Integrating artificial intelligence into cloud platforms for next-generation business intelligence solutions

Artificial intelligence (AI) is a paradigm-shifting area of computer science. AI has delivered technological breakthroughs across applications, devices, and sectors. Some recent implementations of AI are, for example, the ChatGPT chatbot service and a no-code service. These implementations extend the availability of AI algorithms to millions of non-expert users. AI in cloud platforms can now be thought of as providing advanced business intelligence (BI) solutions that address self-service BI data processing in virtually all businesses. Cloud-based BI solutions complement on-premise solutions as they offer better access, less maintenance, and increased scalability. Their hassle-free use for users across companies favors the adoption of still more cloud-based AI solutions. Examples of the success of cloud BI solutions are various companies (Hellerstein; Kelleher, 2019; Stonebraker, 2005 & Ghosh, 2021).

Increasing access to reliable, high-speed cloud data and server hosting has made it possible for small- and medium-sized enterprises to efficiently share BI analytics with little or no in-house expertise. Pure-play AI algorithms that are adroitly adapted to data processing in the cloud can then go a step further by proposing alternate data analyses and/or improved data representations. Although non-experts can thus collaborate in determining the appropriate path to data insights, high-level AI-driven tools do not address the still-existing gap in expertise and trust in BI results. These include sensitivity assessments, analysis model selection, and result trustworthiness diagnostics. Building AI into a cloud BI platform to address sensitivity and trust issues in automated cloud BI enables wider adoption and usage by organizations to accelerate getting actionable information from growing amounts of data of diverse complexity and rapidity.