At long last, the business adoption of Internet of Things (IoT) is picking up steam and entering mainstream production, initially improving existing processes and operations. Companies across industries are moving away from pilot projects and proofs-of-concept and are beginning to run their businesses on IoT. But, we are not out of the woods just yet. Challenges such as existing business structures, organizational cultures, legacy system integrations, fragmented standards and security are hindering IoT from reaching its full potential.

To flourish, IoT needs a supporting cast of technologies -from analytics to blockchain-as well as an industry-wide shift to open standards, which allow these technologies to interoperate. First, let's take a closer look at the key emerging technologies required for IoT success.

The "secret sauce" of IoT, analytics and artificial intelligence (AI) allow for the real-time processing of data in motion, rather than traditional centralized batch analytics. In many ways, AI is the brain and IoT is the body: IoT is both the source of real-time data for AI applications, and the means of executing AI decisions. Although many IoT AI applications are still in the proof-of-concept stage, AI is already transforming many production applications, serving as the backbone of predictive analytics and predictive maintenance capabilities.

"To flourish, IoT needs a supporting cast of technologies-from analytics to blockchain-as well as an industry-wide shift to open standards, which allow these technologies to interoperate."

Our next technology category is cloud/fog computing. The "Cloud Fog Continuum" is where data analytics does most of its work. Traditionally, batch analytics took place in the cloud, but fog computing extends cloud capabilities in the areas such as networking, computing and storage, and functional safety to the edge of the network, where the data is generated. To conserve bandwidth and ensure real-time data processing, fog nodes sort through mounds of data and send only exceptions back to the cloud for further analysis. In cases where latency is a challenge, fog nodes can send real-time alerts, allowing users to take immediate action. AI systems are moving in this direction as well. Once the logic is set, AI systems can run in specialized fog notes using a field-programmable gate array (FPGA), or even an application-specific integrated circuit (ASIC). Such integrated, policy-based cloud to the edge systems will lower costs and support greater adoption of driverless vehicles and other real-time AI solutions.

IoT security technologies and strategies are also vital to success. Today, all major vendors are investing in IoT security alongside other security domains, and security companies and industry groups are accelerating work on standards, interoperability, certification and security education. Additionally, businesses are moving from "security by obscurity" into comprehensive policy-based security architectures managed by the chief security officer teams. These must be built into every part of IoT operations, focusing on preventing security breaches, identifying hacking attempts and remediating problems.

One specific technology enabling IoT is blockchain, which allows for a secure exchange of value between entities in distributed networks. Blockchain creates a tamper-proof record of transactions, making it ideal for tracing the source of goods throughout production and distribution. Bitcoin is perhaps the most famous application of blockchain technology; but enterprise-grade blockchain offers numerous applications that extend beyond digital currency. For example, energy companies are considering blockchain for managing interactions between solar panels and the power grid, and automakers are looking at the technology to authenticate interactions between connected vehicles and the roadside infrastructure.

Lastly, drones have received a great deal of hype in the commercial sector and are often disparaged for their covert applications and dismissed as high-tech playthings. However, IoT is making businesses turn a closer eye to drones, especially when these devices are combined with AI, blockchain and fog computing. For instance, AI-powered autonomous drones can work longer and more efficiently than piloted drones. They can automatically choose the most direct flight path and change it on the fly to avoid poor weather conditions, trees, power lines and other obstacles. One promising application of IoT and drones involves helping surveyors and map-makers document remote, rugged terrain. Or, utility firms can use them to remotely inspect pipelines and cell towers.

The powerful combination of these technologies and IoT can deliver new business value across the enterprise, but only if they are able to work together. This is why we need open standards to enable interoperability. Otherwise, businesses will have difficulty unlocking substantial benefits -from cost savings to new value propositions. Fortunately, the industry has been evolving rapidly from a collection of overlapping standards, semi-standards, specialized and proprietary technologies into true interoperable standards. Such efforts focus on three standardization drivers:

1. Interest groups in the Institute of Electrical and Electronics Engineers (IEEE), the Internet Engineering Task Force (IETF) and others are working to advance existing horizontal standards to meet IoT requirements. For example, Time Sensitive Networking (TSN) in IEEE is a great example of evolving the Ethernet standard to meet manufacturing motion and safety requirements.
2. Numerous vertical industry groups are rapidly evolving and starting to consolidate while moving specialized or proprietary technologies to open standards. For example, they are standardizing the way of expressing "temperature," "pressure" and other values needed for scalable data collection. These efforts are beginning with controller-specific data and then moving to telemetry and diagnostics.
3. Various consortia are developing frameworks and driving interoperability across their members' implementations. One example is the OpenFog Consortium, which recently released the OpenFog Reference Architecture, which aims to create an open architecture fog computing environment.

So, what does all this mean for the state of IoT today? The good news is that technologies are maturing, solutions are becoming interoperable, and we are seeing more scalable production applications. In addition to improving existing processes, we are also starting to see the use of IoT in creating new value propositions, new industries and even new business models. The bad news is that IoT security adoption by both businesses and vendors, especially consumer-class device manufacturers, is lagging, as is migration to open standards. Both of these factors are deterring deployments and increasing the costs of implementations. If I were to grade IoT at this point, I would give it a B-. There is still work to be done, but we are finally on the right track.

This article first appeared inCIO Review.