Gartner, a market research firm, has identified four trends that will drive artificial intelligence innovation in the near future. These include: According to the company's announcement, these technologies and approaches will be critical in scaling up artificial intelligence initiatives:
1) Artificial intelligence that is responsible. According to Svetlana Sicular, research vice president at Gartner, increased trust, transparency, fairness, and auditability in artificial intelligence technologies are being demanded by stakeholders. Responsible AI provides a governance framework for meeting those requirements, which includes the following elements: Responsible AI can help achieve fairness even though biases are baked into data; gain trust even though transparency and explainability methods are still in their infancy; and ensure regulatory compliance while grappling with AI's probabilistic nature, according to Sicular.
2) Data that is both small and broad. Gartner asserts that artificial intelligence models based on large amounts of historical data have become less relevant as a result of the sweeping changes that have occurred within organizations as a result of the COVID-19 outbreak. Today, small data — which Gartner defines as "the application of analytical techniques that require less data but still provide useful insights" — and wide data — which Gartner defines as "data that enables the analysis and synergy of a variety of small and large, unstructured and structured data sources" — allow for more robust analytics for decision-making in a variety of industries. The research firm Gartner predicted that by 2025, 70 percent of organizations will be forced to shift their focus from big to small and wide data, providing more context for analytics and reducing the data appetite of artificial intelligence (AI).
3) The operationalization of artificial intelligence platforms. Operationalization, according to Gartner, is the process of taking artificial intelligence projects from the concept stage to the production stage so that AI solutions can be relied upon to solve enterprise-wide problems. The company notes that operationalization is a critical step in leveraging AI for business transformation. The AI expert noted that only half of AI projects make it from the pilot stage into production, and those that do take an average of nine months to complete the process. Technology advancements in artificial intelligence (AI) operationalization are "enabling reuse, scalability, and governance," she continued, "thereby accelerating AI adoption and growth."
4) Efficient utilization of available resources. For AI to be effective, "it is essential that resources be used as efficiently as possible," according to Gartner, because of the complexity and scale of the data, models, and computing power involved in AI deployments. Multiexperience, composite AI, generative AI, and transformers are just a few of the areas that are gaining traction in this field.