTC) and URLLC. Clearly NR+ has the potential to be very handy in the context of industrial private networks, but the technology’s real-world impact is currently to a large extent unproven.
Based on other work undertaken by Transforma Insights, we have also established that IoT is key to unlocking sustainability benefits associated with new digitally transformative technologies. As discussed above, IoT is the interface between digitally transformed processes and the real-world, and it’s in the real world that energy is consumed by moving things around and heating them up and/or cooling them down.
Artificial Intelligence (AI) is almost as important as IoT
AI enables a significant majority of the digitally transformative domains of change as illustrated above, often adding a next level of benefit layered on top of whatever can be achieved with IoT alone. The benefits associated with AI tend to fall into four main areas.
The first and most obvious benefit of AI in the context of enterprise digital transformation is the optimisation of existing processes. For instance, AI-enabled Digitised Concrete Management that monitors the curing process of newly poured concrete can help to ensure that construction projects can proceed as efficiently as possible. Also in the construction domain, AI can have a significant role for example in supporting Worker Safety by helping to identify and avoid potentially harmful events.
Secondly, the scalability of AI and potential to combine many different data sources and identify correlations, or draw inferences, between different inputs means that the technology often finds a role in extending the scope of optimisation for existing processes. For example, AI can enhance the efficiency of warehouse operations in isolation, but it can also be used to achieve extended optimisation taking into account the status of inbound logistics, a manufacturer’s order book, production status on the factory floor, and so on.
Thirdly, AI-enabled hardware devices can play a significant role in digital transformation. Examples include airborne drones that can be used for crop spraying, Automated Guided Vehicles (AGVs) in warehouse locations, or even robot ‘dogs’ equipped with cameras and other sensors to provide security on unattended construction sites.
Fourth, AI can be a very effective way to monitor existing processes to identify when something unexpected or unusual might be happening, often by simply deploying some kind of exception analysis capability. Simply escalating abnormal operating conditions to human managers can be an effective way to pre-emptively maintain many kinds of equipment. Over time, such AI models can be trained on ‘unusual’ operating conditions that have previously been experienced, so that the AI can suggest next steps without first escalating to human managers.
AI can, of course, be deployed independently of IoT, for instance in the context of Intelligent Recruitment & HR, but such opportunities are far more incremental in nature, and less transformational than the solution areas that we have identified as major domains of change. The generative AI solutions that might be deployed in such contexts are fundamentally different kinds of solution as well. In the 420 pages mentioned earlier, the word ‘hallucination’ (often associated with generative AI) does not appear even once.
Other digitally transformative technologies
Other technologies make relatively fleeting appearances. Distributed ledger, for instance, finds a potential role supporting X-aaS (servitisation) for manufacturers, Supply Chain Transparency, Patient Data Management for healthcare provider networks (particularly in the USA), and Servitised Asset Insurance, Supply Chain Insurance and Smart Contracts in the insurance industry. Additive Manufacturing (or 3D-Printing) is limited essentially to the manufacture of medical implants (particularly dental), niche manufacturing contexts and niche field force applications (so that spare parts can be manufactured where and when they are needed). Additive manufacturing techniques have also been applied to house building, wherein buildings can be built up with extruded concrete.
Applications associated with Autonomous Robotic Systems in particular are very diverse, ranging from exoskeletons that promote worker safety in construction contexts (and allow older workers to continue working for longer) to Surgical Robots and from Worker Assistance in factories to Livestock Management in Agriculture.
Data Sharing techniques are critical in the context of Farm Management, Enhanced Project Management and Asset Tracking in construction contexts, and many solution types in healthcare, smart cities, and supply chains.
It’s not about the technology
As ever, digital transformation is not ‘all about the technology’. The domains of change discussed in this article, and in more detail in the associated reports, all combine new technologies in different ways and to do different things. All of the identified domains of change exist by virtue of the fact that they address constraints associated with current-day approaches, or capitalise on opportunities to deliver better outcomes, typically for less cost.
