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Accelerating AI deployments in the business setting

Source: Digital Journal

According to a new report, businesses need to give serious consideration to putting AI on a rapid path to production. Data and analytics leaders should use this research collection to remove the most common barriers to production.

The report comes from Gartner and it is titled "Accelerating AI Deployments — Paths of Least Resistance." As well as overcoming barriers, the report places considerable emphasis upon strategies for accelerating from artificial intelligence (AI) prototypes to introducing scalable solutions.

The report was compiled by Melissa Davis, and the text draws out several opportunities and challenges that businesses need to grapple with in order to utilize the benefits that artificial intelligence has to offer, such as automating customer interactions or processing and interpreting large volumes of complex data.

Proof of concept

The report finds that enterprises continue to struggle to move from artificial intelligence proofs of concept to scalable implementations that realize business value. As things stand, around half of businesses are achieving success according to recent studies. According to Forbes, to achieve scale value in the AI era, the key is to think big and start small: prioritize advanced analytics, governance, ethics, and talent from jump. In considering AI in general, the Gartner report finds that to increase their chances of successfully deploying AI into production, firms should undertake measures to assess the business value and outcomes from AI initiatives across the enterprise. This includes ensuring that AI is aligned with investments with strategic priorities.

Talent management

Another barrier is with employing people with the right skills, as the report indicates. However, this does not need to specifically in the fields of machine learning and deep learning. The report finds that Acquiring AI talent is not a major barrier to success. This is further evidenced in Gartner's 2020 AI in Organizations Survey. Instead, data suggests that organizations that deploy a diversity of talent are the most successful in bringing their proofs of concept to production deployment. What firms should be doing is building competencies to integrate AI solutions within the organizations. This is especially to address the complexity of embedding and integrating AI with other applications and the infrastructure. The report advises that firms involve middleware specialists, application integration specialists, and other IT teams such as DevOps.

Data privacy and security

Among the main barriers to success, the report finds that integration complexity, security and privacy concerns are among the most significant barriers to production deployment. In relation to this area, the report emphasizes that dealing with the security challenges generated by artificial intelligence will become critical for a range of businesses. To this end, enterprise architecture and technology innovation leaders need to examine how they will protect their AI-powered systems and defend them against the malicious use of AI and machine learning by attackers.

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