IBM has been proving to be one of the most important players in the global trend of AI and they have surely made a significant impact of the Internet of Things or IoT sector when it comes to enterprises. These two are the keywords to understand the shifting nature of business decisions and impacts. Such technologies are becoming crucial, if not imperative, to a business’s success. However, the secret lies in the right application and correct deployment of these technologies so that there is a significant impact on the business.
The right ecosystem for AI
Of course, environment matters and AI is no different in that case. Where you place AI in the operational hierarchies is going to determine how you play with it and what you do with it. Moreover, IBM can never dedicate itself to the cause of IoT in totality and hence, has to look for partners. Partners such as Tesco or Airbus help in this process and ensure that customer relationship is maintained on solid grounds.
Since security is a major headache, IBM has collaborated with Visa so that payment experiences across all IoT devices are secure and safe. This is a classic example of how IBM builds its own ecosystem so that AI can function seamlessly.
Value chains will be transformed
Value chains are no longer going to be the same, or even same concept when IoT and AI arrive at full force. The chains are broken to segregate them in phases of design and testing. Then, such phases are run in parallel introducing feedback loops in them. In fact, the value chain has become shorter and faster because of the removal of some redundant phases so that market can be accessed and gauged easily to propel innovations further.
A networked process
IBM has also stressed on the fact that the success to this technology lies in the quality of network. Since it is not a centralized idea, diffusion is key and secure, precise networks become fundamentally important for that purpose. Edge computing with multi-access features, for example, creates a communicable mode between central databases and points at the fringe. Hence, enterprises should focus on creating a solid infrastructure underlying the huge network to support it.
Changing AI and IoT from core to surface
Of course, the most difficult challenge in this process is to pose a balance between the core of technology and the surface of interaction. On one hand, there is a challenge to make sure these technologies are sensed in the process of customer experience and business outputs. On the other, you also need to root your technologies deep into the business processes to extract more data and make unprecedented moves through predictive analytics.
Such a twofold challenge is never easy to deal with, especially with so much tension. However, once you define the metrics, even the minuscule ones, you need not worry about AI since the ROI will display the changes AI has made and you can take decisions regarding its further involvement in business. For this purpose, IoT solutions need to enter deep into manufacturing process and determine the metrics and KPIs that would have a significant impact on decision and outcome. Hence, if necessary, material science must be involved in this process to make sure supply chains are understood in depth.