AI adoption in enterprises can be increased, if enterprises see more opportunities in the technology, discovering new and more profitable revenue models, talent consolidation and innovations like standardizing platforms
CIOs feel that AI centers can work as a motivating factor for increasing interest in AI adoption by organizations. The early adopter companies to do so didn’t even have names specifically as AI-based centers; they were referred to as analytics CoE, predictive analytics CoE, etc.
Traditionally it used to be a single person department; the practice has slowly changed to the departments blowing up to be full-fledged research centers with full-time employed engineers and scientists.
CIOs believe that once an AI CoE is established with common platforms and in-house experts, the next course of action is to share the best practices throughout the organization. AI tech is not organization boundaries specific. AI CoEs help understand which kind of deep AI expertise is required for which particular issue.
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The CoE researchers test the proposed solution to ensure it works, bring together the best skills, and then deploy it across different levels of the organization. AI-based image recognition helps speed up inspections and helps employees or experts invest their time into a more pressing issue.
AI tech is right for proactive detection of liability before it turns into a problem, which would cost significantly higher for the organization.
Enterprises require a centralized approach for achieving the benefits of AI on a larger scale. Experienced CIOs say that enterprises that adopt AI are more likely to deploy the centralized system to vendor selection and AI tech.
They point out that without a CoE, the organization will have bespoke investments from different business lines, employees reaching out to IT from different levels, and less than satisfactory investment.
They think that efficient AI CoEs will help organizations to transform from one project proofs of concept and prototypes to implementing AI at a big scale.
Many companies have built innovation labs to help boost their AI journey. It helps them identify and eliminate the redundant jobs that required frequent manual intervention. Digitizing processes helps reduce time and increase business efficiency considerably.
AI tech helps collect data and provides the organization with a complete digital footprint related to its operations. Such data plays a critical part in optimizing maintenance, reducing the organization’s carbon footprint, and forecasting the power needs.
AI tech also helps enterprises configure their systems for easy integrations with third-party platforms. It makes rolling out digital experience easier on a broader portfolio. All these advantages are not limited only within the organization; they have helped benefit the clients.
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Clients have reported a drastic reduction in costs, a dynamic environment, decreased power costs, proactive issue resolution, and reduced sustainability footprint.
By deploying an AI CoE and implementing best practices across the enterprise, companies are better positioned to detect new data from AI projects and pilots that can help transform the entire organizational operations.
Megana Natarajan is a Global News Correspondent with OnDOt Media. She has experience in content creation and has previously created content for agriculture, travel, fashion, energy and markets. She has 3.9 years’ experience as a SAP consultant and is an Engineering graduate.
