How will the collective integration of Smart labels, IoT & AI boost enterprise efficiency and innovation?
Adoption of IoT and smart labels is seeing unprecedented growth. According to Transforma Insights, the number of active IoT devices is set to grow from 16.1 billion in 2023 to a mammoth 39.9 billion in 2033. Similarly, the smart label market is projected to soar from $14.1 billion in 2024 to over $64 billion by 2034.
Recent global supply chain issues, the rise of smart utility meters, and the continued growth of online shopping (and, with it, a surge in delivery fraud) are just some of the factors behind the increased popularity of these technologies. IoT and smart labels provide the technology that powers innovative solutions to various everyday challenges. Here, Sharath Muddaiah, Head of Portfolio Strategy for IoT Solutions at G+D, considers the cumulative benefits that the uptake of IoT, smart labels, and AI is set to unleash.
Data and BI underpinning AI
The use of IoT and smart labels in logistics, healthcare, and transportation is reshaping how industries operate globally. However, while the devices collate important data, it is the platforms that house and analyse this data, transforming it into meaningful insights, that are the real industry disruptors. With the advent of AI, the value of the data pooled from IoT and smart labels is set to surge further.
AI is very much the buzzword of today. Still, the reality is that it is primarily being used in applications such as Chat GPT and Microsoft Copilot360 for requests based on common sets of information which enable simple services such as content generation, summarization, and task automation.
For AI to work as a more meaningful enterprise decision-making tool, enterprises first need to capture enough data sets, based solely on their specific business intelligence data, to enable AI tools to run accurate predictive analytics and take on decision-making. And this is where IoT and smart label technologies come into play; by providing detailed data logs, these devices generate vast amounts of data that are analysed by BI teams to generate important business insights and intelligence. These BI data stacks are then stored to provide the essential enterprise-specific data volumes which are needed for AI platforms to accurately analyse situations and take appropriate actions.
To take a real-life example, a UK fruit importer could use smart labels to monitor the transit of goods as they are shipped from their origin overseas to UK warehouses and onto local grocery stores. Using smart labels, the importer can gather data at every stage of this journey for analysis to better understand exactly how long each leg took, whether the goods were stationary for any length of time, and whether the temperature of the goods was altered significantly during transit.
Through business intelligence, enterprises can make informed decisions that help the business run more efficiently. It also creates data benchmarks that are essential fodder for AI platforms, enabling them to work accurately and independently in the future.
Data sharing across the ecosystem will drive innovation
We are still at the initial stages of IoT and smart label adoption, with organisations implementing their own solutions for their specific business or operational needs. This means that within a supply chain scenario, there could be numerous smart labels on despatched items, each reporting data to an individual player (such as a road haulage firm) responsible for the goods for a particular part of their transit.
As IoT and smart label technologies and platforms mature, this siloed data gathering approach will have limitations. Industries would stand to gain more from the implementation of these technologies if data assets could be shared among the ecosystem. In the case of the fruit importer, this would mean having a single smart label gathering a 'single source' of data that all stakeholders could view and from which they could form their own insights. It would eliminate inconsistencies in data gathering, provide a more sustainable solution and help drive innovation through bringing diverse players together.
Of course, data sharing across the ecosystem has its own set of challenges. Stakeholders would need to set up a pool of resources to manage the infrastructure and devices, data security would need to be watertight, and access to this data would need to be well controlled and monitored. Aside from the technical issues, there are cultural challenges to sharing data with other unknown parties and 'letting go' of control that would need to be overcome.
The collective power of new technologies
Already, smart labels and IoT have empowered companies and organisations across supply chains, manufacturing, e-commerce, healthcare, utilities, and transportation to have complete transparency as to the status and condition of their goods and services. They have enabled organisations to make informed decisions and brought about a new level of efficiency and innovation within these sectors.
As the technologies advance and we reach a level of widespread adoption, the number of applications and benefits will continue to increase exponentially. Indeed, it is almost impossible to predict the full impact of IoT and smart label technologies today because their full value will only become truly quantifiable when adoption has fully taken hold. Just like the beginnings of other impactful technologies, such as the mobile phone, new innovative applications will only be developed once data has been fully extracted and elaborated.
What industries need to focus on today is how to prepare for the transition to AI-based platforms to monitor and manage the data deriving from their IoT and smart label devices and how to work within their ecosystems to share the data they are generating. This will help drive innovation and sustainability for the wider prosperity of players within that ecosystem, end users, and the wider community.
