What if IoT were not treated as a set of elements for individual tasks?
Let’s envision an orchestrated workforce that takes care of the core business process's value chain. The IoT devices will empower the AI Engine with data for every small process and system that might lead to an essential data discovery.
IDC predicts that by 2020, global IoT spending will reach $1.29 trillion, a compound annual growth rate of 15.6 percent.
Example: A European aircraft manufacturer utilizing IoT technologies in manufacturing machinery as well as in the tools the workers use. With these tools, the workers assess a task and relay the information or instruction to the machines to perform the needed tasks. [C1]
It's an opportune time for building or buying IoT-ready platforms with IoT devices as a bundle that is easily integratable with your existing systems. Businesses need ready-made IoT platforms with IoT devices, rather than building an IoT platform for an IoT device on their own.
Data is the fuel to this engine, which will drive your business
The Artificial Intelligence Engine (AI Engine) automates IoT, utilizes the analytical insights to automate, and recommends appropriate solutions at the snap of a finger. This is what Digital Business aims to achieve. Business strategies can be mapped with AI recommendations in every nook of an elaborate business core process and the value chain that supports it; be it production, workforce management, sales and marketing, or logistics. This will enable local quick-thinking sales and production managers to run the business more efficiently. Not to mention that while streamlining processes, the engine will eventually learn to self-calibrate systems and processes.
Gartner predicts that by 2020, customers will manage 85% of
their relationship with the enterprise without interacting with a human.
Example: A global adhesive manufacturing customer retrieves data from their lab system, where the raw materials of the adhesive systems are tested. Using AI and Machine Learning (ML), the retrieved data is processed to automatically assess the final product with recommendations: which material to apply when, in which amount, throughout production. This calibrates the manufacturing of the adhesives. [C2]
Augmented data discovery in a more localized form can positively affect the bigger system. A ground-level context for data-driven insights is imperative to increase the sense of connectivity with the bigger business environment; i.e. changes in consumer behavior and production environment.The AI Engine gradually teaches itself to recognize external and internal factors that impact the operation of machines and the consumer environment. These models can be replicated in healthcare, finance, and IT service provider business models, among others.
Robotic Process Automation (RPA) is turning into Intelligent Automation
Intelligent Automations (IA) are being pre-trained to do natural language recognition and processing, deal with unstructured super-data sets, and automate specific business processes. They can make relevant connections and will continue to learn unsupervised, constantly adjusting to the new information they are being fed and thus improve their performance. This way, while performing repetitive tasks, IAs can improvise when needed.
Forrester has projected that the RPA market will reach $1.70 billion in revenue in 2019.
Example: A leading UK insurer engaged an IA to categorize emails. It received emails with claim information (such as attachments of legal documents in various file formats), and understood the context of each one, categorizing accordingly before uploading it in the business's system. If it happened to misunderstand a document, it reached out to the human team responsible for assistance. In turn, it learned from the team's processing of that document, so that it could do the same the next time the same case happened. [C3]
To make automation more intelligent, the automation learns from the team's actions in situations it didn't understand on its own. That way the system learns to make better decisions on its upcoming tasks.
A Quantum computer has the ability to be in multiple states and perform tasks using all possible permutations simultaneously
On the grounds of such features—and by the end of 2025—it is expected that there will be more than USD $23 billion in revenue generated through the adoption of quantum computing across the globe.
During this forecasted decade, the global market for quantum computing is expected to expand exponentially: a whopping 30.9% compound annual growth rate, as per Persistence Market Research.
Example: A German automaker quantum computed an optimized traffic flow for Chinese taxicabs in Beijing, using freely-available GPS data of taxis actively on duty. It took the machine mere seconds to process data and come up with nearly traffic-free routes. [C4]
The most essential medium, connecting all of these elements, that ensures a transfer of data that is as fast as the computing capabilities
5G will ensure the seamless and speedy connectivity and transfer of data for M2M communication (IoT Grid and analytical/AI platform). Not only that, but it also provides scaling possibilities in the mobile network.
According to a report from MarketsandMarkets, the 5G
infrastructure market will be worth $2.86 billion by 2020 and $33.72 billion
by 2026, growing at a compound annual growth rate of 50.9%.
Example: Self-driving cars rely on 5G networks in order to access the speedy data connectivity necessary for smooth and error-free performance.
5G’s extremely high transmission rates enable all infrastructure and storage to be shifted to the cloud.
With a surge in collected data and to power up AI and ML processes, cloud computing is the way to go for organizations to digitize their business completely
It’s not enough to think about a digital journey for end users: companies need to rethink all their corporate processes to be ready for this new era. Organizations already leveraging Cloud Technologies to transform their internal IT departments are simultaneously building a business-ready IT, capable of streamlining the development lifecycle and reduce time to market. This also allows the transformation of their organizational culture, by disbanding silos. Enterprises looking to bring digital transformation into their internal applications, without replacing them, will refactor their core applications using cloud-native technologies, like containers. Others will be bolder and seek core SaaS-based multi-cloud technologies with new developing tools, integration, and deployment options.
According to Forrester, nearly 60% of North American enterprises today rely on public cloud platforms.
Example: A railroad system supplier partnered with a renowned cloud technology services company to centralize their data from multiple sources, stream it uninterrupted, and direct data flow to different systems as required. Together they developed a cloud-based application with the following components: an application gathering and normalizing data to a specific data model format, an analytics application displaying real-time information, and RESTful APIs that integrate business systems with the cloud application. [C5]
Cloud Technologies enable rapid product and service innovation, as well as market readiness, through faster deployment. With quick, flexible scaling and integrating capabilities of fragmented systems, Cloud Technologies provide the foundation for delivering successful customer experiences and business efficiency.