The AI Tech-Stack Model

Presently, enterprises have implemented advanced artificial intelligence (AI) technologies to support business process automation (BPA), provide valuable data insights, and facilitate employee and customer engagement.7 However, developing and deploying new AI-enabled applications poses some management and technology challenges.3,5,12,15 Management challenges include identifying appropriate business use cases for AI-enabled applications, lack of expertise in applying advanced AI technologies, and insufficient funding. Concerning technology challenges, organizations continuously encounter obsolete, incumbent information technology (IT)/information systems (IS) facilities; difficulty and complexity integrating new AI projects into existing IT/IS processes; immature and underdeveloped AI infrastructure; inadequate data quantity and poor-quality learning requirements; growing security problems/threats; and inefficient data preprocessing assistance. Furthermore, major cloud service vendors (for example, Amazon, Google, and Microsoft) and third-party vendors (for instance, Salesforce and Sense-Time) have stepped up efforts as major players in the AI-as-a-service (AIaaS) race by integrating cloud services with AI core components (for example, enormous amounts of data, advanced learning algorithms, and powerful computing hardware).4 Although AIaaS offerings allow companies to leverage AI power without investing massive resources from scratch,8 numerous issues have emerged to hinder the development of desired AI systems. For example, current AI offerings are recognized as a fully bundled package, offering less interoperability between different vendors and causing vendor lock-in and proprietary concerns.