Gartner identifies four trends driving near-term artificial intelligence innovation
Four trends identified in Gartner, Inc.'s Artificial Intelligence Hype Cycle for 2021 are propelling near-term artificial intelligence (AI) innovation. Responsible AI, small and large data methods, operationalization of AI platforms, and efficient use of data, model, and computation resources are among these developments.
"AI innovation is accelerating, with an above-average percentage of innovations on the Hype Cycle achieving mainstream adoption within two to five years," said Shubhangi Vashisth, Gartner's senior principal research analyst. "Innovations such as edge AI, computer vision, decision intelligence, and machine learning all have the potential to alter the market in the coming years."
The artificial intelligence market continues to evolve, with a significant proportion of AI inventions appearing on the upward-sloping Innovation Trigger (see Figure 1). This reflects a market trend toward end users needing specialised technological capabilities that are frequently beyond the capability of existing AI technologies.
According to Gartner, the following four trends are driving AI innovation:
Responsible artificial intelligence
"Improving the trustworthiness, transparency, fairness, and auditability of AI technology continues to be a priority for a broad spectrum of stakeholders," said Svetlana Sicular, research vice president at Gartner. "Responsible AI enables us to accomplish justice, despite the fact that data is biassed; build trust, despite the fact that approaches for transparency and explainability are growing; and maintain regulatory compliance, despite AI's probabilistic nature."
Indeed, Gartner anticipates that all professionals engaged for AI research and training will be required to demonstrate expertise in responsible AI by 2023.
Data that is both small and large
Successful AI programmes are built on data. Small and broad data techniques provide more robust analytics and artificial intelligence, minimise an organization's reliance on large data, and provide richer, more full situational awareness.
Gartner predicts that by 2025, 70% of enterprises will be obliged to move their focus from big to small and wide data, giving greater context for analytics and reducing AI's data appetite.
"Small data analytics is about implementing approaches that use less data but still provide relevant insights, whereas wide data analytics permits the analysis and synergy of a diverse set of data sources," Sicular explained. "By combining these methodologies, we can achieve more robust analytics and a more holistic picture of business problems."
Operationalization of Artificial Intelligence Platforms
The criticality and urgency of adopting AI for business transformation is pushing the demand for AI platforms to be operationalized. This entails transitioning AI initiatives from concept to production, ensuring that AI solutions are capable of resolving enterprise-wide problems.
"According to Gartner research, barely half of AI initiatives progress from pilot to production, and those that do take an average of nine months," Sicular explained. "Innovations like as artificial intelligence orchestration and automation platforms (AIOAPs) and model operationalization (ModelOps) provide reusability, scalability, and governance, hence boosting AI adoption and growth."
Utilization of Resources Efficiently
Given the complexity and scale of the data, models, and computational resources utilised in AI deployments, innovation in AI demands the most efficient use of these resources. Multiexperience, composite AI, generative AI, and transformers are gaining traction in the AI sector due to their potential to more efficiently tackle a wide variety of business challenges.
More information is available to Gartner clients in the research "Hype Cycle for Artificial Intelligence, 2021." This data is included in Gartner's special report, "2021 Hype Cycles: Delivering Innovation Through Trust, Growth, and Change." Gartner's Hype Cycles for 2021 assist organisations in making innovation a core skill and in shaping and prioritising their innovation delivery methodology.
Learn more about top strategic trends, including artificial intelligence, in Gartner's complimentary webinar, "The Gartner 2021 Top Strategic Technology Trends."