AI, blockchain and IoT convergence improves daily applications
IT pros may find IoT, blockchain and AI difficult to conceptualize as individual technologies, but together, the three can generate data, improve analysis and establish trust.
AI, IoT and blockchain are three of the most notable and complicated technological innovations facing enterprises today. Many organizations are awakening to these concepts, but few understand AI, blockchain and IoT convergence.
Consider organic systems as a metaphor for how these technological systems will interrelate. An organism has a central nervous system; IoT systems use sensors to collect and "feel" information about their environments. An organism's brain processes information and detects patterns; AI plays this role in technological systems. Finally, an organism has memory or some ability to store information; distributed ledger technology (DLT) represents shared and immutable record-keeping in technological systems.
These technologies will influence each other's trajectories, as shown in Figure 1 below.
Blockchain, AI and IoT yet to converge as holy trinity
The scope and architectural designs of any one of these technologies -- never mind all three -- are truly vast and advancing daily. Each suffers from various limitations, such as lack of interoperability, processing speed for high-volume or mission-critical applications, and different standards. Each offers new design paradigms in privacy and security contexts. Each faces significant legal, political, cultural and ethical challenges relative to incumbent models. Not one individually, nor all three, is a silver bullet for capitalizing on data or data-driven economies, but all are subjects of extreme hype and promise. Nonetheless, there are important caveats that help ground an understanding.
Figure 1. IoT data funnels to AI to improve analysis and feed information to run applications. IoT and AI data can become immutable in a blockchain.
Much depends on the problem. Whether in a business, health or societal context, do not blindly pursue these technologies because they sound innovative. Instead, evaluate the criteria, capabilities and constituencies needed to solve an actual problem. Look beyond organizations to capitalize on ecosystem-based technologies.
As connectivity and algorithms infuse every aspect of business and society, the need for accountability, security, access to data and, most importantly, trust is only growing.
Not everything will be registered to a blockchain. It's unlikely any blockchain will be able or need to capture all data emanating from IoT devices or elsewhere. But there are plenty of device transactions or interactions that warrant an immutable record, including when a car passes through a tollbooth or when perishable cargo reaches a distribution center. What are the events that multiple constituencies in the network need to agree on?
IoT is just part of the big data iceberg. Sensor and device data are just a fraction of the myriad data feeds powering AI. What's essential is data quality. When it comes to blockchain, however, there are important limitations to current DLT architectures that make that distinction even more important. For example, billions of sensors in an agricultural environment or 1 million cars in a city might cause issues with the scale or connectivity required for real-time reconciliation on a blockchain.
See the bigger picture of blockchain, AI and IoT convergence
Smart homes, cars and cities depict how an AI, blockchain and IoT convergence might underpin real-world uses.
This infographic illustrates the AI, blockchain and IoT convergence potential for checks and balances in an accelerated world with evermore distributed compute. Blockchain helps make AI and IoT more accountable. Software and hardware intelligence enhance IoT and blockchain applications and process automation. As connectivity and algorithms infuse every aspect of business and society, the need for accountability, security, access to data and, most importantly, trust is only growing.
