AI; small and wide data approaches; operationalization of AI platforms; and efficient use of data, model and compute resources.
These are the four trends on Gartner’s Hype Cycle for Artificial Intelligence, 2021 that the analyst claims are driving near-term artificial intelligence (AI) innovation.
“AI innovation is happening at a rapid pace, with an above-average number of technologies on the Hype Cycle reaching mainstream adoption within two to five years,” said Shubhangi Vashisth, senior principal research analyst at Gartner. “Innovations including edge AI, computer vision, decision intelligence and machine learning are all poised to have a transformational impact on the market in coming years.”
The AI market remains in an evolutionary state, with a high percentage of AI innovations appearing on the upward-sloping Innovation Trigger (see Figure 1). This indicates a market trend of end-users seeking specific technology capabilities that are often beyond the capabilities of current AI tools.
Figure 1: Hype Cycle for Artificial Intelligence, 2021
Here are the four trends that are driving AI innovation, according to Gartner:
Responsible AI
“Increased trust, transparency, fairness and auditability of AI technologies continues to be of growing importance to a wide range of stakeholders,” said Svetlana Sicular, research vice president at Gartner.
She added that Responsible AI helps achieve fairness, even though biases are baked into the data; gain trust, although transparency and explainability methods are evolving; and ensure regulatory compliance, while grappling with AI’s probabilistic nature.
Gartner expects that by 2023, all personnel hired for AI development and training work will have to demonstrate expertise in responsible AI.
Small and wide data
Data forms the foundation of successful AI initiatives. Small and wide data approaches enable more robust analytics and AI, reduce organizations’ dependency on big data, and deliver richer, more complete situational awareness.
Gartner predicts that by 2025, 70% of organizations will be compelled to shift their focus from big to small and wide data, providing more context for analytics and making AI less data-hungry.
“Small data is about the application of analytical techniques that require less data but still offer useful insights, while wide data enables the analysis and synergy of a variety of data sources,” said Sicular.
She believed that together, these approaches enable more robust analytics and help attain a more 360-degree view of business problems.
Operationalization of AI platforms
The urgency and criticality of leveraging AI for business transformation is driving the need for the operationalization of AI platforms. This means moving AI projects from concept to production so that AI solutions can be relied upon to solve enterprise-wide problems.
Gartner research has found that only half of AI projects make it from pilot into production and those that do take an average of nine months to do so.
“Innovations such as AI orchestration and automation platforms (AIOAPs) and model operationalization (ModelOps) are enabling reusability, scalability and governance, accelerating AI adoption and growth,” said Sicular.
Efficient use of resources
Given the complexity and scale of the data, models and computing resources involved in AI deployments, AI innovation requires such resources to be used at maximum efficiency.
Multi-experience, composite AI, generative AI and transformers are gaining visibility in the AI market for their ability to solve a wide range of business problems in a more efficient manner.
