More and more CIOs are resorting to machine vision as a much-required automation process to reduce the complexity of tasks. What is common concerning the newfound interest in machine vision and AI as a whole is the underlining need to have proper planning, design, and governance for effective deployment of robotics-powered technology.
FREMONT, CA: Artificial intelligence (AI) has ushered in a new wave of revolutionary technology. The process enables an unparalleled change in the way enterprises function in a specific manner. The current CIOs stand at a crucial junction wherein technology, and innovation are combined. The CIOs in the AI sector are at a point wherein the benefits offered by the emerging technology are numerous and can be leveraged easily.
First, let’s explore the basics of how AI is beneficial to the IT community. The IT industry is reeling under pressure to meet the high expectations of consumers and employees as well. The situation has created a need for having advanced analytical tools along with business intelligence systems for sophisticated information management. The industry is witnessing an emergence of them as well. CIOs across industries have to shoulder a huge responsibility in an enterprise’s attempt towards dealing with enhanced expectations. CIOs are increasingly forced to function in a direct response to consumer demands and are under constant pressure to optimize the business and generate more revenue. Many CIOs are looking forward to AI as the ideal tool to enhance operational efficiency across sectors such as social media analysis, data mining, warehousing of big data or customer analytics. CIOs continue to leverage robotics automation technologies to eliminate the requirement for relying on human-powered manual process.
Almost all businesses are trying to derive effective strategies for successfully integrating machine learning and other AI applications. This scenario positions CIOs at a critical juncture to lead the enterprise through the AI implementation journey. CIOs need to understand the entrepreneurial goals through the implementation of machine learning.
Why Machine Vision can be significant for CIOs?
Contemporary CIOs have to decide how to effectively implement AI technologies, rather than deciding on whether to use or not use. With more and more enterprises resorting to AI such as machine vision, CIOs should be able to derive strategies to incorporate AI-powered technologies in the best possible manner. As a result, the role of CIOs should be changed to an advisory role, which can help the entire organization, rather than a few departments. In other words, as a priority, CIOs need to ensure that they have clearly defined, identified, and understood business scenarios and challenges to be tackled or improved through the incorporation of machine vision.
In his changed role, the CIO needs to be supervising the adoption and execution process of AI-based machine vision all through the firm in a multitude of use cases to derive better insights into the business landscape. Also, it is critical for CIOs to make sure that the highly robust tools being utilized as a part of machine vision are not confined to the leadership or a selected few.
If an enterprise is focusing on incorporating machine vision into their digital transformation process, the primary goal for a CIO will be finding a smarter and faster way to convert data into action. CIOs need to prioritize and adhere to those priorities while incorporating this technology. The priorities for a CIO might have to be centered on laying the foundation for an agile development methodology for machine vision and generating success in these areas besides enabling consistency across several departments.
For instance, let’s consider an enterprise supplying diagnostic medicines trying to incorporate machine vision into its operations for effectively managing demand planning procedures. For a CIO working in such an enterprise, the real challenge is to identify innovative methodologies to incorporate technology into the diagnostic medicine process.
As part of machine vision incorporation, several CIOs have cited the availability of quality data as the biggest obstacle in adapting machine learning initiatives. This happens because data fails to be constantly organized in such a way that it becomes incompatible with the ML algorithms. So, CIOs need to be technically evolved and enhance their technical knowledge to deal with critical machine vision implementation.